In this research to assess emotions from biometric signals, participants are asked to evaluate the emotions they subjectively experienced in order to confirm whether the assumed emotions were actually elicited. However, the evaluation of emotions is not routinely performed in daily life, and it is possible that this evaluation may alter biological signals. In fMRI studies, evaluation has been shown to activate the amygdala, which is said to be related to emotional expression. However, electroencephalography (EEG) studies do not take into consideration the effects of such evaluations, and it is unclear how these evaluations affect emotion-related brain activity observed in EEG. We hypothesized that emotion evaluations would amplify emotions and c alter Frontal Alpha Asymmetry (FAA), which has been shown to be related to emotional pleasantness and unpleasantness. We suspect this is because in order to evaluate one's emotions, one must pay attention to one's internal state, and this self-focused attention has been found to enhance the subjective emotional experience. We measured a 29-channel EEG when presented with unpleasant and highly arousing images from the International Affective Picture System (IAPS) from 40 healthy male and female participants. The results revealed that FAA was significantly lower in the condition in which participants rated their own emotions compared to the condition in which they did not. Similar to fMRI studies, this result indicates that emotion-related brain activity is amplified on an EEG. This paper provides a cautionary note regarding the use of such evaluations in EEG emotion estimation studies.

Abstract

This systematic review and meta-analysis examined the NFT effects on attentional performance in healthy adults. Six databases were searched until June 2022 to identify parallel randomized controlled trials (RCTs) evaluating attentional improvements after NFT. Risk of bias was assessed using the Cochrane Collaboration tool. We identified 41 RCTs for qualitative synthesis and 15 RCTs (569 participants) for meta-analysis. The overall NFT effect on attentional performance was significant (standardized mean difference = 0.27, 95% confidence interval = 0.10–0.44). However, no significant pooled effect was found within the trials comparing its effect with sham NFT (8 RCTs). Additionally, variable effects were observed on individual subsets of attentional performance. Further sham-controlled RCTs are required to validate the improvement of attentional performance with NFT.

This research investigated the difference in aspects of gaze control between esports experts (Expert) and players with lower skills (Low Skill) while playing the real-time strategy game called StarCraft. Three versions of this game at different difficulty levels were made with the StarCraft Editor, and the gaze movements of seven Expert and nine Low Skill players were analyzed while they played the games. The gaze of Expert players covered a significantly larger area in the horizontal direction than the gaze of Low Skill players. Furthermore, the magnitude and number of saccadic eye movements were greater, and saccade velocity was faster in the Expert than in the Low Skill players. In conclusion, StarCraft experts have a specific gaze control ability that enables them to quickly and widely take visual information from all over the monitor. This could be one of the factors enabling StarCraft experts to perform better than players with lower skills when playing games that require task-switching ability.

Objective

This study aimed to develop the Motivation in stroke patients for rehabilitation scale (MORE scale), following the Consensus-based standards for the selection of health measurement instruments (COSMIN).

Method

Study participants included rehabilitation professionals working at the convalescent rehabilitation hospital and stroke patients admitted to the hospital. The original MORE scale was developed from an item pool, which was created through discussions of nine rehabilitation professionals. After the content validity of the scale was verified using the Delphi method with 61 rehabilitation professionals and 22 stroke patients, the scale’s validity and reliability were examined for 201 stroke patients. The construct validity of the scale was investigated using exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and item response theory analysis. Cronbach’s alpha confirmed its internal consistency. Regarding convergent, discriminant, and criterion validity, Spearman’s rho was calculated between the MORE scale and the Apathy Scale (AS), Self-rating Depression Scale (SDS), and Visual Analogue Scale (VAS), which rates the subjective feelings of motivation.

Results

Using the Delphi method, 17 items were incorporated into the MORE scale. According to EFA and CFA, a one-factor model was suggested. All MORE scale items demonstrated satisfactory item response, with item slopes ranging from 0.811 to 2.142, and item difficulty parameters ranging from -3.203 to 0.522. Cronbach’s alpha was 0.948. Regarding test-retest reliability, a moderate correlation was found between scores at the beginning and one month after hospitalization (rho = 0.612. p < 0.001). The MORE scale showed significant correlation with AS (rho = -0.536, p < 0.001), SDS (rho = -0.347, p < 0.001), and VAS (rho = 0.536, p < 0.001), confirming the convergent, discriminant, and criterion validity, respectively.

Conclusions

The MORE scale was verified as a valid and reliable scale for evaluating stroke patients’ motivation for rehabilitation.

Abstract

Inter-brain synchronization is enhanced when individuals perform rhythmic interpersonal coordination tasks, such as playing instruments in music ensembles. Experimentally, synchronization has been shown to correlate with the performance of joint tapping tasks. However, it is unclear whether inter-brain synchronization is related to the stability of interpersonal coordination represented as the standard deviation of relative phase (SDRP). In this study, we simultaneously recorded electroencephalograms of two paired individuals during anti-phase tapping in three interactive tapping conditions: slow (reference inter-tap interval [ITI]: 0.5 s), fast (reference ITI: 0.25 s), and free (preferred ITI), and pseudo tapping where each participant tapped according to the metronome sounds without interaction. We calculated the inter-brain synchronization between pairs of six regions of interest (ROI): frontal, central, left/right temporal, parietal, and occipital regions. During the fast tapping, the inter-brain synchronization significantly increased in multiple ROI pairs including temporoparietal junction in comparison to pseudo tapping. Synchronization between the central and left-temporal regions was positively correlated with SDRP in the theta in the fast condition. These results demonstrate that inter-brain synchronization occurs when task requirements are high and increases with the instability of the coordination.

As humans, we constantly change our movement strategies to adapt to changes in physical functions and the external environment. We have to walk very slowly in situations with a high risk of falling, such as walking on slippery ice, carrying an overflowing cup of water, or muscle weakness owing to aging or motor deficit. However, previous studies have shown that a normal gait pattern at low speeds results in reduced efficiency and stability in comparison with those at a normal speed. Another possible strategy is to change the gait pattern from normal to step-to gait, in which the other foot is aligned with the first swing foot. However, the efficiency and stability of the step-to gait pattern at low speeds have not been investigated yet. Therefore, in this study, we compared the efficiency and stability of the normal and step-to gait patterns at intermediate, low, and very low speeds. Eleven healthy participants were asked to walk with a normal gait and step-to gait on a treadmill at five different speeds (i.e., 10, 20, 30, 40, and 60 m/min), ranging from very low to normal walking speed. The efficiency parameters (percent recovery and walk ratio) and stability parameters (center of mass lateral displacement) were analyzed from the motion capture data and then compared for the two gait patterns. The results suggested that step-to gait had a more efficient gait pattern at very low speeds of 10–30 m/min, with a larger percent recovery, and was more stable at 10–60 m/min in comparison with a normal gait. However, the efficiency of the normal gait was better than that of the step-to gait pattern at 60 m/min. Therefore, step-to gait is effective in improving gait efficiency and stability when faced with situations that force us to walk slowly or hinder quick walking because of muscle weakness owing to aging or motor deficit along with a high risk of falling.

Several functional magnetic resonance imaging (fMRI) studies have demonstrated that resting-state brain activity consists of multiple components, each corresponding to the spatial pattern of brain activity induced by performing a task. Especially in a movement task, such components have been shown to correspond to the brain activity pattern of the relevant anatomical region, meaning that the voxels of pattern that are cooperatively activated while using a body part (e.g., foot, hand, and tongue) also behave cooperatively in the resting state. However, it is unclear whether the components involved in resting-state brain activity correspond to those induced by the movement of discrete body parts. To address this issue, in the present study, we focused on wrist and finger movements in the hand, and a cross-decoding technique trained to discriminate between the multi-voxel patterns induced by wrist and finger movement was applied to the resting-state fMRI. We found that the multi-voxel pattern in resting-state brain activity corresponds to either wrist or finger movements in the motor-related areas of each hemisphere of the cerebrum and cerebellum. These results suggest that resting-state brain activity in the motor-related areas consists of the components corresponding to the elementary movements of individual body parts. Therefore, the resting-state brain activity possibly has a finer structure than considered previously.

<bold>Background:</bold> Motivation is essential for patients with subacute stroke undergoing intensive rehabilitation. Although it is known that motivation induces behavioral changes toward rehabilitation, detailed description has been lacking. Motivation can be intrinsic or extrinsic; however, it is unclear which type of factors mainly motivates patients' daily rehabilitation.

<bold>Purpose:</bold> This study aimed to examine the factors influencing patients' motivation and to explore the behavioral changes induced by motivation, especially age-related differences.

<bold>Method:</bold> Twenty participants (mean age 65.8 years [standard deviation 13.7]) who had a subacute stroke and underwent rehabilitation at a convalescent hospital were recruited using convenience sampling. Semi-structured interviews were conducted by an occupational therapist with an interview topic guide regarding factors influencing motivation and how it affects behavioral change. Interviews were recorded, transcribed to text, and analyzed by three occupational therapists using thematic analysis. The participants were divided into two groups: aged patients (aged ≥ 65 years) and middle-aged patients (aged &amp;lt; 65 years), and data were analyzed according to the groups. This study was conducted according to the consolidated criteria for reporting qualitative research.

<bold>Results:</bold> Seven core categories were identified as factors influencing patients' motivation: patients' goals, experiences of success and failure, physical condition and cognitive function, resilience, influence of rehabilitation professionals, relationships between patients, and patients' supporters. The first four and last three core categories were further classified as personal and social-relationship factors, respectively. The categories related to intrinsic motivation such as enjoyment of rehabilitation itself were not derived. In both age-groups, motivation affected the frequency of self-training and activity in daily lives. In some aged patients, however, high motivation restrained their self-training to conserve their physical strength for rehabilitation by professionals. Some aged patients do not express their high motivation through their facial expressions and conversations compared to middle-aged patients; therefore, motivation is not always observable in aged patients.

<bold>Conclusions:</bold> Interventions tailored to extrinsic factors are important for maintaining patients' motivation. Observational evaluation may lead to mislabeling of their motivation, especially for aged patients. Rehabilitation professionals should use validated evaluation scales or patients' narratives to assess patients' motivation.

Musician's dystonia is a type of focal task-specific dystonia (FTSD) characterized by abnormal muscle hypercontraction and loss of fine motor control specifically during instrument playing. Although the neuropathophysiology of musician's dystonia remains unclear, it has been suggested that maladaptive functional abnormalities in subcortical and cortical regions may be involved. Here, we hypothesized that aberrant effective connectivity between the cerebellum (subcortical) and motor/somatosensory cortex may underlie the neuropathophysiology of musician's dystonia. Using functional magnetic resonance imaging, we measured the brain activity of 30 pianists with or without FTSD as they played a magnetic resonance imaging-compatible piano-like keyboard, which elicited dystonic symptoms in many but not all pianists with FTSD. Pianists with FTSD showed greater activation of the right cerebellum during the task than healthy pianists. Furthermore, patients who reported dystonic symptoms during the task demonstrated greater cerebellar activation than those who did not, establishing a link between cerebellar activity and overt dystonic symptoms. Using multivoxel pattern analysis, moreover, we found that dystonic and healthy pianists differed in the task-related effective connectivity between the right cerebellum and left premotor/somatosensory cortex. The present study indicates that abnormal cerebellar activity and cerebello-cortical connectivity may underlie the pathophysiology of FTSD in musicians.

<title>Abstract</title>Inter-brain synchronization is enhanced when individuals perform rhythmic interpersonal coordination tasks, such as playing instruments in music ensembles. Experimentally, synchronization has been shown to correlate with the performance of joint tapping tasks. However, it is unclear whether inter-brain synchronization is related to the stability of interpersonal coordination represented as the standard deviation of relative phase (SDRP). In this study, we simultaneously recorded electroencephalograms of two paired individuals during anti-phase tapping in three speed conditions: slow (reference inter-tap interval [ITI]: 0.5 s), fast (reference ITI: 0.25 s), and free (preferred ITI). We calculated the inter-brain synchronization within six regions of interest: frontal, central, left/right temporal, parietal, and occipital regions. We found that synchronization of the central-temporal regions was positively correlated with SDRP in the theta and alpha bands, while synchronization of the frontal-frontal and frontal-central was positively correlated with SDRP in the beta band. These results demonstrate that inter-brain synchronization occurs only when task requirements are high, and that it increases with the instability of the coordination. This may be explained by the stronger mutual prediction required in unstable coordination than that in stable coordination, which increases inter-brain synchronization.

Humans have the amazing ability to learn the dynamics of the body and environment to develop motor skills. Traditional motor studies using arm reaching paradigms have viewed this ability as the process of ‘internal model adaptation’. However, the behaviors have not been fully explored in the case when reaches fail to attain the intended target. Here we examined human reaching under two force fields types; one that induces failures (i.e., target errors), and the other that does not. Our results show the presence of a distinct failure-driven adaptation process that enables quick task success after failures, and before completion of internal model adaptation, but that can result in persistent changes to the undisturbed trajectory. These behaviors can be explained by considering a hierarchical interaction between internal model adaptation and the failure-driven adaptation of reach direction. Our findings suggest that movement failure is negotiated using hierarchical motor adaptations by humans.

<title>Abstract</title>Hand choices—deciding which hand to use to reach for targets—represent continuous, daily, unconscious decisions. The posterior parietal cortex (PPC) contralateral to the selected hand is activated during a hand-choice task, and disruption of left PPC activity with a single-pulse transcranial magnetic stimulation prior to the execution of the motion suppresses the choice to use the right hand but not vice versa. These findings imply the involvement of either bilateral or left PPC in hand choice. To determine whether the effects of PPC’s activity are essential and/or symmetrical in hand choice, we increased or decreased PPC excitability in 16 healthy participants using transcranial direct current stimulation (tDCS; 10 min, 2 mA, 5 × 7 cm) and examined its online and residual effects on hand-choice probability and reaction time. After the right PPC was stimulated with an anode and the left PPC with a cathode, the probability of left-hand choice significantly increased and reaction time significantly decreased. However, no significant changes were observed with the stimulation of the right PPC with a cathode and the left PPC with an anode. These findings, thus, reveal the asymmetry of PPC-mediated regulation in hand choice.

