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Reproducing Human Arm Strategy and Its Contribution to Balance Recovery Through Model Predictive Control

Keli Shen 1 Ahmed Chemori 2 Mitsuhiro Hayashibe 1 
2 DEXTER - Robotique médicale et mécanismes parallèles
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : The study of human balance recovery strategies is important for human balance rehabilitation and humanoid robot balance control. To date, many efforts have been made to improve balance during quiet standing and walking motions. Arm usage (arm strategy) has been proposed to control the balance during walking motion in the literature. However, limited research exists on the contributions of the arm strategy for balance recovery during quiet standing along with ankle and hip strategy. Therefore, in this study, we built a simplified model with arms and proposed a controller based on nonlinear model predictive control to achieve human-like balance control. Three arm states of the model, namely, active arms, passive arms, and fixed arms, were considered to discuss the contributions of arm usage to human balance recovery during quiet standing. Furthermore, various indexes such as root mean square deviation of joint angles and recovery energy consumption were verified to reveal the mechanism behind arm strategy employment. In this study, we demonstrate to computationally reproduce human-like balance recovery with and without arm rotation during quiet standing while applying different magnitudes of perturbing forces on the upper body. In addition, the conducted human balance experiments are presented as supplementary information in this paper to demonstrate the concept on a typical example of arm strategy.
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Submitted on : Monday, May 17, 2021 - 10:29:48 AM
Last modification on : Friday, September 16, 2022 - 12:04:26 PM
Long-term archiving on: : Wednesday, August 18, 2021 - 6:16:05 PM


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Keli Shen, Ahmed Chemori, Mitsuhiro Hayashibe. Reproducing Human Arm Strategy and Its Contribution to Balance Recovery Through Model Predictive Control. Frontiers in Neurorobotics, 2021, 15, pp.#679570. ⟨10.3389/fnbot.2021.679570⟩. ⟨lirmm-03227266⟩



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