A Study on Enhancing Wearer Adaptation Through Accurate Gait Phase Prediction and Gradual Increase in Assistive Force Magnitude in Exosuits
Zhuo Wang, Chunjie Chen, Hui Chen, Sheng Wang, Jiale Zhang, Xiangyang Wang, Xinyu Wu
Abstract
Human-exosuit adaptation is a bi-directional process: exosuit-to-human locomotion adaptation maximizes the benefits of exosuit assistance, while human-to-exosuit adaptation accelerates the wearer's access to these benefits. To promote bi-directional adaptation, we investigated precise gait phase prediction and dynamic adjustment of force amplitude. For precise gait phase prediction, it was crucial to account for the impact of exosuit assistance on kinematics, as models trained on data collected with the exosuit assistance outperformed those trained without it. For dynamic adjustment of force amplitude, assistance progressively increased with each step as the wearer adapts, and we investigated two methods for progressively increasing the amplitude: Linear and Sigmoid. To evaluate these strategies, we conducted experiments with a soft exosuit assisting hip flexion, monitoring Root Mean Square variations of electromyographic signals from the Rectus Femoris muscle at each step. Compared to the constant amplitude approach, the gradually increasing amplitude approaches more effectively facilitated human-exosuit adaptation, with the Sigmoid model proving most effective. Specifically, with the Sigmoid growth model, wearers adapted to the exosuit after only 123<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula>31 steps. This study underscored the importance of considering human-exosuit adaptation when introducing assistance and recommended a gradual increase in assistance for naive users.
BibTeX
@inproceedings{ral2025_astudyonenhancin,
title = {A Study on Enhancing Wearer Adaptation Through Accurate Gait Phase Prediction and Gradual Increase in Assistive Force Magnitude in Exosuits},
author = {Zhuo Wang and Chunjie Chen and Hui Chen and Sheng Wang and Jiale Zhang and Xiangyang Wang and Xinyu Wu},
booktitle = {RA-L 2025},
year = {2025}
}