脳卒中患者におけるリハビリテーション(以下、リハ)のモチベーションに関する知見をシステマティックレビューを用いて整理した。PubMed、CENTRAL、医中誌Webのデータベースから1,930文献が検索され、適格基準・除外基準を満たした13論文が抽出された。脳卒中患者のモチベーション評価には、リハのモチベーションに特化していない尺度や医療者による観察評価が用いられていた。モチベーションとリハの相互作用については、モチベーションに影響を与える要因、モチベーションが機能や活動に及ぼす影響について報告されていたが、報告の質・量共に不十分であった。今後は脳卒中患者のリハに対するモチベーションの概念形成や評価尺度開発が必要である。(著者抄録)

Persistent goal-directed behaviours result in achievements in many fields. However, the underlying neural mechanisms of persistence and the methods that enhance the neuroplasticity underlying persistence, remain unclear. We here demonstrate that the structural properties of the frontal pole cortex (FPC) before tasks contain information that can classify Achievers and Non-achievers (goal-directed persistence) participating in three tasks that differ in time scale (hours to months) and task domains (cognitive, language, and motor learning). We also found that most Achievers exhibit experience-dependent neuroplastic changes in the FPC after completing language and motor learning tasks. Moreover, we confirmed that a coaching strategy that used subgoals modified goal-directed persistence and increased the likelihood of becoming an Achiever. Notably, we discovered that neuroplastic changes in the FPC were facilitated by the subgoal strategy, suggesting that goal-striving, using effective coaching, optimizes the FPC for goal persistence.

脳卒中患者におけるリハビリテーション(以下、リハ)のモチベーションに関する知見をシステマティックレビューを用いて整理した。PubMed、CENTRAL、医中誌Webのデータベースから1,930文献が検索され、適格基準・除外基準を満たした13論文が抽出された。脳卒中患者のモチベーション評価には、リハのモチベーションに特化していない尺度や医療者による観察評価が用いられていた。モチベーションとリハの相互作用については、モチベーションに影響を与える要因、モチベーションが機能や活動に及ぼす影響について報告されていたが、報告の質・量共に不十分であった。今後は脳卒中患者のリハに対するモチベーションの概念形成や評価尺度開発が必要である。(著者抄録)

Teachers often believe that they take into account learners’ ongoing learning progress in their teaching. Can behavioural data support this belief? To address this question, we investigated the interactive behavioural coordination between teachers and learners during imitation learning to solve a puzzle. The teacher manually demonstrated the puzzle solution to a learner who immediately imitated and learned it. Manual movements of teachers and learners were analysed using a bivariate autoregressive model. To identify bidirectional information exchange and information shared between the two agents, we calculated causality and noise covariance from the model. Information transfer observed from teacher to learner in the lateral component of their motion indicated imitation of the spatial information of the puzzle solution. Information transfer from learner to teacher in the vertical component of their motion indicated the monitoring process through which teachers adjust their timing of demonstration to the learner’s progress. The shared information in the lateral component increased as learning progressed, indicating the knowledge was shared between the two agents. Our findings demonstrated that the teacher interactively engaged in and contingently supported (i.e. scaffolded) imitation. We thus provide a behavioural signature of the teacher’s intention to promote learning indispensable for understanding the nature of teaching.

In a conventional view of motor control, the human brain might employ an optimization principle that leads a stereotypical motor behavior which we observe as an averaged behavioral data over subjects. In this scenario, the inter-individual motor variability is considered as an observation noise. Here, we challenged this view. We considered a motor control task where the human participants manipulated arm force by coordinating shoulder and elbow torques and investigated the muscle-tuning function that represents how the brain distributed the ideal joint torques to multiple muscles. In the experimental data, we observed large inter-individual variability in the profile of a muscle-tuning function. This contradicts with a well-established optimization theory that is based on minimization of muscle energy consumption and minimization of motor variability. We then hypothesized the inter-subject differences in the structure of the motor cortical areas might be the source of the across-subjects variability of the motor behavior. This was supported by a voxel-based morphometry analysis of magnetic resonance imaging; The inter-individual variability of the muscle tuning profile was correlated with that of the gray matter volume in the premotor cortex which is ipsilateral to the used arm (i.e., right hemisphere for the right arm). This study suggests that motor individuality may originate from inter-individual variation in the cortical structure.

To characterize each cognitive function per se and to understand the brain as an aggregate of those functions, it is vital to relate dozens of these functions to each other. Knowledge about the relationships among cognitive functions is informative not only for basic neuroscientific research but also for clinical applications and developments of brain-inspired artificial intelligence. In the present study, we propose an exhaustive data mining approach to reveal relationships among cognitive functions based on functional brain mapping and network analysis. We began our analysis with 109 pseudo-activation maps (cognitive function maps; CFM) that were reconstructed from a functional magnetic resonance imaging meta-analysis database, each of which corresponds to one of 109 cognitive functions such as 'emotion,' 'attention,' 'episodic memory,' etc. Based on the resting-state functional connectivity between the CFMs, we mapped the cognitive functions onto a two-dimensional space where the relevant functions were located close to each other, which provided a rough picture of the brain as an aggregate of cognitive functions. Then, we conducted so-called conceptual analysis of cognitive functions using clustering of voxels in each CFM connected to the other 108 CFMs with various strengths. As a result, a CFM for each cognitive function was subdivided into several parts, each of which is strongly associated with some CFMs for a subset of the other cognitive functions, which brought in sub-concepts (i.e., sub-functions) of the cognitive function. Moreover, we conducted network analysis for the network whose nodes were parcels derived from whole-brain parcellation based on the whole-brain voxel-to-CFM resting-state functional connectivities. Since each parcel is characterized by associations with the 109 cognitive functions, network analyses using them are expected to inform about relationships between cognitive and network characteristics. Indeed, we found that informational diversities of interaction between parcels and densities of local connectivity were dependent on the kinds of associated functions. In addition, we identified the homogeneous and inhomogeneous network communities about the associated functions. Altogether, we suggested the effectiveness of our approach in which we fused the large-scale meta-analysis of functional brain mapping with the methods of network neuroscience to investigate the relationships among cognitive functions.

<p>Purpose: To examine the relationship of subjective visual vertical (SVV) with static postural balance and gait asymmetry in patients with lateral medullary infarction.</p><p>Method: Nine participants with body lateropulsion (BL) due to lateral medullary infarction were enrolled. The relationships of SVV with three stabilometric parameters (length of center of pressure, envelopment area, and weight-bearing asymmetry during standing) and gait asymmetry were investigated.</p><p>Results: The mean (SD) SVV was found to be 7.4° (9.5°). SVV values significantly correlated with weight-bearing asymmetry in the eye-closed condition (r = 0.75, P < 0.05), whereas they did not significantly correlate with weight-bearing asymmetry in the eye-open condition. In addition, the absolute values of SVV significantly correlated with gait asymmetry (r = –0.78, P < 0.05).</p><p>Conclusion: In patients with lateral medullary infarction with BL, SVV was related to static postural balance in the eye-closed condition and gait asymmetry. Further investigation is needed to examine the causality between SVV and these measures.</p>

Objective The aim of this study was to investigate whether anodal transcranial direct current stimulation over the left temporoparietal area improved audioverbal memory performance in stroke patients.
Design Twelve stroke patients with audioverbal memory impairment participated in a single-masked, crossover, and sham-controlled experiment. The anodal or sham transcranial direct current stimulation was applied during the Rey Auditory Verbal Learning Test, which evaluates the ability to recall a list of 15 heard words over five trials. The number of correctly recalled words was compared between the anodal and sham conditions and the influence of transcranial direct current stimulation on serial position effect of the 15 words was also examined.
Results The increase in the number of correctly recalled words from the first to the fifth trial was significantly greater in the anodal condition than in the sham condition (P &lt; 0.01). There was a significant difference (P &lt; 0.01) between the anodal and sham conditions in the number of correctly recalled words within the first five words (primacy region) over the second to fifth trial trials, but not in the middle (next five words) or recency (last five words) regions.
Conclusions Anodal transcranial direct current stimulation over the left temporoparietal area improved audioverbal memory performance and induced the primacy effect in stroke patients.

PURPOSE: Older and/or cognitively impaired patients require verbal guidance to prevent accidents during wheelchair operation, thus increasing the burden on caregivers. This study aimed to develop a new portable voice guidance device for manual wheelchairs and examine its clinical usefulness. METHOD: We developed a portable voice guidance device to monitor the statuses of wheelchair brakes and footrests and automatically provide voice guidance for operation. The device comprises a microcomputer, four magnets and magnetic sensors, speaker and battery. Device operation was assessed during the transfer from a wheelchair to bed six times per day over three days for a total of 90 transfers in five stroke patients (mean age: 79.6 years) who required verbal guidance to direct wheelchair operation. Device usability was also assessed using a questionnaire. RESULTS: The device performed perfectly during all attempted transfers (100%). To ensure safety, the assessor needed to add verbal guidance during 33 of 90 attempted transfers (36.6%). Overall, the device usability was favourable. However, some assessors were unsatisfied with the volume of the device voice, guidance timing and burden reduction. CONCLUSIONS: Our device could facilitate wheelchair operation and might potentially be used to reduce fall risk in stroke patients and the burden on caregivers. Implications for Rehabilitation The acquisition of transfer independence is an important step in the rehabilitation of patients with mobility issues. Many patients require supervision and guidance regarding the operation of brakes and footrests on manual wheelchairs. This newly developed voice guidance device for manual wheelchair transfers worked well in patients with hemiplegia and might be helpful to reduce the fall risks and the burden of care.

Functional near-infrared spectroscopy (fNIRS) is a widely utilized neuroimaging tool in fundamental neuroscience research and clinical investigation. Previous research has revealed that task-evoked systemic artifacts mainly originating from the superficial-tissue may preclude the identification of cerebral activation during a given task. We examined the influence of such artifacts on event-related brain activity during a brisk squeezing movement. We estimated task-evoked superficial-tissue hemodynamics from short source-detector distance channels (15 mm) by applying principal component analysis. The estimated superficial-tissue hemodynamics exhibited temporal profiles similar to the canonical cerebral hemodynamic model. Importantly, this task-evoked profile was also observed in data from a block design motor experiment, suggesting a transient increase in superficial-tissue hemodynamics occurs following motor behavior, irrespective of task design. We also confirmed that estimation of event-related cerebral hemodynamics was improved by a simple superficial-tissue hemodynamic artifact removal process using 15-mm short distance channels, compared to the results when no artifact removal was applied. Thus, our results elucidate task design-independent characteristics of superficial-tissue hemodynamics and highlight the need for the application of superficial-tissue hemodynamic artifact removal methods when analyzing fNIRS data obtained during event-related motor tasks. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

The cortical motor areas are activated not only during contralateral limb movements but also during ipsilateral limb movements. Although these ipsilateral activities have been observed in several brain imaging studies, their functional role is poorly understood. Due to its high temporal resolution and low susceptibility to artifacts from body movements, the electrocorticogram (ECoG) is an advantageous measurement method for assessing the human brain function of motor behaviors. Here, we demonstrate that contra- and ipsilateral movements share a similarity in the high-frequency band of human ECoG signals. The ECoG signals were measured from the unilateral sensorimotor cortex while patients conducted self-paced movements of different body parts, contra or ipsilateral to the measurement side. The movement categories (wrist, shoulder, or ankle) of ipsilateral movements were decoded as accurately as those of contralateral movements from spatial patterns of the high frequency band of the precentral motor area (the primary motor and premotor areas). The decoder, trained in the high-frequency band of ipsilateral movements generalized to contralateral movements, and vice versa, confirmed that the activity patterns related to ipsilateral limb movements were similar to contralateral ones in the precentral motor area. Our results suggest that the high-frequency band activity patterns of ipsilateral and contralateral movements might be functionally coupled to control limbs, even during unilateral movements.

Functional near-infrared spectroscopy (fNIRS) is used to measure cerebral activity because it is simple and portable. However, scalp-hemodynamics often contaminates fNIRS signals, leading to detection of cortical activity in regions that are actually inactive. Methods for removing these artifacts using standard source-detector distance channels (Long-channel) tend to over-estimate the artifacts, while methods using additional short source-detector distance channels (Short-channel) require numerous probes to cover broad cortical areas, which leads to a high cost and prolonged experimental time. Here, we propose a new method that effectively combines the existing techniques, preserving the accuracy of estimating cerebral activity and avoiding the disadvantages inherent when applying the techniques individually. Our new method accomplishes this by estimating a global scalp-hemodynamic component from a small number of Short-channels, and removing its influence from the Long-channels using a general linear model (GLM). To demonstrate the feasibility of this method, we collected fNIRS and functional magnetic resonance imaging (fMRI) measurements during a motor task. First, we measured changes in oxygenated hemoglobin concentration (Delta Oxy-Hb) from 18 Short-channels placed over motor-related areas, and confirmed that the majority of scalp-hemodynamics was globally consistent and could be estimated from as few as four Short-channels using principal component analysis. We then measured Delta Oxy-Hb from 4 Short- and 43 Long-channels. The GLM identified cerebral activity comparable to that measured separately by fMRI, even when scalp-hemodynamics exhibited substantial task-related modulation. These results suggest that combining measurements from four Short-channels with a GLM provides robust estimation of cerebral activity at a low cost. (C) 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license.

Sensory disturbance is a very common following stroke, and severe sensory loss may inhibit the ability of patients to use the affected upper limb in daily activities, even when they have good motor function. We hypothesized that task-specific training with sensory feedback may improve patients' ability to manipulate objects. We developed a system of sensory feedback using transcutaneous electrical nerve stimulation (SENS) for stroke rehabilitation and investigated its effectiveness. In this study, we conducted a case studies with stroke patients. The instability of tip pressure during a cube pinch and lifting task was improved after one hour's training with SENS, although it was not changed by training without SENS. We concluded that SENS would be useful in the rehabilitation of stroke patients with sensory loss.

