Generation of gait data less prone to stumbling considering the physical differences among trainees

• Published in: Journal of Biomechanical Science and Engineering Vol. 16; no. 4; p. 20-00478
• Main Authors: OSAWA, Yusuke, WATANUKI, Keiichi, KAEDE, Kazunori, MURAMATSU, Keiichi
• Format: Journal Article
• Language: English
• Published: Tokyo The Japan Society of Mechanical Engineers 2021; Japan Science and Technology Agency
• Subjects: Activation maximization; Artificial neural networks; Classification; Gait; Gait recognition; Gait training; Healthcare; Motion analysis; Multivariate analysis; Neural network; Neural networks; Physical training; Test sets; Training; Walking; Walking factor

Several training methods have been developed to obtain motion information during real-time walking and feed it back to trainees who adjust their gait to ensure that the measured gait parameters approach target value, which may not always be suitable for every trainee owing to physical differences be...