BACKGROUND: Training physiotherapists require substantial experience and a lengthy period of time to achieve proficiency. However, establishing an objective method for quantifying the degree of force applied during treatment remains elusive, making training difficult. OBJECTIVES: This study aims to clarify the difference in the degree of force application between novice and expert physiotherapists using muscle deformation sensors and to assist in teaching. METHODS: A muscle deformation sensor array was utilized to capture the muscle bulging (muscle deformation), and the degree of force was visualized. The experiment involved two types of physiotherapy: upper and lower extremity exercises. Subsequently, the muscle deformation value and standard deviations of the muscle deformation data obtained were compared. RESULTS: Significant differences between novices and experts were observed in forearm muscle deformation values and standard deviations across both types of physiotherapies (p<0.05). Additionally, a distinction was observed in the left lower limb flexor muscles during upper extremity exercise (p<0.05). CONCLUSION: The results of this survey showed notable differences in the degree of force application between novices and experts, as demonstrated by our findings. Moreover, these implications extend beyond physiotherapy to sports, hobbies, and the teaching of traditional skills.

Hand-gripping training is important for improving the fundamental functions of human physical activity. Bernstein’s idea of “repetition without repetition” suggests that motor control function should be trained under changing states. The randomness level of load should be visualized for self-administered screening when repeating various training tasks under changing states. This study aims to develop a sensing methodology of random loads applied to both the agonist and antagonist skeletal muscles when performing physical tasks. We assumed that the time-variability and periodicity of the applied load appear in the time-series feature of muscle deformation data. In the experiment, 14 participants conducted the gripping tasks with a gripper, ball, balloon, Palm clenching, and paper. Crumpling pieces of paper (paper exercise) involves randomness because the resistance force of the paper changes depending on the shape and layers of the paper. Optical myography during gripping tasks was measured, and time-series features were analyzed. As a result, our system could detect the random movement of muscles during training.

The COVID-19 pandemic impacted collaborative activities, travel, and physical contact, increasing the demand for real-time interactions with remote environments. However, the existing remote communication solutions provide limited interactions and do not convey a high sense of presence within a remote environment. Therefore, we propose a snake-shaped wearable telexistence robot, called Piton, that can be remotely used for a variety of collaborative applications. To the best of our knowledge, Piton is the first snake-shaped wearable telexistence robot. We explain the implementation of Piton, its control architecture, and discuss how Piton can be deployed in a variety of contexts. We implemented three control methods to control Piton: HM—using a head-mounted display (HMD), HH—using an HMD and hand-held tracker, and FM—using an HMD and a foot-mounted tracker. We conducted a user study to investigate the applicability of the proposed control methods for telexistence, focusing on body ownership (Alpha IVBO), mental and physical load (NASA-TLX), motion sickness (VRSQ), and a questionnaire to measure user impressions. The results show that both the HM and HH provide relevantly high levels of body ownership, had high perceived accuracy, and were highly favored, whereas the FM control method yielded the lowest body ownership effect and was least favored. We discuss the results and highlight the advantages and shortcomings of the control methods with respect to various potential application contexts. Based on our design and evaluation of Piton, we extracted a number of insights and future research directions to deepen our investigation and realization of wearable telexistence robots.

The goal of the study was to develop a system that can adjust the electrical muscle stimulation parameters for individuals when sharing experiences with stimulation by sensing the degree of muscle contraction during electrical stimulation. If we do not know the appropriate amount of current for stimulation for an individual, the muscles would not contract as we aimed, and we will not be able to share the experience as we expected. In this study, we presented a system estimating fingertip force as the output of electrical muscle stimulation by monitoring the muscle state based on infrared optical sensing for adjusting electrical muscle stimulation parameters for the individual. We developed a regression model based on support vector regression during electrical stimulation using an infrared optical sensor with seven people's data to estimate the pushing force. The coefficient of determination between the measured pushing force and estimated pushing force was greater than 0.8 and 0.9 for the index and middle fingers, respectively. The system can monitor a feedback value of electrical muscle stimulation fingertip control. The system showed the feasibility of infrared optical sensing for the closed-loop feedback control system of the electrical stimulation parameters for an individual.