Patients who have suffered impairment of their neuromotor abilities due to a disease or accident have to relearn to control their bodies. For example, after stroke the ability to coordinate the movements of the upper limb in order to reach and grasp an object could be severely damaged. Or in the case of amputees, the functional ability is completely lost.

Humans are capable of achieving complex tasks with redundant degrees of freedom. Much attention has been paid to task relevant variance modulation as an indication of online feedback control strategies to cope with motor variability. Meanwhile, it has been discussed that the brain learns internal models of environments to realize feedforward control with nominal trajectories. Here we examined trajectory variance in both spatial and temporal domains to elucidate the relative contribution of these control schemas. We asked subjects to learn reaching movements with multiple via-points, and found that hand trajectories converged to stereotyped trajectories with the reduction of task relevant variance modulation as learning proceeded. Furthermore, variance reduction was not always associated with task constraints but was highly correlated with the velocity profile. A model assuming noise both on the nominal trajectory and motor command was able to reproduce the observed variance modulation, supporting an expression of nominal trajectories in the brain. The learning-related decrease in task-relevant modulation revealed a reduction in the influence of optimal feedback around the task constraints. After practice, the major part of computation seems to be taken over by the feedforward controller around the nominal trajectory with feedback added only when it becomes necessary.

How do humans choose one arm or the other to reach single targets in front of the body? Current theories of reward-driven decisionmaking predict that choice results from a comparison of "action values," which are the expected rewards for possible actions in a given state. In addition, current theories of motor control predict that in planning arm movements, humans minimize an expected motor cost that balances motor effort and endpoint accuracy. Here, we test the hypotheses that arm choice is determined by comparison of action values comprising expected effort and expected task success for each arm, as well as a handedness bias. Right-handed subjects, in either a large or small target condition, were first instructed to use each hand in turn to shoot through an array of targets and then to choose either hand to shoot through the same targets. Effort was estimated via inverse kinematics and dynamics. A mixed-effects logistic-regression analysis showed that, as predicted, both expected effort and expected success predicted choice, as did arm use in the preceding trial. Finally, individual parameter estimation showed that the handedness bias correlated with mean difference between right- and left-arm success, leading to overall lower use of the left arm. We discuss our results in light of arm nonuse in individuals' poststroke.

It has been suggested that resting-state brain activity reflects task-induced brain activity patterns. In this study, we examined whether neural representations of specific movements can be observed in the resting-state brain activity patterns of motor areas. First, we defined two regions of interest (ROIs) to examine brain activity associated with two different behavioral tasks. Using multi-voxel pattern analysis with regularized logistic regression, we designed a decoder to detect voxel-level neural representations corresponding to the tasks in each ROI. Next, we applied the decoder to resting-state brain activity. We found that the decoder discriminated resting-state neural activity with accuracy comparable to that associated with task-induced neural activity. The distribution of learned weighted parameters for each ROI was similar for resting-state and task-induced activities. Large weighted parameters were mainly located on conjunctive areas. Moreover, the accuracy of detection was higher than that for a decoder whose weights were randomly shuffled, indicating that the resting-state brain activity includes multi-voxel patterns similar to the neural representation for the tasks. Therefore, these results suggest that the neural representation of resting-state brain activity is more finely organized and more complex than conventionally considered.

Motor memory is updated to generate ideal movements in a novel environment. When the environment changes every trial randomly, how does the brain incorporate this uncertainty into motor memory? To investigate how the brain adapts to an uncertain environment, we considered a reach adaptation protocol where individuals practiced moving in a force field where a noise was injected. After they had adapted, we measured the trial-to-trial variability in the temporal profiles of the produced hand force. We found that the motor variability was significantly magnified by the adaptation to the random force field. Temporal profiles of the motor variance were significantly dissociable between two different types of random force fields experienced. A model-based analysis suggests that the variability is generated by noise in the gains of the internal model. It further suggests that the trial-to-trial motor variability magnified by the adaptation in a random force field is generated by the uncertainty of the internal model formed in the brain as a result of the adaptation. (C) 2014 Wolters Kluwer Health vertical bar Lippincott Williams & Wilkins.

Human dexterity with tools is believed to stem from our ability to incorporate and use tools as parts of our body. However tool incorporation, evident as extensions in our body representation and peri-personal space, has been observed predominantly after extended tool exposures and does not explain our immediate motor behaviours when we change tools. Here we utilize two novel experiments to elucidate the presence of additional immediate tool incorporation effects that determine motor planning with tools. Interestingly, tools were observed to immediately induce a trial-by-trial, tool length dependent shortening of the perceived limb lengths, opposite to observations of elongations after extended tool use. Our results thus exhibit that tools induce a dual effect on our body representation; an immediate shortening that critically affects motor planning with a new tool, and the slow elongation, probably a consequence of skill related changes in sensory-motor mappings with the repeated use of the tool.

How do physical interactions with others change our own motor behavior? Utilizing a novel motor learning paradigm in which the hands of two - individuals are physically connected without their conscious awareness, we investigated how the interaction forces from a partner adapt the motor behavior in physically interacting humans. We observed the motor adaptations during physical interactions to be mutually beneficial such that both the worse and better of the interacting partners improve motor performance during and after interactive practice. We show that these benefits cannot be explained by multi-sensory integration by an individual, but require physical interaction with a reactive partner. Furthermore, the benefits are determined by both the interacting partner's performance and similarity of the partner's behavior to one's own. Our results demonstrate the fundamental neural processes underlying human physical interactions and suggest advantages of interactive paradigms for sport-training and physical rehabilitation.

Our brain is known to automatically optimize effort expenditure during motor coordination, such that for example, during bimanual braking of a bicycle, a well-oiled brake will automatically be used more than a corroded, heavy brake. But how does our brain infer the effort expenditure? All previous motor coordination models have believed that the effort in a task is known precisely to our brain, solely from the motor commands it generates. Here we show that this belief is incorrect. Through experiments and simulation we exhibit that in addition to the motor commands, the returning haptic signals play a crucial role in the inference of the effort during a force sharing task. Our results thus elucidate a previously unknown sensory-motor association that has major ramifications for our understanding of motor coordination and provides new insights into how sensory modifications due to ergonomics, stroke and disease can affect motor coordination in humans.

BACKGROUND: Sensory disturbance is common following stroke and can exacerbate functional deficits, even in patients with relatively good motor function. In particular, loss of appropriate sensory feedback in severe sensory loss impairs manipulation capability. We hypothesized that task-oriented training with sensory feedback assistance would improve manipulation capability even without sensory pathway recovery. METHODS: We developed a system that provides sensory feedback by transcutaneous electrical nerve stimulation (SENS) for patients with sensory loss, and investigated the feasibility of the system in a stroke patient with severe sensory impairment and mild motor deficit. The electrical current was modulated by the force exerted by the fingertips so as to allow the patient to identify the intensity. The patient had severe sensory loss due to a right thalamic hemorrhage suffered 27 months prior to participation in the study. The patient first practiced a cylindrical grasp task with SENS for 1 hour daily over 29 days. Pressure information from the affected thumb was fed back to the unaffected shoulder. The same patient practiced a tip pinch task with SENS for 1 hour daily over 4 days. Pressure information from the affected thumb and index finger was fed back to the unaffected and affected shoulders, respectively. We assessed the feasibility of SENS and examined the improvement of manipulation capability after training with SENS. RESULTS: The fluctuation in fingertip force during the cylindrical grasp task gradually decreased as the training progressed. The patient was able to maintain a stable grip force after training, even without SENS. Pressure exerted by the tip pinch of the affected hand was unstable before intervention with SENS compared with that of the unaffected hand. However, they were similar to each other immediately after SENS was initiated, suggesting that the somatosensory information improved tip pinch performance. The patient's manipulation capability assessed by the Box and Block Test score improved through SENS intervention and was partly maintained after SENS was removed, until at least 7 months after the intervention. The sensory test score, however, showed no recovery after intervention. CONCLUSIONS: We conclude that the proposed system would be useful in the rehabilitation of patients with sensory loss.

Background. Arm nonuse, defined as the difference between what the individual can do when constrained to use the paretic arm and what the individual does when given a free choice to use either arm, has not yet been quantified in individuals poststroke. Objectives. (1) To quantify nonuse poststroke and (2) to develop and test a novel, simple, objective, reliable, and valid instrument, the Bilateral Arm Reaching Test (BART), to quantify arm use and nonuse poststroke. Methods. First, we quantify nonuse with the Quality of Movement (QOM) subscale of the Actual Amount of Use Test (AAUT) by subtracting the AAUT QOM score in the spontaneous use condition from the AAUT QOM score in a subsequent constrained use condition. Second, we quantify arm use and nonuse with BART by comparing reaching performance to visual targets projected over a 2D horizontal hemi-work space in a spontaneous-use condition (in which participants are free to use either arm at each trial) with reaching performance in a constrained-use condition. Results. All participants (N = 24) with chronic stroke and with mild to moderate impairment exhibited nonuse with the AAUT QOM. Nonuse with BART had excellent test-retest reliability and good external validity. Conclusions. BART is the first instrument that can be used repeatedly and practically in the clinic to quantify the effects of neurorehabilitation on arm use and nonuse and in the laboratory for advancing theoretical knowledge about the recovery of arm use and the development of nonuse and "learned nonuse" after stroke.

Objective: Pedaling is widely used for rehabilitation of locomotion because it induces muscle activity very similar to locomotion. Afferent stimulation is important for the modulation of spinal reflexes. Furthermore, supraspinal modulation plays an important role in spinal plasticity induced by electrical stimulation. We, therefore, expected that active pedaling combined with electrical stimulation could induce strong after-effects on spinal reflexes.
Design: Twelve healthy adults participated in this study. They were instructed to perform 7 min of pedaling. We applied electrical stimulation to the common peroneal nerve during the extension phase of the pedaling cycle. We assessed reciprocal inhibition using a soleus H-reflex conditioning-test paradigm. The magnitude of reciprocal inhibition was measured before, immediately after, 15 and 30 min after active pedaling alone, electrical stimulation alone and active pedaling combined with electrical stimulation (pedaling + ES).
Results: The amount of reciprocal inhibition was significantly increased after pedaling + ES. The aftereffect of pedaling + ES on reciprocal inhibition was more prominent and longer lasting compared with pedaling or electrical stimulation alone.
Conclusions: Pedaling + ES could induce stronger after-effects on spinal reciprocal inhibitory neurons compared with either intervention alone. Pedaling + ES might be used as a tool to improve locomotion and functional abnormalities in the patient with central nervous lesion. (C) 2012 Elsevier Ltd. All rights reserved.

Transcranial direct current stimulation (tDCS) is a noninvasive, painless cortical stimulation technique. Depending on the polarity of the current flow, brain excitability can either be increased by the anodal tDCS or decreased by the cathodal tDCS. The aim of the present study was to examine the behavioral effects of direct current delivery on left posterior temporal and inferior parietal cortices during the Rey's auditory verbal learning test (RAVLT) that examine audioverbal short-term memory. The RAVLT includes a list of 15 common words to be read one-by-one. After the presentation of 15 words, the subjects are required to repeat as many words as they can. The encoding-recall procedure is repeated five times. Twelve healthy subjects performed the RAVLT, during which, 10 minutes anodal and 15 seconds sham current was applied on left posterior temporal and inferior parietal cortex from the second encoding process. There was a significant difference in the mean number of remembered words between anodal and sham stimulation on the second process. Anodal tDCS increased the number of remembered words when compared to sham stimulation. Our results imply that anodal tDCS induced short-term modulation of the left posterior temporal and inferior parietal cortex while learning auditory presented words. tDCS is a promising tool for improving memory performance.

Measuring discrete-trial motor-related brain activity using functional near-infrared spectroscopy (fNIRS) is considered difficult. This is because its spatial resolution is much lower than that of functional magnetic resonance imaging (fMRI), and its signals include non-motion-related artifacts. To detect changes in hemoglobin induced by movements, most fNIRS studies have used a block design in which a subject conducts a set of repetitive movements for over a few seconds. Changes in hemoglobin induced by the series of movements are accumulated. Here, we address whether fNIRS can detect a phasic change induced by a discrete ballistic movement using an event-related design similar to those often adopted in fMRI experiments. To detect only event-related brain activity and to reduce the effect of artifacts, we adopted a general linear model whose design matrix contains data from the short transmitter-receiver distance channels that are considered components of artifacts. As a result, high event-related activity was detected in the contralateral sensorimotor cortex. We also compared the topographic functional map produced by fNIRS with the map given by an event-related fMRI experiment in which the same subjects performed exactly the same task. Both maps showed activity in equivalent areas, and the similarity was significant. We conclude that fNIRS affords the opportunity to explore motor-related brain activity even for discrete ballistic movements.

Objective: For the recovery of hemiparetic hand function, a therapy was developed called contralateral homonymous muscle activity stimulated electrical stimulation (CHASE), which combines electrical stimulation and bilateral movements, and its feasibility was studued in three chronic stroke patients with severe hand hemiparesis.
Methods: Patients with a subcortical lesion were asked to extend their wrist and fingers bilaterally while an electromyogram (EMG) was recorded from the extensor carpi radialis (ECR) muscle in the unaffected hand. Electric stimulation was applied to the homonymous wrist and finger extensors of the affected side. The intensity of the electrical stimulation was computed based on the EMG and scaled so that the movements of the paretic hand looked similar to those of the unaffected side. The patients received 30-minutes of therapy per day for 2 weeks.
Results: Improvement in the active range of motion of wrist extension was observed for all patients. There was a decrease in the scores of modified Ashworth scale in the flexors. Fugl-Meyer assessment scores of motor function of the upper extremities improved in two of the patients.
Conclusions: The results suggest a positive outcome can be obtained using the CHASE system for upper extremity rehabilitation of patients with severe hemiplegia.

