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

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 17; p. 5673
• Main Authors: Pollard, Ryan S., Bass, Sarah M., Schall, Mark C., Zabala, Michael E.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 31.08.2024
• Subjects: Accuracy; Algorithms; Ankle; Ankle Joint - physiology; Biomechanical Phenomena - physiology; Business metrics; Control algorithms; Controllers; estimation algorithms; Exoskeleton Device; exoskeleton mock-up; Gait - physiology; Human mechanics; Human subjects; Humans; joint angles; Joints - physiology; Kinematics; Machine Learning; Metabolism; random forest; Sensors; single sensor; exoskeleton mock-up; kinematics; estimation algorithms; random forest; joint angles; single sensor
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24175673

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.