The effect of the different training regimes and histories on the spatiotemporal characteristics of human running was evaluated in four groups of subjects who had different histories of engagement in running-specific training; sprinters, distance runners, active athletes, and sedentary individuals. Subjects ran at a variety of velocities, ranging from slowest to fastest, over 30 trials in a random order. Group averages of maximal running velocities, ranked from fastest to slowest, were: sprinters, distance runners, active athletes, and sedentary individuals. The velocity-cadence-step length (V-C-S) relationship, made by plotting step length against cadence at each velocity tested, was analyzed with the segmented regression method, utilizing two regression lines. In all subject groups, there was a critical velocity, defined as the inflection point, in the relationship. In the velocity ranges below and above the inflection point (slower and faster velocity ranges), velocity was modulated primarily by altering step length and by altering cadence, respectively. This pattern was commonly observed in all four groups, not only in sprinters and distance runners, as has already been reported, but also in active athletes and sedentary individuals. This pattern may reflect an energy saving strategy. When the data from all groups were combined, there were significant correlations between maximal running velocity and both running velocity and step length at the inflection point. In spite of the wide variety of athletic experience of the subjects, as well as their maximum running velocities, the inflection point appeared at a similar cadence (3.0 ± 0.2 steps/s) and at a similar relative velocity (65-70%Vmax). These results imply that the influence of running-specific training on the inflection point is minimal.

PURPOSE: The purpose of this study was to investigate the relationship between spatiotemporal variables of running and onset/offset timing of rectus femoris (RF) and biceps femoris (BF) muscle activities in both legs. METHODS: Eighteen male well-trained athletes (age = 20.7 ± 1.8 yr) were asked to run 50 m at maximal speed. The spatiotemporal variables (running speed, step frequency, and step length) over the distance from 30 to 50 m were measured. In addition, RF and BF muscle activities were obtained from both legs using wireless EMG sensors. To quantify the onset and offset timing of muscle activity, the band-pass filtered (20-450 Hz) EMG signal was processed using a Teager-Kaiser energy operator filter. We calculated RF and BF onset/offset timings (%) in both legs (e.g., ipsilateral leg RF [iRF] and contralateral leg BF [cBF]) during running cycle. Based on those timings, we obtained the EMG timing variables (%) as follows: "Switch1 (iBF-offset to iRF-onset)," "Switch2 (iRF-offset to iBF-onset)," "Scissors1 (cBF-onset to iRF-onset)," and "Scissors2 (iRF-offset to cBF-offset). RESULTS: We found that "Switch2" had positive (r = 0.495, P = 0.037), "Scissors1" had negative (r = -0.469, P = 0.049), and "Scissors2" had positive (r = 0.574, P = 0.013) correlations with step frequency. However, these variables had no significant correlations with running speed or step length. CONCLUSIONS: These results indicate that higher step frequency would be achieved by smoother switching of the agonist-antagonist muscle activities and earlier iRF activation relative to the cBF activity. To improve sprint performance, athletes and coaches should consider not only muscle activities in one leg but also coordination of muscle activities in both legs.

This paper proposes a method that can classify foot strike types using a deep learning model and can extract unknown factors, which enables to evaluate running motions without being influenced by biases of sports experts, using the contribution degree of input values (CDIV). Accelerometers are attached to the runner's body, and when the runner runs, a fixed camera observes the runner and acquires a video sequence synchronously with the accelerometers. To train a deep learning model for classifying foot strikes, we annotate foot strike acceleration data for RFS (Rearfoot strike) or non-RFS objectively by watching the video. To interpret the unknown factors extracted from the learned model, we calculate two CDIVs: the contributions of the resampling time and the accelerometer value to the output (foot strike type). Experiments on classifying unknown runners' foot strikes were conducted. As a common result to sport science, it is confirmed that the CDIVs contribute highly at the time of the right foot strike, and the sensor values corresponding to the right and left tibias contribute highly to classifying the foot strikes. Experimental results show the right tibia is important for classifying foot strikes. This is because many of the training data represent difference between the two foot strikes in the right tibia. As a conclusion, our proposed method could extract unknown factors from the classifier and could interpret the factors that contain similar knowledge to the prior knowledge of experts, as well as new findings that are not included in conventional knowledge.

This study was performed to devise an instructional program for children who were not good at sprinting and to verify the program’s effectiveness for improvement of sprinting ability and motion.<br>
The participants were 19 upper grade elementary school children who were not good at sprinting. The program included 2 drills with some teaching devices and running on flat markers. The children attended the program for 8 days (2 days per week) and each lesson lasted an hour. In order to validate the program outcome, sprint time (50 m), interval speed (every 10 m), average speed, maximal speed, rate of speed decline, interval and average step frequency and step length were analyzed, and sprint motions were evaluated. The results were as follows:<br>
1) Most of the children’s 50 m times were below the national average. This suggested that their negative feelings toward sprinting resulted from the realization that they were unable to run as fast as other children.<br>
2) The children’s sprint times were improved after the program, and a significant correlation between pre-time and post-pre time was revealed. It was also found that the greater the increase in the children’s step frequency, the faster their sprint times became. These results suggest that sprinting instruction allows low-performing children to increase their step frequency and improve their sprint times.<br>
3) The main aim of the program was to improve children’s sprint motions in the mid sprint phase, and the participants practiced start motions only twice during the program. As a result, speeds from the start to 10 m, 20-50 m, and maximum speed were increased significantly by this practice, suggesting that significant changes of speed led to improvement of the sprint times.<br>
4) Participants became able to swing back their leg under their body and to make contact with the ground with the middle or front of the foot. Therefore it was considered that the drills and running on flat markers with teaching devices were valuable for improving the children’s sprint motions.<br>
5) Although the scissors-like leg motion was not improved by practice with a color board and bells, the kneefolding motion of the swing leg did appear to be improved. Therefore, the children seemed to acquire basic skill in more rapid scissors-like leg motion.<br>
These results suggest that our instructional program was effective in enabling children to improve their sprinting ability and motion. However, additional research focusing on aspects such as the relationship between sprinting ability and sprint motion, or individual feelings and motor competency in the context of sprinting, will be needed.

There are many situations that the difference between imagery（subjective） and actual（objective） movements occurs in skiing. In traditionally utilized feedback method with video, it is impossible to get real time visual feedback. In the present study, we developed the system for real-time visual feedback during skiing, and evaluated the availability of the system. The movie captured by a video-operator who followed a subject was projected in online through the smartglass that the subjects wore. Sixteen skiers and snowboarders with various skill levels tried this system.We interviewed subjects immediately after the trial.Their impression was that getting real-time visual feedback of their own motion utilizing the present system, which helped them modify their performance in middle and high level subjects. However, beginner subjects could not afford to use the feedback information. Since the present system had unavoidable time-delay of 186 ms, it was suggested that it is not suitable for learning short turn. Therefore, the present examination suggested that this real-time visual feedback system can be effective in learning long turn for middle and high level skier who have clear goal images of their performance.