Background: We developed an electroencephalogram-based brain computer interface system to modulate functional electrical stimulation (FES) to the affected tibialis anterior muscle in a stroke patient. The intensity of FES current increased in a stepwise manner when the event-related desynchronization (ERD) reflecting motor intent was continuously detected from the primary cortical motor area.
Methods: We tested the feasibility of the ERD-modulated FES system in comparison with FES without ERD modulation. The stroke patient who presented with severe hemiparesis attempted to perform dorsiflexion of the paralyzed ankle during which FES was applied either with or without ERD modulation.
Results: After 20 minutes of training, the range of movement at the ankle joint and the electromyography amplitude of the affected tibialis anterior muscle were significantly increased following the ERD-modulated FES compared with the FES alone.
Conclusions: The proposed rehabilitation technique using ERD-modulated FES for stroke patients was feasible. The system holds potentials to improve the limb function and to benefit stroke patients.

Previous simulation and experimental studies have demonstrated that the application of Variational Bayesian Multimodal EncephaloGraphy (VBMEG) to magnetoencephalography (MEG) data can be used to estimate cortical currents with high spatio-temporal resolution, by incorporating functional magnetic resonance imaging (fMRI) activity as a hierarchical prior. However, the use of combined MEG and fMRI is restricted by the high costs involved, a lack of portability and high sensitivity to body-motion artifacts. One possible solution for overcoming these limitations is to use a combination of electroencephalography (EEG) and near-infrared spectroscopy (NIRS). This study therefore aimed to extend the possible applications of VBMEG to include EEG data with NIRS activity as a hierarchical prior. Using computer simulations and real experimental data, we evaluated the performance of VBMEG applied to EEG data under different conditions, including different numbers of EEG sensors and different prior information. The results suggest that VBMEG with NIRS prior performs well, even with as few as 19 EEG sensors. These findings indicate the potential value of clinically applying VBMEG using a combination of EEG and NIRS. (C) 2011 Elsevier Inc. All rights reserved.

Movement error is a driving force behind motor learning. For motor learning with discrete movements, such as point-to-point reaching, it is believed that the brain uses error information of the immediately preceding movement only. However, in the case of continuous and repetitive movements (i.e., rhythmic movements), there is a ceaseless inflow of performance information. Thus, an accurate temporal association of the motor commands with the resultant movement errors is not necessarily guaranteed. We investigated how the brain overcomes this challenging situation. Human participants adapted rhythmic movements between two targets to visuomotor rotations, the amplitudes of which changed randomly from cycle to cycle (the duration of one cycle was similar to 400 ms). A system identification technique revealed that the motor adaptation was affected not just by the preceding movement error, but also by a history of errors from the previous cycles. Error information obtained from more than one previous cycle tended to increase, rather than decrease, movement error. This result led to a counterintuitive prediction: providing visual error feedback for only a fraction of cycles should enhance visuomotor adaptation. As predicted, we observed that motor adaptation to a constant visual rotation (30 degrees) was significantly enhanced by providing visual feedback once every fourth or fifth cycle rather than for every cycle. These results suggest that the brain requires a specific processing time to modify the motor command, based on the error information, and so is unable to deal appropriately with the overwhelming flow of error information generated during rhythmic movements.

Background: To more accurately evaluate rehabilitation outcomes in stroke patients, movement irregularities should be quantified. Previous work in stroke patients has revealed a reduction in the trajectory smoothness and segmentation of continuous movements. Clinically, the Stroke Impairment Assessment Set (SIAS) evaluates the clumsiness of arm movements using an ordinal scale based on the examiner&apos;s observations. In this study, we focused on three-dimensional curvature of hand trajectory to quantify movement, and aimed to establish a novel measurement that is independent of movement duration. We compared the proposed measurement with the SIAS score and the jerk measure representing temporal smoothness.
Methods: Sixteen stroke patients with SIAS upper limb proximal motor function (Knee-Mouth test) scores ranging from 2 (incomplete performance) to 4 (mild clumsiness) were recruited. Nine healthy participant with a SIAS score of 5 (normal) also participated. Participants were asked to grasp a plastic glass and repetitively move it from the lap to the mouth and back at a conformable speed for 30 s, during which the hand movement was measured using OPTOTRAK. The position data was numerically differentiated and the three-dimensional curvature was computed. To compare against a previously proposed measure, the mean squared jerk normalized by its minimum value was computed. Age-matched healthy participants were instructed to move the glass at three different movement speeds.
Results: There was an inverse relationship between the curvature of the movement trajectory and the patient&apos;s SIAS score. The median of the -log of curvature (MedianLC) correlated well with the SIAS score, upper extremity subsection of Fugl-Meyer Assessment, and the jerk measure in the paretic arm. When the healthy participants moved slowly, the increase in the jerk measure was comparable to the paretic movements with a SIAS score of 2 to 4, while the MedianLC was distinguishable from paretic movements.
Conclusions: Measurement based on curvature was able to quantify movement irregularities and matched well with the examiner&apos;s observations. The results suggest that the quality of paretic movements is well characterized using spatial smoothness represented by curvature. The smaller computational costs associated with this measurement suggest that this method has potential clinical utility.

In periodic bimanual movements, anti-phase-coordinated patterns often change into in-phase patterns suddenly and involuntarily. Because behavior in the initial period of a sequence of cycles often does not show any obvious errors, it is difficult to predict subsequent movement errors in the later period of the cyclical sequence. Here, we evaluated performance in the later period of the cyclical sequence of bimanual periodic movements using human brain activity measured with functional magnetic resonance imaging as well as using initial movement features. Eighteen subjects performed a 30 s bimanual finger-tapping task. We calculated differences in initiation-locked transient brain activity between antiphase and in-phase tapping conditions. Correlation analysis revealed that the difference in the anterior putamen activity during antiphase compared within-phase tapping conditions was strongly correlated with future instability as measured by the mean absolute deviation of the left-hand intertap interval during antiphase movements relative to in-phase movements (r = 0.81). Among the initial movement features we measured, only the number of taps to establish the antiphase movement pattern exhibited a significant correlation. However, the correlation efficient of 0.60 was not high enough to predict the characteristics of subsequent movement. There was no significant correlation between putamen activity and initial movement features. It is likely that initiating unskilled difficult movements requires increased anterior putamen activity, and this activity increase may facilitate the initiation of movement via the basal ganglia-thalamocortical circuit. Our results suggest that initiation-locked transient activity of the anterior putamen can be used to predict future motor performance.

Background. Transcranial direct current stimulation (tDCS) of the motor cortex can enhance the performance of a paretic upper extremity after stroke. Reported effects on lower limb (LL) function are sparse. Objective. The authors examined whether tDCS can increase the force production of the paretic quadriceps. Methods. In this double-blind, crossover, sham-controlled experimental design, 8 participants with chronic subcortical stroke performed knee extension using their hemiparetic leg before, during, and after anodal or sham tDCS of the LL motor cortex representation in the affected hemisphere. Affected hand-grip force was also recorded. Results. The maximal knee-extension force increased by 21 N (13.2%, P < .01) during anodal tDCS compared with baseline and sham stimulation. The increase persisted less than 30 minutes. Maximal hand-grip force did not change. Conclusions. Anodal tDCS transiently enhanced knee extensor strength. The modest increase was specific to the LL. Thus, tDCS might augment the rehabilitation of stroke patients when combined with lower extremity strengthening or functional training.

【目的】<BR>踵接地の検出は歩行解析を行う上で必要不可欠であり，その検出にはフットスイッチを使用することが多い．フットスイッチはある圧力が加わったことにより踵接地のタイミングを検出するため，装着位置により計測データがばらつく可能性がある．我々は足底に導電性の布を装着し，導電性の素材を貼付したトレッドミルとの接触を感知することで踵接地タイミングを検出するシステムを開発した．本研究は高速度カメラ，従来のフットスイッチ，開発した接触センサを同時に用いて踵接地のタイミングを計測し，フットスイッチと接触センサの精度を比較した．<BR>【方法】<BR>健常成人7名（25.1±4.1歳）を対象とした．被験者は75 m/minに設定したトレッドミル上を歩行した．踵接地タイミングの計測には高速度カメラ（200 frames/s)，接触センサ（Sampling: 1 kHz），フットスイッチ（踵に1 cm間隔で2か所に貼付，Sampling: 1 kHz）を用いた．20歩分の右踵接地タイミングを分析対象とし，高速度カメラをコマ送りして目視で接地タイミングを検出した．カメラで得られたタイミングと，接触センサ・踵後部フットスイッチ・踵前部フットスイッチの検出タイミングの差について7名の平均値と標準偏差（SD）を算出した．平均値，SDに対して3群間でWilcoxon signed-rank testにより差を検定した．<BR>【結果】<BR>高速度カメラで得られた踵接地タイミングとの差の平均値について，3群間すべてで有意差があり，接触センサ，踵後部，踵前部の順に差が小さく，いずれも高速度カメラより接地タイミングが遅かった．SDは踵前部フットスイッチが接触センサよりも有意に大きかった（いずれもBonferroni corrected p<.05）．<BR>【考察】<BR>接触センサと高速度カメラとの検出タイミングの差が最も小さかったことより，接触センサはフットスイッチより正確に踵接地を計測できることが示された．また，フットスイッチの貼付位置により踵接地タイミングがずれ得ることが示された．踵接地時に足関節は背屈位であり踵後部から前方へ順に接地していくため，フットスイッチの検出タイミングに差が生じたと考えられる．Initial contactが踵接地でないまたは一定しない脳卒中片麻痺患者などの歩行を分析する際，フットスイッチでは踵接地タイミングを正確に計測できない場合が生じる可能性が考えられる．<BR>【まとめ】<BR>本研究結果より，フットスイッチは貼付位置や異常歩行により踵接地タイミングを正確に計測できない可能性があり，接触センサはより正確に踵接地を計測できることが示された．今後，測定条件や対象者を変えて，接触センサとフットスイッチの検出タイミングについて検討していきたい．

Sensory disturbance is very common following stroke and may exacerbate a patient's functional impairment, even if the patient has good motor function. For instance, patients with sensory disturbances will often grip an object with excessive or underestimated pinch pressure, because they do not receive the appropriate sensory feedback and must rely only on visual feedback. In this study, we developed a sensory feedback system that used cutaneous electrical stimulation for patients with sensory loss. In the system, electrical stimulation is modulated by the strength of pinch pressure and the patients are able to identify their fingertip pinch pressure. To evaluate the efficacy of the system, a clinical case study was conducted in a stroke patient with severe sensory loss. The fluctuation in force control during grasping was gradually decreased as the training progressed and the patient was able to maintain a stable pinch pressure during grasping even without the system following 2 months of intervention. We conclude that the system described in this study may be a useful contribution towards the rehabilitation of patients with sensory loss.

Efficient human motor control is characterized by an extensive use of joint impedance modulation, which is achieved by co-contracting antagonistic muscles in a way that is beneficial to the specific task. While there is much experimental evidence available that the nervous system employs such strategies, no generally-valid computational model of impedance control derived from first principles has been proposed so far. Here we develop a new impedance control model for antagonistic limb systems which is based on a minimization of uncertainties in the internal model predictions. In contrast to previously proposed models, our framework predicts a wide range of impedance control patterns, during stationary and adaptive tasks. This indicates that many well-known impedance control phenomena naturally emerge from the first principles of a stochastic optimization process that minimizes for internal model prediction uncertainties, along with energy and accuracy demands. The insights from this computational model could be used to interpret existing experimental impedance control data from the viewpoint of optimality or could even govern the design of future experiments based on principles of internal model uncertainty.

When two hands require different information in bimanual asymmetric movements, interference can occur via callosal connections and ipsilateral corticospinal pathways. This interference could potentially work as a cost-effective measure in symmetric movements, allowing the same information to be commonly available to both hands at once. Using functional magnetic resonance imaging, we investigated supra-additive and sub-additive neural interactions in bimanual movements during the initiation and continuation phases of movement. We compared activity during bimanual asymmetric and symmetric movements with the sum of activity during unimanual right and left finger-tapping. Supra-additive continuation-related activation was found in the right dorsal premotor cortex and left cerebellum (lobule V) during asymmetric movements. In addition, for unimanual movements, the right dorsal premotor cortex and left cerebellum (lobule V) showed significant activation only for left-hand (non-dominant) movements, but not for right-hand movements. These results suggest that resource-demanding interactions in bimanual asymmetric movements are involved in a non-dominant hand motor network that functions to keep non-dominant hand movements stable. We found sub-additive continuation-related activation in the supplementary motor area (SMA), bilateral cerebellum (lobule VI) in symmetric movements, and the SMA in asymmetric movements. This suggests that no extra demands were placed on these areas in bimanual movements despite the conventional notion that they play crucial roles in bimanual coordination. Sub-additive initiation-related activation in the left anterior putamen suggests that symmetric movements place lower demands on motor programming. These findings indicate that, depending on coordination patterns, the neural substrates of bimanual movements either exhibit greater effort to keep non-dominant hand movements stable, or save neural cost by sharing information commonly to both hands.

The present study investigated a skill-level-dependent interaction between gravity and muscular force when striking piano keys. Kinetic analysis of the arm during the downswing motion performed by expert and novice piano players was made using an inverse dynamic technique. The corresponding activities of the elbow agonist and antagonist muscles were simultaneously recorded using electromyography (EMG). Muscular torque at the elbow joint was computed while excluding the effects of gravitational and motion-dependent interaction torques. During descending the forearm to strike the keys, the experts kept the activation of the triceps (movement agonist) muscle close to the resting level, and decreased anti-gravity activity of the biceps muscle across all loudness levels. This suggested that elbow extension torque was produced by gravity without the contribution of agonist muscular work. For the novices, on the other hand, a distinct activity in the triceps muscle appeared during the middle of the downswing, and its amount and duration we e increased with increasing loudness. Therefore, for the novices, agonist muscular force was the predominant contributor to the acceleration of elbow extension during the downswing. We concluded that a balance shift from muscular force dependency to gravity dependency for the generation of a target joint torque occurs with long-term piano training. This shift would support the notion of non-muscular force utilization for improving physiological efficiency of limb movement with respect to the effective use of gravity. (C) 2009 IBRO. Published by Elsevier Ltd. All rights reserved.