Vision plays an important role in motion planning for mobile robots which coexist with humans. Because a method predicting a pedestrian path with a camera has a trade-off relationship between the calculation speed and accuracy, such a path prediction method is not good at instantaneously detecting multiple people at a distance. In this study, we thus present a method with visual recognition and prediction of transition of human action states to assess the risk of collision for selecting the avoidance target. The proposed system calculates the risk assessment score based on recognition of human body direction, human walking patterns with an object, and face orientation as well as prediction of transition of human action states. First, we investigated the validation of each recognition model, and we confirmed that the proposed system can recognize and predict human actions with high accuracy ahead of 3 m. Then, we compared the risk assessment score with video interviews to ask a human whom a mobile robot should pay attention to, and we found that the proposed system could capture the features of human states that people pay attention to when avoiding collision with other people from vision.

The minimum toe clearance (MTC) results from the coordination of all bilateral lower limb body segments, i.e., a redundant kinematic chain. We tested the hypothesis that repeated exposure to trip-like perturbations induces a more effective covariation of limb segments during steady walking, in accordance with the uncontrolled manifold (UCM) theory, to minimize the MTC across strides. Twelve healthy young adults (mean age 26.2 ± 3.3 years) were enrolled. The experimental protocol consisted of three identical trials, each involving three phases carried outin succession: steady walking (baseline), managing trip-like perturbations, and steady walking (post-perturbation). Lower limb kinematics collected during both steady walking phases wereanalyzed in the framework of the UCM theory to test the hypothesis that the reduced MTC variability following the perturbation can occur, in conjunction with more effective organization of the redundant lower limb segments. Results revealed that, after the perturbation, the synergy underlying lower limb coordination becomes stronger. Accordingly, the short-term effects of the repeated exposure to perturbations modify the organization of the redundant lower limb-related movements. In addition, results confirm that the UCM theory is a promising tool for exploring the effectiveness of interventions aimed at purposely modifying motor behaviors.

Controlling minimum toe clearance (MTC) is considered an important factor in preventing tripping. In the current study, we investigated modifications of neuro-muscular control underlying toe clearance during steady locomotion induced by repeated exposure to tripping-like perturbations of the right swing foot. Fourteen healthy young adults (mean age 26.4 ± 3.1 years) participated in the study. The experimental protocol consisted of three identical trials, each involving three phases: steady walking (baseline), perturbation, and steady walking (post-perturbation). During the perturbation, participants experienced 30 tripping-like perturbations at unexpected timing delivered by a custom-made mechatronic perturbation device. The temporal parameters (cadence and stance phase ), mean, and standard deviation of MTC were computed across approximately 90 strides collected during both baseline and post-perturbation phases, for all trials. The effects of trial (three levels), phase (two levels: baseline and post-perturbation) and foot (two levels: right and left) on the outcome variables were analyzed using a three-way repeated measures analysis of variance. The results revealed that exposure to repeated trip-like perturbations modified MTC toward more precise control and lower toe clearance of the swinging foot, which appeared to reflect both the expectation of potential forthcoming perturbations and a quicker compensatory response in cases of a lack of balance. Moreover, locomotion control enabled subjects to maintain symmetric rhythmic features during post-perturbation steady walking. Finally, the effects of exposure to perturbation quickly disappeared among consecutive trials. %

Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.

