Evaluating the Performance of Joint Angle Estimation Algorithms on an Exoskeleton Mock-Up via a Modular Testing Approach

作     者：Pollard, Ryan S. Bass, Sarah M. Schall Jr, Mark C. Zabala, Michael E. 

作者机构：Auburn Univ Dept Mech Engn Auburn AL 36849 USA Auburn Univ Dept Ind & Syst Engn Auburn AL 36849 USA 

出 版 物：《SENSORS》 (传感器)

年 卷 期：2024年第24卷第17期

页      面：5673页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 0808[工学-电气工程] 07[理学] 0804[工学-仪器科学与技术] 0703[理学-化学] 

基　　金：U.S. Army Combat Capabilities Development Command (DEVCOM) W15QKN-17-9-1025-RPP-10 MOB 17-08 PR2024-568 

主　　题：exoskeleton mock-up estimation algorithms random forest kinematics single sensor joint angles 

摘      要：A common challenge for exoskeleton control is discerning operator intent to provide seamless actuation of the device with the operator. One way to accomplish this is with joint angle estimation algorithms and multiple sensors on the human-machine system. However, the question remains of what can be accomplished with just one sensor. The objective of this study was to deploy a modular testing approach to test the performance of two joint angle estimation models-a kinematic extrapolation algorithm and a Random Forest machine learning algorithm-when each was informed solely with kinematic gait data from a single potentiometer on an ankle exoskeleton mock-up. This study demonstrates (i) the feasibility of implementing a modular approach to exoskeleton mock-up evaluation to promote continuity between testing configurations and (ii) that a Random Forest algorithm yielded lower realized errors of estimated joint angles and a decreased actuation time than the kinematic model when deployed on the physical device.