Neural correlates of driving and of decision making have been investigated separately, but little is known about the underlying neural mechanisms of decision making in driving. Previous research discusses two types of decision making: reward-weighted decision making and cost-weighted decision making. There are many reward-weighted decision making neuroimaging Studies but there are few cost-weighted studies. Considering that driving involves serious risk, it is assumed that decision making in driving is cost weighted. Therefore, neural substrates of cost-weighted decision making can be assessed by investigation of driver&apos;s decision making. In this study, neural correlates of resolving uncertainty in driver&apos;s decision making were investigated. Turning right in left-hand traffic at a signalized intersection was simulated by computer graphic animation based videos. When the driver&apos;s view was occluded by a big truck, the uncertainty of the oncoming traffic was resolved by an in-car video assist system that presented the driver&apos;s occluded view. Resolving the uncertainty reduced activity in a distributed area including the amygdala and anterior cingulate. These results implicate the amygdala and anterior cingulate as serving a role in cost-weighted decision making. Hum Brain Mapp 30:5804-2812, 2009. (C) 2008 Wiley-Liss. Inc.

Despite the existence of neural noise, which leads variability in motor commands, the central nervous system can effectively reduce movement variance at the end effector to meet task requirements. Although online correction based on feedback information is essential for reducing error, feedforward impedance control is another way to regulate motor variability. This Update Article reviews key studies examining the relation between task constraints and impedance control for human arm movement. When a smaller reaching target is given as a task constraint, flexor and extensor muscles are co-activated, and positional variance is decreased around the task constraint. Trial-by-trial muscle activations revealed no on-line feedback correction, indicating that humans are able to regulate their impedance in advance. These results demonstrate that not only on-line feedback correction, but also feedforward impedance control, helps reduce the motor variability caused by internal noise to realize dexterous movements of human arms. A computational model of movement planning considering the presence of signal-dependent noise provides a unifying framework that potentially accounts for optimizing impedance to maximize accuracy. A recently proposed learning algorism formulated as a V-shaped learning function explains how the central nervous system acquires impedance to optimize accuracy as well as stability and efficiency. (C) 2009 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

Despite the existence of neural noise, which leads variability in motor commands, the central nervous system can effectively reduce movement variance at the end effector to meet task requirements. Although online correction based on feedback information is essential for reducing error, feedforward impedance control is another way to regulate motor variability. This Update Article reviews key studies examining the relation between task constraints and impedance control for human arm movement. When a smaller reaching target is given as a task constraint, flexor and extensor muscles are co-activated, and positional variance is decreased around the task constraint. Trial-by-trial muscle activations revealed no on-line feedback correction, indicating that humans are able to regulate their impedance in advance. These results demonstrate that not only on-line feedback correction, but also feedforward impedance control, helps reduce the motor variability caused by internal noise to realize dexterous movements of human arms. A computational model of movement planning considering the presence of signal-dependent noise provides a unifying framework that potentially accounts for optimizing impedance to maximize accuracy. A recently proposed learning algorism formulated as a V-shaped learning function explains how the central nervous system acquires impedance to optimize accuracy as well as stability and efficiency. (C) 2009 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

Near-infrared spectroscopy (NIRS) has recently been used to measure human motor-cortical activation, enabling the classification of the content of a sensory-motor event such as whether the left or right hand was used. Here, we advance this NIRS application by demonstrating quantitative estimates of multiple sensory-motor events from single-trial NIRS signals. It is known that different degrees of sensory-motor activation are required to generate various hand/finger force levels. Thus, using a sparse linear regression method, we examined whether the temporal changes in different force levels could be reconstructed from NIRS signals. We measured the relative changes in oxyhemoglobin concentrations in the bilateral sensory-motor cortices while participants performed an isometric finger-pinch force production with their thumb and index finger by repeatedly exerting one of three target forces (25, 50, or 75% of the maximum voluntary contraction) for 12 s. To reconstruct the generated forces, we determined the regression parameters from the training datasets and applied these parameters to new test datasets to validate the parameters in the single-trial reconstruction. The temporal changes in the three different levels of generated forces, as well as the baseline resting state, could be reconstructed, even for the test datasets. The best reconstruction was achieved when using only the selected NIRS channels dominantly located in the contralateral sensory-motor cortex, and with a four second hemodynamic delay. These data demonstrate the potential for reconstructing different levels of external loads (forces) from those of the internal loads (activation) in the human brain using NIRS. (C) 2009 Elsevier Inc. All rights reserved.

Background and objective. We devised a therapeutic approach to facilitate the use of the hemiparetic upper extremity (UE) in daily life by combining integrated volitional control electrical stimulation with a wrist splint, called hybrid assistive neuromuscular dynamic stimulation (HANDS). Methods. Twenty patients with chronic hemiparetic stroke ( median 17.5 months) had moderate to severe UE weakness. Before and immediately after completing 3 weeks of training in 40-minute sessions, 5 days per week over 3 weeks and wearing the system for 8 hours each day, clinical measures of motor impairment, spasticity, and UE functional scores, as well as neurophysiological measures including electromyography activity, reciprocal inhibition, and intracortical inhibition were assessed. A follow-up clinical assessment was performed 3 months later. Results. UE motor function, spasticity, and functional scores improved after the intervention. Neurophysiologically, the intervention induced restoration of presynaptic and long loop inhibitory connections as well as disynaptic reciprocal inhibition. Paired pulse transcranial magnetic stimulation study indicated disinhibition of the short intracortical inhibition in the affected hemisphere. The follow-up assessment showed that improved UE functions were maintained at 3 months. Conclusion. The combination of hand splint and volitional and electrically induced muscle contraction can induce corticospinal plasticity and may offer a promising option for the management of the paretic UE in patients with stroke. A larger sample size with randomized controls is needed to demonstrate effectiveness.

This paper discusses an integration issue of multi-level postural balancing on humanoid robot. We give a unified viewpoint of postural balancing, which covers Ankle Strategy to Hip Strategy. Two kinds of distributor of desired ground reaction force to whole-body joint torque are presented. The one distributor leads to a dynamic balancer which covers Hip strategy, with the under-actuated situation. A simple angular momentum regulator is also proposed to stabilize the internal motions due to the joint redundancy. The other distributor leads to a static balancer which lies between Ankle and Hip strategy. Furthermore, this paper demonstrates that replacement of the center of mass feedback with the local joint stiffness makes the robot much stabler for some fast motions. Motivated by the practicability of the static balancer and the strong push-recovery performance of the dynamic balancer, this paper presents a simple integration by superposition of the both balancers on a compliant human-sized biped robot. The simulation and experimental videos are supplemented.

We propose a new model of motor learning to explain the exceptional dexterity and rapid adaptation to change, which characterize human motor control. It is based on the brain simultaneously optimizing stability, accuracy and efficiency. Formulated as a V-shaped learning function, it stipulates precisely how feedforward commands to individual muscles are adjusted based on error. Changes in muscle activation patterns recorded in experiments provide direct support for this control scheme. In simulated motor learning of novel environmental interactions, muscle activation, force and impedance evolved in a manner similar to humans, demonstrating its efficiency and plausibility. This model of motor learning offers new insights as to how the brain controls the complex musculoskeletal system and iteratively adjusts motor commands to improve motor skills with practice.

【目的】<BR> メンタルプラクティスとは，実際の身体的運動を伴わず頭の中で繰り返し運動を想起する方法である．近年，メンタルプラクティスによる脳卒中片麻痺患者への治療効果が報告されており，随意的な運動が難しい患者に対して，身体的な負荷を増加することなく中枢レベルでの運動を反復できる有効な治療手段の1つと考えられている．しかしメンタルプラクティスが大脳皮質に及ぼす影響については報告されているが，近赤外線分光法においては十分に検討されていない．そこで本研究では近赤外線分光法を用いて脳血流量を測定し，随意運動とメンタルプラクティスにおける違いを検討した．<BR>【方法】<BR> 対象は本研究の内容を十分に説明し同意を得た健常成人9名（平均年齢29.9±5.7歳，男性7名，女性2名）であった．局所脳血流の測定には，近赤外線分光装置（島津製作所製FOIRE-3000）を用いた。プローブは国際10ー20基準電極法に基づく基準Czを中心に，C3，C4を含む20チャンネルを設置し，酸素化ヘモグロビンの変化をサンプリング周波数6.3Hzで記録した．被験者は座位にて閉眼し，肘掛に前腕を置き安楽な状態で計測した．実施課題は左手関節最大背屈運動とし，すべての被検者は随意運動と同様の運動のメンタルプラクティスを行なった．プロトコールは安静15秒，課題30秒，安静15秒の計60秒を1サイクルとして，それぞれ5回繰り返した．課題試行時には，音信号に合わせて3秒間の左手関節最大背屈位と3秒間の安静を繰り返し行なうように指示した．データ解析は，まず安静時と課題中の変化量の比較として，サイクル毎にそれぞれの酸素化ヘモグロビン変化量の平均値を算出した．次に課題間の比較を行った．安静時の酸素化ヘモグロビン変化量の平均値と標準偏差を基準として課題中の測定値を標準化した値を算出した．なおそれぞれデータの信号対雑音比が十分でないものは対象外とし，3－5サイクルのデータを加算平均し，さらに課題中の平均値を求めた．解析部位は，右側の一次運動野周辺とした．統計処理は対応のあるt検定を用い，有意水準は5％とした．<BR>【結果および考察】<BR> 随意運動及びメンタルプラクティスで，安静時と比較し酸素化ヘモグロビン変化量は有意に増加した．これはメンタルプラクティスにおいても一次運動野の脳血流量の増加を起こすことが可能であり，有効な治療手段であることを示唆していると考えられる．しかし随意運動は0.82±0.12，メンタルプラクティスは0.59±0.06で，両課題に有意差を認めた（p<0.05）．今回の課題で行った手関節背屈運動のような単純運動のメンタルプラクティスでは，随意運動と比較し十分な一次運動野における脳血流量の増加が起こらない可能性があると考えられる．今後，課題の選択および脳卒中片麻痺患者を対象とした検討を行っていきたい．<BR>

The visually induced corrective motor responses of hand position during reaching movements were investigated. Subjects performed reaching movements on a robotic manipulandum where the hand position was presented to the subjects by means of a projected display. On random reaching trials the projected hand position was perturbed relative to the actual hand position while the hand was constrained to the straight line to the target. Electromyographic activity of eight arm muscles were collected. A corrective muscular response starting at 150 ms from the onset of the visual perturbation was found. This response was found to be reflexive in nature and not suppressed by prior instruction. A second study found that the reflexive response was not modified by changes in the background muscle activity level or by the size of the perturbation. The results suggest that the visual system elicits simple motor reflexes in response to visual errors from the expected hand position.

The visually and mechanically induced corrective motor responses of hand position during reaching movements were investigated. Subjects performed reaching movements on a robotic manipulandum where the hand position was presented to the subjects by means of a projected display. On random reaching trials the subject's hand position was mechanically perturbed relative to the predicted hand trajectory. The visual representation of the hand position was either perturbed in the same direction as the hand, mirrored relative to the hand, or not perturbed at all. The visually induced reflexive responses were still elicited after a mechanical perturbation, whether or not the information agreed with the mechanical perturbation. The visually induced reflexes contributed to limb stiffness after 200 ms from the onset. If the visual and mechanical errors were consistent, the restoring force to a perturbation (or the effective stiffness) was increased at long latencies. The results suggest that on short time scales, error signals from different sensory modalities are processed separately, combined only at the output stage.

Language plays essential roles in human cognition and social communication, and therefore technology of reading out speech using non-invasively measured brain activity will have both scientific and clinical merits. Here, we examined whether it is possible to decode each syllable from human fMRI activity. Four healthy subjects participated in the experiments. In a decoding session, the subjects repeatedly uttered a syllable presented on a screen at 3Hz for a 12-s block. Nine different syllables are presented in a single experimental run which was repeated 8 times. We also specified the voxels which showed articulation-related activities by utterance of all the syllables in Japanese phonology in a conventional task-rest sequence. Then, we used either all of these voxels or a part of these voxels that exist in anatomically specified ROIs (M1, cerebellum) during decoding sessions as data samples for training and testing a decoder (linear support vector machine) that classifies brain activity patterns for different syllables. To evaluate decoding performance, we performed cross-validation by testing the sample of one decoding session using a decoder trained with the samples of the remaining sessions. As a result, syllables were correctly decoded at above-chance levels. The results suggest the possibility of using non-invasively measured brain activity to read out the intended speech of disabled patients in speech motor control.

Many evidences suggest that the central nervous system (CNS) acquires and switches internal models for adaptive control in various environments. However, little is known about the neural mechanisms responsible for the switching. A recent computational model for simultaneous learning and switching of internal models proposes two separate switching mechanisms: a predictive mechanism purely based on contextual information and a postdictive mechanism based on the difference between actual and predicted sensorimotor feedbacks. This model can switch internal models solely based on contextual information in a predictive fashion immediately after alteration of the environment. Here we show that when subjects simultaneously adapted to alternating blocks of opposing visuomotor rotations, explicit contextual information about the rotations improved the initial performance at block alternations and asymptotic levels of performance within each block but not readaptation speeds. Our simulations using separate switching mechanisms duplicated these effects of contextual information on subject performance and suggest that improvement of initial performance was caused by improved accuracy of the predictive switch while adaptation speed corresponds to a switch dependent on sensorimotor feedback. Simulations also suggested that a slow change in output signals from the switching mechanisms causes contamination of motor commands from an internal model used in the previous context (anterograde interference) and partial destruction of internal models (retrograde interference). Explicit contextual information prevents destruction and assists memory retention by improving the changes in output signals. Thus, the asymptotic levels of performance improved.