With the increasing use of backpacks on a daily basis, appropriate assessment of shoulder load, which has adverse effects on the body, has become more important. We focused on nociceptive pain, which is a physiological warning signal, and performed a subjective evaluation of loading conditions. In this study, we investigated the relationship between multi-point mechanical stimuli set at38 measuring points on the shoulder, and overall pain. In the experiment, eight subjects rated their pain levels at 24 loading conditions (combinations of 3 weight, 2 weight-distance, 2 weight-height, and 2 padding conditions) using a pain scale. In the statistical analysis, the overall pain intensities at different loading conditions were compared through ANOVA, and weight and distance from body were confirmed as main contributing factors. In the regression analysis, four different models were used to fit the overall data. A generalized linear model (GLM) with polynomial sigmoid function resulted in the best fit. GLM fitting was also performed on the data after these have been divided into 8 groups based on combinations of distance-height-padding. The independent variables, the selected combinations of loads at the measuring points, differed depending on the loading conditions. For more accurate regression, loads that contribute to the determination of overall pain intensity should be appropriately selected according to the loading conditions. These results can be used to comprehensively evaluate backpack design based on shoulder pain.

When using a backpack, proper shoulder load reduction is required. We focused on pain (nociceptive pain), which is a warning signal to protect the human body, and we aimed to extract the shoulder parts to avoid heavy loads while walking with a backpack. We set 19 measuring points on each shoulder and 12 measuring points on the lower back. Using three-axis tactile sensors, we then recorded the interface load on the shoulders and lower back under two back panel conditions: general flat panel and panel with lumbar pad. With 180 data for mean load and 150 data for peak load on each measuring point, we confirmed the load distribution and load shift effects using a lumbar pad by comparing the shoulder load and the lower back load. Then, the shoulder load data was normalized by the pain threshold for a single-point pressure stimulus at each measuring point of the subject. The pain threshold was estimated by an approximate expression with a sigmoid function for pain scores, which were collected by subjective evaluation with a pain scale. In statistical analysis, through multiple comparisons (Steel-Dwass test) for the mean values of the normalized shoulder load on each measuring point and its mean value of the entire shoulder, we extracted seven potential high-risk points (coracoid process, medial and lateral part of the clavicle regions, medial and lateral part of the ridgeline of the shoulder, and supraspinatus). Moreover, we observed that high-risk loads remained locally behind a significant reduction of the entire shoulder load with a lumbar pad. These results can be used to improve backpack design for proper loads on the shoulder.

An ability of visually-guided and anticipatory adjustments of locomotion corresponding to upcoming obstacles is important to avoid trip-induced fall. For establishing gait training based on visually-guided and anticipatory adjustments, techniques reproducing realistic training environment are essential. Although some previous works proposed virtual obstacles using mixed reality, the feasibility of virtual obstacles encouraging people to perform realistic obstacle negotiation on a treadmill, where gait training is usually conducted, is still unclear. In this study, we investigated toe heights when stepping over the obstacle in both cases of virtual and real obstacles during walking on the treadmill. Five participants stepped over two types of mixed reality boxes and real boxes, with box placements close and distant from them. The results generally indicate that the toe heights of the leading foot tended to be similar between mixed reality and real obstacles in cases where the obstacle was located distant from participants, a condition that enabled participants to predict when obstacles approached. However, the toe heights of the trailing foot tended to be lower when stepping over the MR obstacles than the real obstacles. We discuss the feasibility and shortcomings of the future use of MR HMDs as replacement for traditional gait training setup.

Nowadays, the use of electric vehicles is increasing leading to a growing demand for more efficient use of lithium-ion batteries. The state-of-charge (SOC) has been estimated in previous studies to optimize energy management of batteries. For more efficient battery utilization, detecting degradation is important. However, it is difficult for conventional methods to distinguish the effect of the model parameters including different time constants. Identifying model parameters of multiple RC parallel branches, which represent the impedance of wider frequency ranges, is a necessary requirement to detect the degradation of parts. In this study, we present a method for estimating the model parameters of multiple RC parallel branches. We designed the Markov Chain Monte Carlo algorithm by setting a search range limit and moving window, which enable estimation of the model parameters of parallel branches of different time constants. Through validation of the algorithm based on simulation, the model parameters of a third-order circuit were estimated to be within the error range of 15.2 %. In addition, impedance was calculated from the estimated model parameters in the test using a real battery dataset. The error of impedance was less than 10 % from 0.01 to 100 Hz which was sufficiently low to monitor the change of the parameters owing to degradation. As the impedance in the high-frequency band above 0.1 Hz is more likely to change because of degradation, the proposed method can be used to monitor the model parameters that change as a result of degradation.

