One Step At a Time

Newly Proposed Gait Event Detection Using Position Benchmarked Against Existing Acceleration­-Based Methods

Master thesis (2020)

Authors

Y.O.U.P. Mickers Mechanical Engineering

Contributors

H. Vallery Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering (mentor)
M.M. Reijne Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering (graduation committee member)
M. Kok Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering (graduation committee member)
Faculty
Mechanical Engineering, Mechanical Engineering
Copyright: © 2020 Youp Mickers
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Published Date

04-12-2020

Language

English

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Faculty

Mechanical Engineering

Abstract

Gait event detection allows for insight into one’s gait pattern, an invaluable aid in rehabilitation. Current methods often rely on measured acceleration and rarely on position measurements [3]–[6]. In this paper we propose 4 novel gait detection methods based on the position of the Center of Mass (one approach being causal and thus suitable for real-time use) and compare them to 4 existing state-of-the-art acceleration- based methods. All algorithms are benchmarked on an existing data set (overground walking, 23 participants, 1772 steps), comparing the detection rate, false positive rate and the mean and (intra- and interparticipant) standard deviation of the timing error for Heel Strikes and Toe-Offs. We show that position-based algorithms give well-balanced results and are able to outperform the acceleration-based algorithms in all five metrics. Additionally, we propose and compare several methods for detecting left and right steps, thereby enabling quantification of the full gait cycle.