It has been shown that humans are able to selectively control the endpoint impedance of their arms when moving in an unstable environment. However, directional instability was only examined for the case in which the main contribution was from coactivation of biarticular muscles. The goal of this study was to examine whether, in general, the CNS activates the sets of muscles that contribute to selective control of impedance in particular directions. Subjects performed reaching movements in three differently oriented unstable environments generated by a robotic manipulandum. After subjects had learned to make relatively straight reaching movements in the unstable force field, the endpoint stiffness of the limb was measured at the midpoint of the movements. For each force field, the endpoint stiffness increased in a specific direction, whereas there was little change in stiffness in the orthogonal direction. The increase in stiffness was oriented along the direction of instability in the environment, which caused the major axis of the stiffness ellipse to rotate toward the instability in the environment. This study confirms that the CNS is able to control the endpoint impedance of the limbs and selectively adapt it to the environment. Furthermore, it supports the idea that the CNS incorporates an impedance controller that acts to ensure stability, reduce movement variability, and reduce metabolic cost.

Although human motor variability inevitably exists because of neural noises, it is not clear how humans can effectively reduce this variability for task accuracy. We examined the ability of humans to compensate for this variability during task-constraint reaching movements. The positional variance and muscle activations of reaching movements with task-constraints were compared with those without task-constraints. Flexor and extensor muscles were co-activated for the movements with task-constraints, and the positional variance was decreased to obtain accuracy. The results indicated that subjects were able to regulate their muscle impedance and to modulate the variability to meet the task requirements. © 2007 Elsevier B.V. All rights reserved.

Humans have exceptional abilities to learn new skills, manipulate tools and objects, and interact with our environment. In order to be successful at these tasks, our brain has become exceptionally well adapted to learning to deal not only with the complex dynamics of our own limbs but also with novel dynamics in the external world. While learning of these dynamics includes learning the complex time-varying forces at the end of limbs through the updating of internal models, it must also include learning the appropriate mechanical impedance in order to stabilize both the limb and any objects contacted in the environment. This article reviews the field of human learning by examining recent experimental evidence about adaptation to novel unstable dynamics and explores how this knowledge about the brain and neuro-muscular system can expand the learning capabilities of robotics and prosthetics. © 2006.

Movement variability plays a vital role in motor control. Although previous studies have examined the size and direction of the variability at end points, little research has examined how the variability changes during the time of move. The time course of the variability on point-to-point movements seems to be composed of two different properties: one increases monotonically and the other has an increasing-decreasing property. The first one can be explained by neural noise at the control level, such as the signal-dependent noise (SDN). However, how do we explain the latter one? Our numerical experiment hypothesized the time-jitter noise at trajectory planning level, which represents local advance or delay time of the reference trajectory for reaching movements. The simulation result could well reproduce the feature of the behavioral results. © 2006.

In this study, two different viscous force fields are generated by using a PFM (parallel link direct-drive air-magnet floating manipulandum), and the responding human learning process for multi-joint reaching motion is observed. In this experiment, the subject is given a certain cue for the force field. The cues are provided as follows: (I) audiovisual cue (a color and a peep sound corresponding to the force field, and a feather pattern representing the direction and magnitude of the force) and (2) movement cue (two trials are defined as a set, and the first movement is used as the cue). The task of the experiment is to learn the movement in force fields which are switched at random. Whichever cue (1) or (2) is used, there is a tendency that the area error from the desired straight trajectory decreases with an increasing number of trials. It is also observed that when the force field is eliminated at random after learning, a marked aftereffect arises in the group to which the audiovisual cue is given. The aftereffect is also observed in the group to which the movement cue is given, but is not as marked as in the group to which the audiovisual cue is given. The above results suggest that the internal models for two different viscous force fields can be acquired simultaneously without time separation by providing an appropriate cue to indicate the force field and learning in the environment in which the force field is switched at random. (c) 2006 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 89(7): 29-39,2006; Published online in Wiley InterScience (www.interscience.wiley.com).

In control, stability captures the reproducibility of motions and the robustness to environmental and internal perturbations. This paper examines how stability can be evaluated in human movements, and possible mechanisms by which humans ensure stability. First, a measure of stability is introduced, which is simple to apply to human movements and corresponds to Lyapunov exponents. Its application to real data shows that it is able to distinguish effectively between stable and unstable dynamics. A computational model is then used to investigate stability in human arm movements, which takes into account motor output variability and computes the force to perform a task according to an inverse dynamics model. Simulation results suggest that even a large time delay does not affect movement stability as long as the reflex feedback is small relative to muscle elasticity. Simulations are also used to demonstrate that existing learning schemes, using a monotonic antisymmetric update law, cannot compensate for unstable dynamics. An impedance compensation algorithm is introduced to learn unstable dynamics, which produces similar adaptation responses to those found in experiments.

It has been repeatedly demonstrated that the opening between the index finger and thumb (grasp component) during an object-directed reach-to-grasp movement achieves maximum aperture approximately two-thirds of the way through the duration of the reaching movement (transport component). Here we offer a quantitative model of the temporal coupling between grip aperture and wrist velocity which shows experimentally that the correlation between grip aperture and object size is a sigmoidal function of movement duration. When wrist velocity reaches its peak value, the correlation between the grip aperture and the size of the goal object has reached half of the correlation that is achieved by the end of the movement.

A minimum commanded torque change criterion based on the optimization principle is proposed as a model that accounts for human voluntary motion. It is shown that the trajectory of human arm motion can be well reproduced by the model. In the point-to-point movement, the calculation of the torque based on the minimum commanded torque change criterion requires a highly nonlinear calculation, and it is difficult to determine the optimal trajectory. As solution methods, a Newton-like method and a steepest descent method have been proposed. However, an optimal solution cannot be obtained by these methods, for several reasons. This paper proposes a method in which the trajectory of the joint angle is analytically represented by a system of orthogonal polynomials, and the coefficients of the orthogonal polynomials are estimated by a linear iterative calculation so that the parameters satisfy the EulerPoisson equation, as a necessary condition for the optimal solution. As a result of numerical experiments, it is shown that a solution satisfying the Euler-Poisson equation with high numerical accuracy is obtained in a short time, regardless of the parameters such as those of the boundary conditions. © 2005 Wiley Periodicals, Inc.

脳が手足を制御するときに解決すべき問題は、制御プログラムが手足のあるロボットを制御するとき解決すべき問題と類似している。計算論的神経科学では、このような視点から、脳に実装されていると思われる機構を同定する。これまでの研究により、例えば速くて正確な腕の運動を実現するためには、フィードフォワード制御が必要であることがわかってきた。このためには、制御対象(例えば腕)のダイナミクスを表現する内部モデルが脳内に獲得されていなければならない。リハビリテーションが必要な状態というのは、このような脳内の制御機構に何らかの異常を来したか、その制御対象である手足に異常を来したのかどちらかであることが多い。いずれの場合にも、速くて正確な運動を取り戻すには、内部モデルの再構築が必要である。これまでのリハビリテーション訓練は、フィードバック制御を必要とする運動が多く、内部モデルを再構築するには最適ではない可能性がある。このような視点からリハビリテーション手法を見直すことでよりよい機能回復がはかれる可能性がある。

The results of recent studies suggest that humans can form internal models that they use in a feedforward manner to compensate for both stable and unstable dynamics. To examine how internal models are formed, we performed adaptation experiments in novel dynamics, and measured the endpoint force, trajectory and EMG during learning. Analysis of reflex feedback and change of feedforward commands between consecutive trials suggested a unified model of motor learning, which can coherently unify the learning processes observed in stable and unstable dynamics and reproduce available data on motor learning. To our knowledge, this algorithm, based on the concurrent minimization of (reflex) feedback and muscle activation, is also the first nonlinear adaptive controller able to stabilize unstable dynamics.

An influential idea in human motor learning is that there is a consolidation period during which motor memories are transformed from a fragile to a permanent state, no longer susceptible to interference from new learning. The evidence supporting this idea comes from studies showing that the motor memory of a task ( A) is lost when an opposing task ( B) is experienced soon after, but not if sufficient time is allowed to pass (similar to6 hr). We report results from three laboratories challenging this consolidation idea. We used an ABA paradigm in the context of a reaching task to assess the influence of experiencing B after A on the retention of A. In two experiments using visuomotor rotations, we found that B fully interferes with the retention of A even when B is experienced 24 hr after A. Contrary to previous reports, in four experiments on learning force fields, we also observed full interference between A and B when they are separated by 24 hr or even 1 week. This latter result holds for both position-dependent and velocity-dependent force fields. For both the visuomotor and force-field tasks, complete interference is still observed when the possible affects of anterograde interference are controlled through the use of washout trials. Our results fail to support the idea that motor memories become consolidated into a protected state. Rather, they are consistent with recent ideas of memory formation, which propose that memories can shift between active and inactive states.

Rhythmic movements, such as walking, chewing or scratching, are phylogenetically old motor behaviors found in many organisms, ranging from insects to primates. In contrast, discrete movements, such as reaching, grasping or kicking, are behaviors that have reached sophistication primarily in younger species, particularly primates. Neurophysiological and computational research on arm motor control has focused almost exclusively on discrete movements, essentially assuming similar neural circuitry for rhythmic tasks. In contrast, many behavioral studies have focused on rhythmic models, subsuming discrete movement as a special case. Here, using a human functional neuroimaging experiment, we show that in addition to areas activated in rhythmic movement, discrete movement involves several higher cortical planning areas, even when both movement conditions are confined to the same single wrist joint. These results provide neuroscientific evidence that rhythmic arm movement cannot be part of a more general discrete movement system and may require separate neurophysiological and theoretical treatment.

There is an infinity of impedance parameter values, and thus different co-contraction levels, that can produce similar movement kinematics from which the CNS must select one. Although signal-dependent noise (SDN) predicts larger motor-command variability during higher co-contraction, the relationship between impedance and task performance is not theoretically obvious and thus was examined here. Subjects made goal-directed, single-joint elbow movements to either move naturally to different target sizes or voluntarily co-contract at different levels. Stiffness was estimated as the weighted summation of rectified EMG signals through the index of muscle co-contraction around the joint (IMCJ) proposed previously. When subjects made movements to targets of different sizes, IMCJ increased with the accuracy requirements, leading to reduced endpoint deviations. Therefore without the need for great accuracy, subjects accepted worse performance with lower co-contraction. When subjects were asked to increase co-contraction, the variability of EMG and torque both increased, suggesting that noise in the neuromotor command increased with muscle activation. In contrast, the final positional error was smallest for the highest IMCJ level. Although co-contraction increases the motor-command noise, the effect of this noise on the task performance is reduced. Subjects were able to regulate their impedance and control endpoint variance as the task requirements changed, and they did not voluntarily select the high impedance that generated the minimum endpoint error. These data contradict predictions of the SDN-based theory, which postulates minimization of only endpoint variance and thus require its revision.

Studies have shown that humans cannot simultaneously learn opposing force fields or opposing visuomotor rotations, even when provided with arbitrary contextual information, probably because of interference in their working memory(1-6). In contrast, we found that subjects can adapt to two opposing force fields when provided with contextual cues and can consolidate motor memories if random and frequent switching occurs. Because significant aftereffects were seen, this study suggests that multiple internal models can be acquired simultaneously during learning and predictively switched, depending only on contextual information.

The results of recent studies suggest that humans can form internal models that they use in a feedforward manner to compensate for both stable and unstable dynamics. To examine how internal models are formed, we performed adaptation experiments in novel dynamics, and measured the endpoint force, trajectory and EMG during learning. Analysis of reflex feedback and change of feedforward commands between consecutive trials suggested a unified model of motor learning, which can coherently unify the learning processes observed in stable and unstable dynamics and reproduce available data on motor learning. To our knowledge, this algorithm, based on the concurrent minimization of (reflex) feedback and muscle activation, is also the first nonlinear adaptive controller able to stabilize unstable dynamics.

To research the relation between joint impedance and signal dependent noise which opposes each other at the end-point error, we performed 2 types of experiments. Their results suggested that muscle activity increased as demanded accuracy increases, and the relation between duration and accuracy (Fitts' law) varied by conscious control of muscle activity.

Recently, we demonstrated that humans can learn to make accurate movements in an unstable environment by controlling magnitude, shape, and orientation of the endpoint impedance. Although previous studies of human motor learning suggest that the brain acquires an inverse dynamics model of the novel environment, it is not known whether this control mechanism is operative in unstable environments. We compared learning of multijoint arm movements in a "velocity-dependent force field" (VF), which interacted with the arm in a stable manner, and learning in a "divergent force field" (DF), where the interaction was unstable. The characteristics of error evolution were markedly different in the 2 fields. The direction of trajectory error in the DF alternated to the left and right during the early stage of learning; that is, signed error was inconsistent from movement to movement and could not have guided learning of an inverse dynamics model. This contrasted sharply with trajectory error in the VF, which was initially biased and decayed in a manner that was consistent with rapid feedback error learning. EMG recorded before and after learning in the DF and VF are also consistent with different learning and control mechanisms for adapting to stable and unstable dynamics, that is, inverse dynamics model formation and impedance control. We also investigated adaptation to a rotated DF to examine the interplay between inverse dynamics model formation and impedance control. Our results suggest that an inverse dynamics model can function in parallel with an impedance controller to compensate for consistent perturbing force in unstable environments.