With the increasing use of backpacks in recent years, proper reduction of shoulder load has become essential. We focused on nociceptive pain, which is a physiological warning signal, and performed subjective evaluation as an index to determine appropriate loading. In this paper, we investigated the relationship between a single-point pressure stimulus on the shoulder and pain and characterized pain sensitivity for each region of the shoulder. In the experiment, seven subjects rated their pain level upon stimulation using a pain scale. In data analysis, the rating scores were revised to an equivalence scale, and the relation between the pain score and stimulus intensity was approximated by a sigmoid function. Moreover, pain thresholds were estimated by approximate expression and classified into four sensitivity levels after normalizing. Combining the measurement points, we proposed characterization mapping of pain sensitivity and showed the pain sensitivity level at each measurement point. The results of pain rating for the strongest stimulus in the experiment, characterization mapping, and the results of pain sensitivity at each measurement point were anatomically matched. As such, we clarified the shoulder regions that can actively support load and those that should avoid loading. These results can be used to evaluate and improve backpack strap design.

Adaptive assistance of gait training robots has been determined to improve gait performance through motion assistance. An important control role during walking is to avoid tripping by controlling minimum toe clearance (MTC), which is an indicator of tripping risk, to avoid its decrease among gait cycles. No conventional gait training robots can adjust assistance timing based on MTC. In this paper, we propose a system that applies force intermittently based on the MTC prediction algorithm to encourage people to avoid lowering the MTC. This prediction algorithm is based on a radial basis function network, the input data of which include the angles, angular velocities, and angular accelerations of the hip, knee, and ankle joints in the sagittal and coronal planes at toe-off. The cable-driven system that can switch between assistance and non-assistance modes applies force when the predicted MTC is lower than the mean value. Nine participants were asked to walk on a treadmill, and we tested the effect of the system. The MTC data before, during, and after the assistance phase were analyzed for 120 s. The results showed that the minimum and first quartile values of MTC could be increased after the assistance phase.

The detection of gait events with wearable sensors is necessary for a robotic system interacting with walking people. Conventional gait phase detection methods are based on machine learning. However, this method cannot detect a gait event every gait cycle because it is difficult to extract characteristic points. Additionally, using only angular information for detection is beneficial because angular information is needed for the control and evaluation of the robots. This paper proposes a novel algorithm for the detection of heel contact and toe-off using the inter-joint coordination of the hip, knee, and ankle joints that has a lower-dimensional structure. The proposed algorithm derives the four planes in the angular space and finds the switching points of the planes. Seven participants walked on force plates that measured the force of the foot against the floor. The error was less than 0.035 s when the gait events were detected after calculating planes using the first gait datum. The change in the patterns of the inter-joint coordination reflected the change in gait phases. Although the data were calculated offline, the results show that the heel contact and toe-off could be detected as soon as the angles were sensed once the planes were derived.

Gait phase detection and quantitative evaluation are significant for synchronous robotic assistance of human walking, rehabilitation training, or diagnosis of human motion state. Especially, accurate heel-contact detection in a gait cycle is a key requirement for gait analysis applications. Some techniques have been proposed by utilizing wearable devices, however, existing systems typically require precise and continuous time-series data at every single timestep for calibration, which largely increases the burden to users. Therefore, we propose a novel posing-based detection method through measuring muscle deformation, which only requires arbitrary and discrete posture data for calibration without walking. In this study, we firstly collected the posing data as the training set and gait data as the test set from participants through a FirstVR device. Then the Support Vector Machine was trained to be a two-class classifier of heel-contact and non-heel-contact phases by using the collected muscle deformation data during posing. Finally we propose an efficient evaluation system by taking advantage of OpenPose to automatically label our continuous gait data. Experimental results demonstrate the muscle deformation sensor could correctly detect heel-contact with approximately 80% accuracy during walking, which shows the feasibility of posing-based method with muscle deformation information for heel-contact detection.

