@article{ref1,
title="Gait phase detection in walking and stairs using machine learning",
journal="Journal of biomechanical engineering",
year="2022",
author="Bauman, ValerieV and Brandon, Scott",
volume="ePub",
number="ePub",
pages="ePub-ePub",
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.<p /> <p>Language: en</p>",
language="en",
issn="0148-0731",
doi="10.1115/1.4055504",
url="http://dx.doi.org/10.1115/1.4055504"
}