Integrated Motion State Prediction for Sit-to-Stand and Stand-to-Sit Motions Toward Effective Power Assist Control
Kai Ren, Yuichi Nakamura, Kazuaki Kondo, Kei Shimonishi, Takahide Ito, Jun-ichiro Furukawa, Qi An
Abstract
Sit-to-stand and stand-to-sit motions are important in daily activities. However, elderly individuals often find these motions difficult to perform with declining lower limb strength, which causes a considerable reduction to their quality of life. In this study, a sensing method for controlling robotic assistive devices was proposed. This method utilizes electromyographic measurements and a deep neural network to predict motion initiation, and it estimates the timing of triggering assistive devices. Experimental results indicate that four muscle synergy patterns are required to represent the sit-to-stand and stand-to-sit motions together, with two of them being shared between both movements. Subsequently, a long short-term memory network was designed to forecast these two motions, and the result indicates that the prediction accuracy reached 92.95% ± 0.83% with forecasting time of 300 ms.
BibTeX
@inproceedings{icra2025_integratedmotion,
title = {Integrated Motion State Prediction for Sit-to-Stand and Stand-to-Sit Motions Toward Effective Power Assist Control},
author = {Kai Ren and Yuichi Nakamura and Kazuaki Kondo and Kei Shimonishi and Takahide Ito and Jun-ichiro Furukawa and Qi An},
booktitle = {ICRA 2025},
year = {2025}
}