Although conventional service providers are independent from each other when attending most of the population, demanded services are changing along with the social structure. Especially in the case of Taiwan, the number of co-working families has been increasing, and self-employed households occupy a large proportion of all working forms. Due to their diverse lifestyles and work styles, services that are suitable for personal objectives and that optimize the use of time are required. To meet this demand, it is important to connect people and city facilities to make it easier to provide suitable services. Based on those backgrounds, an innovated personal service platform in Taiwan is proposed, focusing on three factors, including time, place and personal information to connect people and city service facilities. Among various kinds of services, we targets services purchased in cities such as sales, mobility services, health services, government services and so on. It aims to link these services flexibly and dynamically to achieve personal objectives according to each situation. And, it can provide suitable services for a variety of every-day living situations. With this system, people can increase satisfaction and free time, improving life quality while making the economy more dynamic.

To ensure that humans and robots can safely coexist, the ability to recognize human behavior is a prerequisite for robots and a fundamental technical challenge for researchers. Current research can only recognize relatively simple cases of human behavior due to the lack of enough data and archetypally designed experiments. Our study elucidates human behavior in a systematic manner by observing the behavior of human subjects under more complex situations where they are surrounded by other people or objects to address the challenge. We focus on the following common situation that people pass each other through a narrow passage. We constructed a motion capture room with a narrow passage environment and measured the motion of human subjects performing different tasks. In addition, during the narrow passage experiment, we made subjects hold different daily necessaries (such as a backpack) to observe influences on human behavior. Our study found that passing and avoidance behavior exhibited by each of our subjects were significantly influenced by what kind of daily necessaries subjects carry. This research provides novel findings on human behavior in complex environments: in the case where subjects holding a handbag (a type of daily necessaries that stays next to one's body), they showed the tendency to be affected by the other subject and move more dynamically compared to the subject without anything or with other daily necessaries; in the case where subjects carrying a backpack (a type of daily necessaries on one's back) and looking at a smartphone, they also showed the tendency of being affected by others, but their motion is restricted compared to the subject without anything.

Gait assistance robots are used to improve gait performance ability or perform gait motion with an assistance for several articular motions. The sparing use of a gait assistance robot to decrease the duration of the robot’s assistance is important for keeping the ability to perform a movement when the robot assists walking. In previous research, methods of ensuring a compliance mechanism and control method have been studied, and assistance for articular motions has been conducted independently using actuators corresponding to each articular motion. In this paper, we propose a wire-driven gait assistance robot to aid both hip and knee articular flexion motions by applying just one force to assist motion in the swing phase. We focused on a force that assists hip and knee flexion motion, and designed a robot with a compensation mechanism for the wire length. We used an assistance timing detection method for the robot, conducting tensile force control based on information from the hip, knee, and ankle angles. We carried out an experiment to investigate the controlled performance of the proposed robot and the effect on hip and knee angular velocity. We confirmed that the proposed robotic system can aid both hip and knee articular motion with just one force application.

The adaptive control of gait training robots is aimed at improving the gait performance by assisting motion. In conventional robotics, it has not been possible to adjust the robotic parameters by predicting the toe motion, which is considered a tripping risk indicator. The prediction of toe clearance during walking can decrease the risk of tripping. In this paper, we propose a novel method of predicting toe clearance that uses a radial basis function network. The input data were the angles, angular velocities, and angular accelerations of the hip, knee, and ankle joints in the sagittal plane at the beginning of the swing phase. In the experiments, seven subjects walked on a treadmill for 360 s. The radial basis function network was trained with gait data ranging from 20 to 200 data points and tested with 100 data points. The root mean square error between the true and predicted values was 3.28 mm for the maximum toe clearance in the earlier swing phase and 2.30 mm for the minimum toe clearance in the later swing phase. Moreover, using gait data of other five subjects, the root mean square error between the true and predicted values was 4.04 mm for the maximum toe clearance and 2.88 mm for the minimum toe clearance when the walking velocity changed. This provided higher prediction accuracy compared with existing methods. The proposed algorithm used the information of joint movements at the start of the swing phase and could predict both the future maximum and minimum toe clearances within the same swing phase.

