Using Reinforcement Learning to Estimate Human Joint Moments From Electromyography or Joint Kinematics: An Alternative Solution to Musculoskeletal-Based Biomechanics

运动学 手腕 肌电图 生物力学 接头(建筑物) 力矩(物理) 计算机科学 前臂 掌指关节 逆动力学 物理医学与康复 数学 医学 解剖 工程类 拇指 结构工程 物理 经典力学
作者
Wen Wu,Katherine R. Saul,He Huang
出处
期刊:Journal of biomechanical engineering [ASM International]
卷期号:143 (4) 被引量:27
标识
DOI:10.1115/1.4049333
摘要

Reinforcement learning (RL) has potential to provide innovative solutions to existing challenges in estimating joint moments in motion analysis, such as kinematic or electromyography (EMG) noise and unknown model parameters. Here, we explore feasibility of RL to assist joint moment estimation for biomechanical applications. Forearm and hand kinematics and forearm EMGs from four muscles during free finger and wrist movement were collected from six healthy subjects. Using the proximal policy optimization approach, we trained two types of RL agents that estimated joint moment based on measured kinematics or measured EMGs, respectively. To quantify the performance of trained RL agents, the estimated joint moment was used to drive a forward dynamic model for estimating kinematics, which was then compared with measured kinematics using Pearson correlation coefficient. The results demonstrated that both trained RL agents are feasible to estimate joint moment for wrist and metacarpophalangeal (MCP) joint motion prediction. The correlation coefficients between predicted and measured kinematics, derived from the kinematics-driven agent and subject-specific EMG-driven agents, were 98% ± 1% and 94% ± 3% for the wrist, respectively, and were 95% ± 2% and 84% ± 6% for the metacarpophalangeal joint, respectively. In addition, a biomechanically reasonable joint moment-angle-EMG relationship (i.e., dependence of joint moment on joint angle and EMG) was predicted using only 15 s of collected data. In conclusion, this study illustrates that an RL approach can be an alternative technique to conventional inverse dynamic analysis in human biomechanics study and EMG-driven human-machine interfacing applications.
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