This study compared adaptation in novel force fields where trajectories were initially either stable or unstable to elucidate the processes of learning novel skills and adapting to new environments. Subjects learned to move in a null force field (NF), which was unexpectedly changed either to a velocity-dependent force field (VF), which resulted in perturbed but stable hand trajectories, or a position-dependent divergent force field (DF), which resulted in unstable trajectories. With practice, subjects learned to compensate for the perturbations produced by both force fields. Adaptation was characterized by an initial increase in the activation of all muscles followed by a gradual reduction. The time course of the increase in activation was correlated with a reduction in hand-path error for the DF but not for the VF. Adaptation to the VF could have been achieved solely by formation of an inverse dynamics model and adaptation to the DF solely by impedance control. However, indices of learning, such as hand-path error, joint torque, and electromyographic activation and deactivation suggest that the CNS combined these processes during adaptation to both force fields. Our results suggest that during the early phase of learning there is an increase in endpoint stiffness that serves to reduce hand-path error and provides additional stability, regardless of whether the dynamics are stable or unstable. We suggest that the motor control system utilizes an inverse dynamics model to learn the mean dynamics and an impedance controller to assist in the formation of the inverse dynamics model and to generate needed stability.

This study compared the mechanisms of adaptation to stable and unstable dynamics from the perspective of changes in joint mechanics. Subjects were instructed to make point to point movements in force fields generated by a robotic manipulandum which interacted with the arm in either a stable or an unstable manner. After subjects adjusted to the initial disturbing effects of the force fields they were able to produce normal straight movements to the target. In the case of the stable interaction, subjects modified the joint torques in order to appropriately compensate for the force field. No change in joint torque or endpoint force was required or observed in the case of the unstable interaction. After adaptation, the endpoint stiffness of the arm was measured by applying displacements to the hand in eight different directions midway through the movements. This was compared to the stiffness measured similarly during movements in a null force field. After adaptation, the endpoint stiffness under both the stable and unstable dynamics was modified relative to the null field. Adaptation to unstable dynamics was achieved by selective modification of endpoint stiffness in the direction of the instability. To investigate whether the change in endpoint stiffness could be accounted for by change in joint torque or endpoint force, we estimated the change in stiffness on each trial based on the change in joint torque relative to the null field. For stable dynamics the change in endpoint stiffness was accurately predicted. However, for unstable dynamics the change in endpoint stiffness could not be reproduced. In fact, the predicted endpoint stiffness was similar to that in the null force field. Thus, the change in endpoint stiffness seen after adaptation to stable dynamics was directly related to changes in net joint torque necessary to compensate for the dynamics in contrast to adaptation to unstable dynamics, where a selective change in endpoint stiffness occurred without any modification of net joint torque.

We have studied the learning processes of reaching movements under novel environments whose kinematic and dynamic properties are altered. In the experiments, we have used, as the kinematic transformation, a rotational transformation which is displayed by rotating a cursor indicating hand position in the orthogonal coordinate system on a CRT
a viscous transformation using viscous field as the dynamic transformation
and a combined transformation of these two transformations. It is observed that the hand trajectory approaches a straight line along with learning and accurately reaches the target. When the combined transformation is learned after the rotational transformation and viscous transformation are learned first, respectively, the final error becomes smaller and the path length also becomes shorter than the case when the combined transformation is learned first. Moreover, the final error and path length of the movement under rotation al transformation and viscous transformation when the combined transformation is learned first also become smaller than the case when the rotational and viscous transformations are learned first. These results suggest that the central nervous system has learned separately the multiple internal models which compensate the respective transformations, and has composed or decomposed the respective internal models in accordance with the environmental changes. It may be considered that such multiplicity of internal models makes, it possible for the living body to flexibly cope with the environments or tools having various dynamic and kinematic properties. © 2002 Wiley Periodicals, Inc. Syst. Comp. Jpn., 33(11).

In the field of motor control, two hypotheses have been controversial: whether the brain acquires internal models that generate accurate motor commands, or whether the brain avoids this by using the viscoelasticity of musculoskeletal system. Recent observations on relatively low stiffness during trained movements support the existence of internal models. However, no study has revealed the decrease in viscoelasticity associated with learning that would imply improvement of internal models as well as synergy between the two hypothetical mechanisms. Previously observed decreases in electromyogram (EMG) might have other explanations, such as trajectory modifications that reduce joint torques. To circumvent such complications, we required strict trajectory control and examined only successful trials having identical trajectory and torque profiles. Subjects were asked to perform a hand movement in unison with a target moving along a specified and unusual trajectory, with shoulder and elbow in the horizontal plane at the shoulder level. To evaluate joint viscoelasticity during the learning of this movement, we proposed an index of muscle co-contraction around the joint (IMCJ). The IMCJ was defined as the summation of the absolute values of antagonistic muscle torques around the joint and computed from the linear relation between surface EMG and joint torque. The IMCJ during isometric contraction, as well as during movements, was confirmed to correlate well with joint stiffness estimated using the conventional method, i.e., applying mechanical perturbations. Accordingly, the IMCJ during the learning of the movement was computed for each joint of each trial using estimated EMG-torque relationship. At the same time, the performance error for each trial was specified as the root mean square of the distance between the target and hand at each time step over the entire trajectory. The time-series data of IMCJ and performance error were decomposed into long-term components that showed decreases in IMCJ in accordance with learning with little change in the trajectory and short-term interactions between the IMCJ and performance error. A cross-correlation analysis and impulse responses both suggested that higher IMCJs follow poor performances, and lower IMCJs follow good performances within a few successive trials. Our results support the hypothesis that viscoelasticity contributes more when internal models are inaccurate, while internal models contribute more after the completion of learning. It is demonstrated that the CNS regulates viscoelasticity on a short- and long-term basis depending on performance error and finally acquires smooth and accurate movements while maintaining stability during the entire learning process.

We used fMRI to identify the brain areas related to the perception of biological motion (4 T EPI; whole brain). In experiment 1, 10 subjects viewed biological motion (a human figure jumping up and down, composed of 21 dots), alternating with a control stimulus created by applying autoregressive models to the biological motion stimulus (such that the dots' speeds and amplitudes were preserved whereas their linking structure was not). The lengths of the stimulus bouts varied, and therefore the transitions between biological motion and control stimuli were unpredictable. Subjects had to indicate with a button press when each transition occurred. In a related biological motion task, subjects detected short (1 s) disturbances within these displays. We also examined the neural substrates of motion and shape perception, as well as motor imagery, to determine whether or not the cortical regions involved in these processes are also recruited during biological motion perception. Subjects viewed linear motion displays alternating with static dots and a series of common objects alternating with band-limited white noise patterns. Subjects also generated imagery of their own arm movements alternating with visual imagery of common objects. Biological motion specific BOLD signal was found within regions of the lingual gyrus at the cuneus border, showing little overlap with object recognition, linear motion or motion imagery areas. The lingual gyrus activation was replicated in a second experiment that also mapped retinotopic visual areas in three subjects. The results suggest that a region of the lingual gyrus within VP is involved in higher-order processing of motion information.

To manipulate objects or to use tools we must compensate for any forces arising from interaction with the physical environment. Recent studies indicate that this compensation is achieved by learning an internal model of the dynamics(1-6), that is, a neural representation of the relation between motor command and movement(5,7). In these studies interaction with the physical environment was stable, but many common tasks are intrinsically unstable(8,9). For example, keeping a screwdriver in the slot of a screw is unstable because excessive force parallel to the slot can cause the screwdriver to slip and because misdirected force can cause loss of contact between the screwdriver and the screw. Stability may be dependent on the control of mechanical impedance in the human arm because mechanical impedance can generate forces which resist destabilizing motion. Here we examined arm movements in an unstable dynamic environment created by a robotic interface. Our results show that humans learn to stabilize unstable dynamics using the skilful and energy-efficient strategy of selective control of impedance geometry.

In previous research, criteria based on optimal theories were examined to explain trajectory features in time and space in multi joint arm movement. Four criteria have been proposed. They were the minimum hand jerk criterion (by which a trajectory is planned in an extrinsic-kinematic space), the minimum angle jerk criterion (which is planned in an intrinsic-kinematic space), the minimum torque change criterion (where control objects are joint links; it is planned in an intrinsic-dynamic-mechanical space), and the minimum commanded torque change criterion (which is planned in an intrinsic space considering the arm and muscle dynamics). Which of these is proper as a criterion for trajectory planning in the central nervous system has been investigated by comparing predicted trajectories based on these criteria with previously measured trajectories. Optimal trajectories based on the two former criteria can be calculated analytically. In contrast, optimal trajectories based on the minimum commanded torque change criterion are difficult to be calculated, even with numerical methods. In some cases, they can be computed by a Newton-like method or a steepest descent method combined with a penalty method. However, for a realistic physical parameter range, the former becomes unstable quite often and the latter is unreliable about the optimality of the obtained solution.
In this paper, we propose a new method to stably calculate optimal trajectories based on the minimum commanded torque change criterion. The method can obtain trajectories satisfying Euler-Poisson equations with a sufficiently high accuracy. In the method, a joint angle trajectory, which satisfies the boundary conditions strictly, is expressed by using orthogonal polynomials. The coefficients of the orthogonal polynomials are estimated by using a linear iterative calculation so as to satisfy the Euler-Poisson equations with a sufficiently high accuracy. In numerical experiments, we show that the optimal solution can be computed in a wide work space and can also be obtained in a short lime compared with the previous methods.
Finally, we perform supplementary examinations of the experiments by Nakano, Imamizu, Osu, Uno, Gomi, Yoshioka et al. (1999). Estimation of dynamic joint torques and trajectory formation from surface electromyography signals using a neural network model. Biological Cybernetics, 73, 291-300. Their experiments showed that the measured trajectory is the closest to the minimum commanded torque change trajectory by statistical examination of many point-to-point trajectories over a wide range in a horizontal and sagittal work space. We recalculated the minimum commanded torque change trajectory using the proposed method, and performed the same examinations as previous investigations. As a result, it could be reconfirmed that the measured trajectory is closest to the minimum commanded torque change trajectory previously reported. (C) 2001 Elsevier Science Ltd. All rights reserved.

練習を繰り返すことにより運動技能は向上する.身体の柔軟性はこのときどのように変化しているのだろうか.表面筋電図を用いた方法により,学習に伴い剛性が低下すること,また失敗が直後の試行の剛性に影響を与えることがわかった.これは内部モデルを用いた予測的制御と剛性を用いたフィードバック制御がうまく組み合わされていることを示す.また,不安定な状況では,剛性を予測的,適応的に変化させて対応しており,生体がインピーダンスコントロールを実現していることがわかった.このように,神経系は外界を予測し,巧みに適応している.このような機能に注目したリハビリテーションの可能性を探ることも有用であろう.

In previous research, criteria based on optimal theories were examined to explain trajectory features in time and space in multi joint arm movements. Four criteria have been proposed. They were the minimum hand jerk criterion, the minimum angle jerk criterion, the minimum torque change criterion, and the minimum commanded torque change criterion. Optimal trajectories based on the two former criteria can be calculated analytically. In contrast, optimal trajectories based on the minimum commanded torque change criterion are difficult to be calculated even with numerical methods. In some cases, they can be computed by a Newton-like method or a steepest descent method combined with a penalty method. However, for a realistic physical parameter range, a former becomes unstable quite often, and the latter is unreliable about the optimality of the obtained solution. In this paper, we propose a new method to stably calculate optimal trajectories based on the minimum commanded torque change criterion. The method can obtain trajectories satisfying Euler-Poisson equations with a sufficiently high accuracy. In the method, a joint angle trajectory, which satisfies the boundary conditions strictly, is expressed by using orthogonal polynomials. The coefficients of the orthogonal polynomials are estimated by using a linear iterative calculation so as to satisfy the Euler-Poisson equations with a sufficiently high accuracy. In numerical experiments, we show that the optimal solution can be computed in a wide work space and can also be obtained in a short time compared with the previous methods.

Current methods for measuring stiffness during human arm movements are either limited to one-joint motions, or lead to systematic errors. The technique presented here enables a simple, accurate and unbiased measurement of endpoint stiffness during multi-joint movements. Using a computer-controlled mechanical interface, the hand is displaced relative to a prediction of the undisturbed trajectory. Stiffness is then computed as the ratio of restoring force to displacement amplitude. Because of the accuracy of the prediction ( &lt; 1 cm error after 200 ms) and the quality of the implementation, the movement is not disrupted by the perturbation. This technique requires only 1/3 as many trials to identify stiffness as the method of Gomi and Kawato (1997, Biological Cybernetics 76, 163-171) and may, therefore, be used to investigate the evolution of stiffness during motor adaptation. (C) 2000 Elsevier Science Ltd. All rights reserved.

In multi-joint planar reaching movement, hand trajectories tend to be gently curved depending on the hand position in extracorporal space. Following two explanations are possible for the observed curvature; (i) planned trajectories themselves are curved, (ii) planned trajectories are straight but realized trajectories are curved because of the reasons other than planning. We examined one of the possible reasons that belongs to the second explanation. That is, the realized trajectories are curved because the internal model in the central nervous system is imperfect. We represented imperfect internal model by (1) parametoric method, (2) neural metwork method (3) table look up method, and compared the trajectory predicted by imperfect internal model with the measured trajectories. The results suggest that the imperfect internal model cannot explain the observed curvature better than the minimum commanded torque change model that plans curved trajectories.

While biological principles have inspired researchers in computational and engineering research for a long time, there is still rather limited knowledge flow back from computational to biological domains. This paper presents examples of our work where research on anthropomorphic robots lead us to new insights into explaining biological movement phenomena, starting from behavioral studies up to brain imaging studies. Our research over the past years has focused on principles of trajectory formation with nonlinear dynamical systems, on learning internal models for nonlinear control, and on advanced topics like imitation learning. The formal and empirical analyses of the kinematics and dynamics of movements systems and the tasks that they need to perform lead us to suggest principles of motor control that later on we found surprisingly related to human behavior and even brain activity.