Powered prostheses with low degree of freedom (DoF) have been developed for people with disabilities to assist daily tasks. These prostheses neglect the user's compensatory movements caused by the low degree of freedom. We assume that the movements can be reduced by well-designed controller of the devices. This paper explores an optimal control gain of the powered prosthesis to prevent the user from compensatory movements through experiments. In the experiments, we developed 1-DoF hand prosthesis with a position-controlled servo, which includes the constant gain as a feed-forward term. The compensatory movements are regarded as a joint torque at a shoulder (abduction/adduction). 4 intact subjects performed a pick-and-place task, using the prosthesis with several control gains. The empirical results show that there was the optimal gain for each subject, which reduces their compensatory movement.

Prediction of minimum toe clearance (MTC) during walking can decrease the risk of tripping. In this paper, we proposed a novel MTC prediction method using a radial basis function network. Input data were the angles, angular velocities, and angular accelerations of the hip, knee, and ankle joints in the sagittal plane at the start of the swing phase. In experiments, five subjects walked on a treadmill for 360 s. The radial basis function network was trained with 60 s of gait data and tested with the remaining 300 s of gait data. The root mean square error between the true and predicted MTC values was lower than 2.79 mm in all subjects.

Gait-training robots are used to improve gait performance by assisting motion. A robotic assistance method to encourage people to learn an induced gait is required, and robotic control methods of assistance as needed to enhance the active participation of patients have been studied. In this paper, we propose an intermittent force control method with a wire-driven gait-training robot to encourage people to learn the induced gait. We focused on a force-assisting knee flexion with increased toe clearance. We used an assistance timing detection method for the robot, conducting tensile force control based on information from the hip, knee, and ankle angles. The gait-training robot controlled the wire tensile force by controlling the motor rotation, and could switch between a mode in which force was applied and a mode in which force was not applied. We investigated the effect of the frequency of force application on the change in knee flexion angle after the gait-training robot stopped intervention. We confirmed that intermittent force application that did not assist every gait cycle was more beneficial in encouraging people to learn the induced gait than force application during every gait cycle.

We propose an effective timing of intermittent force application by a gait assistance robot with a wire-driven system to increase the toe trajectory throughout the swing phase. We tested different timings of force application at the shank, employing the short-term assistance of the robot to increase toe clearance throughout the swing phase. The force was applied to the shank to generate knee flexion torque because the shank motion generated by the knee flexion motion makes the largest contribution to toe clearance. Four timings of the force application were considered: when starting knee flexion before toe-off, when lifting the foot, while maintaining knee flexion after toe-off, and when finishing knee flexion after toe-off. Furthermore, we evaluated changes in the toe trajectory and articular angles of the lower limb for each timing condition. We used a timing detection method for the robot conducting tensile force control based on information from the hip, knee, and ankle angles. For all participants, an increase in the knee flexion angle in the early swing phase due to the force application increased the toe clearance throughout the swing phase. We thus conclude that force application when the user begins lifting their toe is effective in increasing toe clearance throughout the swing phase.

