Gait Event Detection from Accelerometry using the Teager-Kaiser Energy Operator

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Title: Gait Event Detection from Accelerometry using the Teager-Kaiser Energy Operator
Authors: Flood, Matthew W.O'Callaghan, BenLowery, Madeleine M.
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Date: Mar-2020
Online since: 2019-08-26T14:07:37Z
Abstract: Objective: A novel method based on the application of the Teager-Kaiser Energy Operator is presented to estimate instances of initial contact (IC) and final contact (FC) from accelerometry during gait. The performance of the proposed method was evaluated against four existing gait event detection (GED) methods under three walking conditions designed to capture the variance of gait in real-world environments. Methods: A symmetric discrete approximation of the Teager-Kaiser energy operator was used to capture simultaneous amplitude and frequency modulations of the shank acceleration signal at IC and FC during flat treadmill walking, inclined treadmill walking, and flat indoor walking. Accuracy of estimated gait events were determined relative to gait events detected using force-sensitive resistors. The performance of the proposed algorithm was assessed against four established methods by comparing mean-absolute error, sensitivity, precision and F1-score values. Results: The proposed method demonstrated high accuracy for GED in all walking conditions, yielding higher F1-scores (IC: >0.98, FC: >0.9) and lower mean-absolute errors (IC: <0.018s, FC: <0.039s) than other methods examined. Estimated ICs from shank-based methods tended to exhibit unimodal distributions preceding the force-sensitive resistor estimated ICs, whereas estimated gait events for waist-based methods had quasi-uniform random distributions and lower accuracy. Conclusion: Compared to established gait event detection methods, the proposed method yielded comparably high accuracy for IC detection, and was more accurate than all other methods examined for FC detection. Significance: The results support the use of the Teager-Kaiser Energy Operator for accurate automated GED across a range of walking conditions.
Funding Details: European Commission Horizon 2020
Science Foundation Ireland
metadata.dc.description.othersponsorship: Insight Research Centre
Type of material: Journal Article
Publisher: IEEE
Journal: IEEE Transactions on Biomedical Engineering
Volume: 67
Issue: 3
Start page: 658
End page: 666
Copyright (published version): 2019 IEEE
Keywords: AccelerometryFinal ContactGait Event DetectionInitial ContactTeager-Kaiser Energy OperatorTreadmill WalkingShank MountedWaist Mounted
DOI: 10.1109/TBME.2019.2919394
Language: en
Status of Item: Peer reviewed
Appears in Collections:Electrical and Electronic Engineering Research Collection
Insight Research Collection

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