Citation

Bauman VV, Brandon S. J. Biomech. Eng. 2022; ePub(ePub): ePub.

Copyright

(Copyright © 2022, American Society of Mechanical Engineers)

DOI

10.1115/1.4055504

PMID

36062965

Abstract

Activity and gait phase recognition algorithms are used in powered motion assistive devices to inform control of motorized components. The objective of this study was to develop a machine learning-based algorithm using inertial measurement data from the thigh and shank to simultaneously detect activity and gait phase (stance, swing) in real-world walking, stair ascent, and stair descent, with the intent of such an algorithm to be used in the control of a motion assistive device local to the knee. Using data from 80 participants, two decision tree and ten long short-term memory (LSTM) models that each used different feature sets and input data were tested and evaluated using a novel performance metric: proportion of perfectly classified strides (PPCS). Separate models were developed to classify i) both activity and gait phase simultaneously (one model predicting six states), and ii) activity-specific models (three individual binary classifiers predicting stance/swing phases). The superior activity-specific model had an accuracy of 98.0% and PPCS of 55.7%. The superior six-phase model used filtered inertial measurement data as its features and a median filter on its predictions and had an accuracy of 92.1% and PPCS of 22.9%. Pooling stance and swing phases from all activities and treating this model as a binary classifier, this model had an accuracy of 97.1%, which may be acceptable for real-world lower limb exoskeleton control if only stance and swing gait phases must be detected.

Language: en