Gait training robots are useful for changing gait patterns and decreasing risk of trip. Previous research has reported that decreasing duration of the assistance or guidance of the robot is beneficial for efficient gait training. Although robotic intermittent control method for assisting joint motion has been established, the effect of the robot intervention timing on change of toe clearance is unclear. In this paper, we tested different timings of applying torque to the knee, employing the intermittent control of a gait training robot to increase toe clearance throughout the swing phase. We focused on knee flexion motion and designed a gait training robot that can apply flexion torque to the knee with a wire-driven system. We used a method of timing detecting for the robot conducting torque control based on information from the hip, knee, and ankle angles to establish a non-time dependent parameter that can be used to adapt to gait change, such as gait speed. We carried out an experiment in which the conditions were four time points: starting the swing phase, lifting the foot, maintaining knee flexion, and finishing knee flexion. The results show that applying flexion torque to the knee at the time point when people start lifting their toe is effective for increasing toe clearance in the whole swing phase.

Since the deceleration of the markets of infrastructures in developed economies occurred, the growing demand for infrastructure development in semi-developed countries has become more outstanding. Semi-developed countries such as Thailand aims to become a developed country. However, it is stuck in 'semi-developed country trap'. To break through this, Thai people need local industries with high-added value. Therefore, we proposed an inversed innovative strategy focusing on railway industry. At first, how railway industry was established so far was investigated for finding keys to establish it. Next, we had field trips so that we can find real needs from Thai people. Actually, many countries compete in Thai railway markets. Thus, a comparison between our proposal and others were made. Our proposal for establishing railway industry includes some steps to get local industry focusing on Thai situation. Finally, it also presents ASEAN market as a future plan after they acquire their local industries.

Elderly people are at risk of falling because of their low toe clearance. Gait training to improve toe clearance could be instrumental in avoiding tripping. We propose using a gait-training robot that applies torque during the pre-swing phase to achieve this goal. It is still possible to revert to their original trajectory after the training, however, depending on the magnitude of the applied torque. We investigated the relation between the magnitude of the applied torque and the change in toe clearance before and after application of torque. We developed a robot and carried out an experiment in which a motor pulls a string embedded on the robotic frame worn by the participants, thereby applying torque during the pre-swing phase. The experimental task included walking on a treadmill for 50 s. We applied torque to the knee during the pre-swing phase for 20 s. The phases before and after applying torque were 15-s normal walking phases with no interference from the robot. We compared toe clearance during the phases before and after applying torque. We found that the toe clearance increased after applying a torque of 8 Nm. We were thus able to verify the influence of torque on toe clearance.

Megan Emmons informs that Jory Denny and Ye Zhao, former cochairs of student Activities Committee (SAC) have graduated to new stages in their careers, and she would like to wish them much success in their future endeavors. She personally appreciates all the time, thought, and energy they invested in the robotics community. The SAC has placed an official call for two new cochairs to help fill the vacancies of Denny and Zhao. It has received many applications and appreciates the enthusiastic student community for its efforts.

Elderly people are at risk of tripping because of their narrow range of articular motion. To avoid tripping, gait training that improves their range of articular motion would be beneficial. In this study we propose a gait-training robot that applies a torque during the pre-swing phase to achieve this goal. We investigated the relationship between magnitude of applied torque and change in the range of knee-articular motion while walking before and after the application of this torque. We developed a wearable robot and carried out an experiment on human participants in which a motor pulls a string embedded on the robotic frame, applying torque in the pre-swing phase for a period of 20 [s]. Before and after applying torque the participant walked normally for 15 [s] without interference from the robot. We found that knee flexion angle increased after applying the torque if the torque was within the range of approximately 6-8 [Nm]. Therefore, we were able to verify that a new range of knee articular motion can be learned through application of torque.

To decrease the risk of falls in older people, gait training robots are needed. However, compliance control, which is the usual method used in existing gait training robots, may cause patients to walk in an unnatural way. To solve this problem, the purpose of this study is to develop a gait training robot that applies torque to the knee only in the pre-swing phase. We investigated the influence on the minimum toe clearance in the swing phase from applied torque to the knee. We developed the robot and carried out an experiment in which a motor pulls a string embedded on the frame that the subjects wear, and applies torque in the pre-swing phase. The results demonstrated torque conditions wherein the minimum toe clearance remains high after applying the torque.