The technique presented in this paper enables a simple, accurate and unbiased measurement of hand stiffness during human arm movements. Using a computer-controlled mechanical interface, the hand is shifted relative to a prediction of the undisturbed trajectory. Stiffness is then computed as the restoring force divided by the position amplitude of the perturbation. A precise prediction algorithm insures the measurement quality. We used this technique to measure stiffness in free movements and after adaptation to a linear velocity dependent force field. The subjects compensated for the external force by co-contracting muscles selectively. The stiffness geometry changed with learning and stiffness tended to increase in the direction of the external force.

A number of invariant features of multijoint planar reaching movements have been observed in measured hand trajectories. These features include roughly straight hand paths and bell-shaped speed profiles where the trajectory curvatures between transverse and radial movements have been found to be different. For quantitative and statistical investigations, we obtained a large amount of trajectory data within a wide range of the workspace in the horizontal and sagittal planes (400 trajectories for each subject). A pair of movements within the horizontal and sagittal planes was set to be equivalent in the elbow and shoulder flexion/extension. The trajectory curvatures of the corresponding pair in these planes were almost the same. Moreover, these curvatures can be accurately reproduced with a linear regression from the summation of rotations in the elbow and shoulder joints. This means that trajectory curvatures systematically depend on the movement location and direction represented in the intrinsic body coordinates. We then examined the following four candidates as planning spaces and the four corresponding computational models for trajectory planning. The candidates were as follows: the minimum hand jerk model in an extrinsic-kinematic space, the minimum angle jerk model in an intrinsic-kinematic space, the minimum torque change model in an intrinsic-dynamic-mechanical space, and the minimum commanded torque change model in an intrinsic-dynamic-neural space. The minimum commanded torque change model, which is proposed here as a computable version of the minimum motor command change model, reproduced actual trajectories best for curvature, position, velocity, acceleration, and torque. The model's prediction that the longer the duration of the movement the larger the trajectory curvature was also confirmed. Movements passing through via- points in the horizontal plane were also measured, and they converged to those predicted by the minimum commanded torque change model with training. Our results indicated that the brain may plan, and learn to plan, the optimal trajectory in the intrinsic coordinates considering arm and muscle dynamics and using representations for motor commands controlling muscle tensions.

The learning process of reaching movements was examined under novel environments whose kinematic and dynamic properties were altered. We used a kinematic transformation (visuomotor rotation), a dynamic transformation (viscous curl field), and a combination of these transformations. When the subjects learned the combined transformation, reaching errors were smaller if the subject first learned the separate kinematic and dynamic transformations. Reaching errors under the kinematic (but not the dynamic) transformation were smaller if subjects first learned the combined transformation. These results suggest that the brain learns multiple internal models to compensate for each transformation and has some ability to combine and decompose these internal models as called for by the occasion.

Stiffness properties of the musculoskeletal system can be controlled by regulating muscle activation and neural feedback gain. To understand the regulation of multijoint stiffness, we examined the relationship between human arm joint stiffness and muscle activation during static force control in the horizontal plane by means of surface electromyographic (EMG) studies. Subjects were asked to produce a specified force in a specified direction without cocontraction or they were asked to keep different cocontractions while producing or not producing an external force. The stiffness components of shoulder, elbow, and their cross-term and the EMG of six related muscles were measured during the tasks. Assuming that the EMG reflects the corresponding muscle stiffness, the joint stiffness was predicted from the EMG by using a two-link six-muscle arm model and a constrained least-square- error regression method. Using the parameters estimated in this regression, single-joint stiffness (diagonal terms of the joint-stiffness matrix) was decomposed successfully into biarticular and monoarticular muscle components. Although biarticular muscles act on both shoulder and elbow, they were found to covary strongly with elbow monoarticular muscles. The preferred force directions of biarticular muscles were biased to the directions of elbow monoarticular muscles. Namely, the elbow joint is regulated by the simultaneous activation of monoarticular and biarticular muscles, whereas the shoulder joint is regulated dominantly by monoarticular muscles. These results suggest that biarticular muscles are innervated mainly to control the elbow joint during static force-regulation tasks. In addition, muscle regulation mechanisms for static force control tasks were found to be quite different from those during movements previously reported. The elbow single- joint stiffness was always higher than cross-joint stiffness (off-diagonal terms of the matrix) in static tasks while elbow single-joint stiffness is reported to be sometimes as small as cross-joint stiffness during movement. That is, during movements, the elbow monoarticular muscles were occasionally not activated when biarticular muscles were activated. In static tasks, however, monoarticular muscle components in single-joint stiffness were increased considerably whenever biarticular muscle components in single- and cross-joint stiffness increased. These observations suggest that biarticular muscles are not simply coupled with the innervation of elbow monoarticular muscles but also are regulated independently according to the required task. During static force-regulation tasks, covariation between biarticular and elbow monoarticular muscles may be required to increase stability and/or controllability or to distribute effort among the appropriate muscles.

Human arm viscoelasticity is important in stabilizing posture, movement, and in interacting with objects. Viscoelastic spatial characteristics are usually indexed by the size, shape, and orientation of a hand stiffness ellipse. It is well known that arm posture is a dominant factor in determining the properties of the stiffness ellipse. However, it is still unclear how much joint stiffness can change under different conditions, and the effects of that change on the spatial characteristics of hand stiffness are poorly examined. To investigate the dexterous control mechanisms of the human arm, we studied the controllability and spatial characteristics of viscoelastic properties of human multijoint arm during different cocontractions and force interactions in various directions and amplitudes in a horizontal plane. We found that different cocontraction ratios between shoulder and elbow joints can produce changes in the shape and orientation of the stiffness ellipse, especially at proximal hand positions. During force regulation tasks we found that shoulder and elbow single-joint stiffness was each roughly proportional to the torque of its own joint, and cross-joint stiffness was correlated with elbow torque. Similar tendencies were also found in the viscosity-torque relationships. As a result of the joint stiffness changes, the orientation and shape of the stiffness ellipses varied during force regulation tasks as well. Based on these observations, we consider why we can change the ellipse characteristics especially in the proximal posture. The present results suggest that humans control directional characteristics of hand stiffness by changing joint stiffness to achieve various interactions with objects.

Although the straightness of hand paths is a widely accepted feature of human multijoint reaching movement, detailed examinations have revealed slight curvatures in some regions of the workspace. This observation raises the question of whether planned trajectories are straight or curved. If they are straight, 3 possible factors can explain the observed curvatures: (a) imperfect control, (b) visual distortion, or (c) interaction between straight virtual trajectories and the dynamics of the arm. Participants instructed to generate straight movement paths produced movements much straighter than those generated spontaneously. Participants generated spontaneously curved trajectories in the frontoparallel plane, where visual distortion is not expected. Electromyograms suggested that participants generated straighter paths without an increase in arm stiffness. These findings argue against the 3 factors. It follows that planned trajectories are likely to be curved.

To investigate tile motion control mechanism of human arm during force control, shoulder, elbow, and double-joint stiffness were measured by applying a small perturbation, and their contributions to joint torques were estimated, Each joint stiffness greatly altered for the different force direction at hand, and shoulder and elbow single joint stiffness were linearly correlated to each joint torques, ny assuming a linear joint stiffness, generated torques were decomposed into torques produced by each stiffness component. This analysis has revealed that the elbow torque was produced complementary by the single and double-joint stiffness, and that the shoulder torque was mainly produced by the shoulder single joint stiffness, Additionally, the double-joint stiffness was linearly correlated to tile decomposed elbow joint torques produced by double-joint stiffness, These results suggest that shoulder and elbow torques are separately controlled by different muscles in this task.

A general theory of movement-pattern perception based on bi-directional theory for sensory-motor integration can be used for motion capture and learning by watching in robotics. We demonstrate our methods using the game of Kendama, executed by the SARCOS Dextrous Slave Arm, which has a very similar kinematic structure to the human arm. Three ingredients have to be integrated for the successful execution of this task. The ingredients are (1) to extract via-points from a human movement trajectory using a forward-inverse relaxation model, (2) to treat via-points as a control variable while reconstructing the desired trajectory from all the via-points, and (3) to modify the via-points for successful execution. In order to test the validity of the via-point representation, we utilized a numerical model of the SARCOS arm, and examined the behavior of the system under several conditions. Copyright (C) 1996 Elsevier Science Ltd.

In multi-joint movements, possible trajectories for a given target are infinite, but actually have certain invariant features. It has been discussed whether trajectories of the human arm are planned in an extrinsic space or in an intrinsic space. Hand paths planned in the former are predicted to be always straight, while those in the latter are generally curved. Both Uno et al. and Osu et al. reported that actual hand paths tended to significantly curve for some specific arm postures, movement distances, and movement durations. We have extended the previous studies by using various initial and final positions located within a workspace and examined if the curvature of a trajectory quantitatively varies with arm posture when subjects make point to point reaching movements on a horizontal plane. Curvatures of measured hand trajectories were linearly estimated using two models, hand position and hand translation, which are represented by extrinsic coordinates, and other two models, joint angle and joint rotation, which are represented by intrinsic coordinates. In experiment I and II, movement durations were restricted, and in experiment III, movement durations were flexible and added to parameters. Movement durations and joint rotation significantly contributed to curvature. We succeeded in predicting the curvature of hand paths by using the arm posture before and after a movement. The results suggest that trajectory curvature depends on arm posture and is in accordance with predictions made under planning in the intrinsic space, rather than that in the extrinsic space. Furthermore, the result that a longer movement duration causes a larger curvature is in agreement with the predictions of Uno and Kawato, in which a longer movement duration makes paths expand toward the outer side because of an effectively larger viscosity ratio.

Various optimal criteria have been proposed for trajectory planning in multi-joint arm movements. The minimumjerk criterion plans smooth trajectories in the extrinsic task space. The minimum-joint-angle-jerk criterion, the minimum-torque-change criterion, and the minimum-motor-command-change criterion plan smooth trajectories in the intrinsic body space. Assuming that realized trajectories reflect planned trajectories, we compared the values of above four optimal criteria calculated from observed movement data. If the value of a certain criterion is larger in a spontaneously generated movement than in some other movement, that criterion can be rejected. If, however, the value of a certain criterion is smaller in a spontaneously generated movement than in any other movement, it supports that criterion. Subjects were instructed to move their hand to a target passing through a via-point. Several via-points were given randomly to make subjects generate hand paths with various curvatures. The curvatures of the paths that have minimum values of a certain criterion are compared to curvatures of the spontaneously generated paths. The values of hand-jerk and joint-angle-jerk were obtained from measured position data. The values of torque-change were obtained using the dynamics equation of a two-joint arm model with estimated physical parameters. The values of motor-command-change were obtained from quasi-tension calculated from rectified EMG using a second-order low-pass-filter. The minimum-jerk criterion was larger in spontaneously curved movements than in movements with straighter hand paths. This result rejects the minimum-hand-jerk criterion. However, joint angle jerk was not always minimum around the hand paths predicted by the minimum-joint-angle-jerk criterion. Subjects tend to generate trajectories that have lower values of minimum-motor-command-change criterion. © 1996, Japanese Society for Medical and Biological Engineering. All rights reserved.

In multi-joint movements, possible trajectories for a given target are infinite, but actually have certain invariant features. It has been discussed whether trajectories of the human arm are planned in an extrinsic space or in an intrinsic space. Hand paths planned in the former are predicted to be always straight, while those in the latter are generally curved. Both Uno et al. and Osu et al. reported that actual hand paths tended to significantly curve for some specific arm postures, movement distances, and movement durations. We have extended the previous studies by using various initial and final positions located within a workspace and examined if the curvature of a trajectory quantitatively varies with arm posture when subjects make point to point reaching movements on a horizontal plane. Curvatures of measured hand trajectories were linearly estimated using two models, hand position and hand translation, which are represented by extrinsic coordinates, and other two models, joint angle and joint rotation, which are represented by intrinsic coordinates. In experiment I and II, movement durations were restricted, and in experiment III, movement durations were flexible and added to parameters. Movement durations and joint rotation significantly contributed to curvature. We succeeded in predicting the curvature of hand paths by using the arm posture before and after a movement. The results suggest that trajectory curvature depends on arm posture and is in accordance with predictions made under planning in the intrinsic space, rather than that in the extrinsic space. Furthermore, the result that a longer movement duration causes a larger curvature is in agreement with the predictions of Uno and Kawato, in which a longer movement duration makes paths expand toward the outer side because of an effectively larger viscosity ratio. © 1996, Japanese Society for Medical and Biological Engineering. All rights reserved.

Various optimal criteria have been proposed for trajectory planning in multi-joint arm movements. The minimumjerk criterion plans smooth trajectories in the extrinsic task space. The minimum-joint-angle-jerk criterion, the minimum-torque-change criterion, and the minimum-motor-command-change criterion plan smooth trajectories in the intrinsic body space. Assuming that realized trajectories reflect planned trajectories, we compared the values of above four optimal criteria calculated from observed movement data. If the value of a certain criterion is larger in a spontaneously generated movement than in some other movement, that criterion can be rejected. If, however, the value of a certain criterion is smaller in a spontaneously generated movement than in any other movement, it supports that criterion. Subjects were instructed to move their hand to a target passing through a via-point. Several via-points were given randomly to make subjects generate hand paths with various curvatures. The curvatures of the paths that have minimum values of a certain criterion are compared to curvatures of the spontaneously generated paths. The values of hand-jerk and joint-angle-jerk were obtained from measured position data. The values of torque-change were obtained using the dynamics equation of a two-joint arm model with estimated physical parameters. The values of motor-command-change were obtained from quasi-tension calculated from rectified EMG using a second-order low-pass-filter. The minimum-jerk criterion was larger in spontaneously curved movements than in movements with straighter hand paths. This result rejects the minimum-hand-jerk criterion. However, joint angle jerk was not always minimum around the hand paths predicted by the minimum-joint-angle-jerk criterion. Subjects tend to generate trajectories that have lower values of minimum-motor-command-change criterion. © 1996, Japanese Society for Medical and Biological Engineering. All rights reserved.