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Open AccessArticle

A Wearable Gait Phase Detection System Based on Force Myography Techniques

MENRVA lab, Schools of Mechatronic Systems and Engineering Science, Simon Fraser University, Metro Vancouver, BC V5A 1S6, Canada
Author to whom correspondence should be addressed.
Sensors 2018, 18(4), 1279;
Received: 20 March 2018 / Revised: 11 April 2018 / Accepted: 19 April 2018 / Published: 21 April 2018
(This article belongs to the Section Biosensors)
(1) Background: Quantitative evaluation of gait parameters can provide useful information for constructing individuals’ gait profile, diagnosing gait abnormalities, and better planning of rehabilitation schemes to restore normal gait pattern. Objective determination of gait phases in a gait cycle is a key requirement in gait analysis applications; (2) Methods: In this study, the feasibility of using a force myography-based technique for a wearable gait phase detection system is explored. In this regard, a force myography band is developed and tested with nine participants walking on a treadmill. The collected force myography data are first examined sample-by-sample and classified into four phases using Linear Discriminant Analysis. The gait phase events are then detected from these classified samples using a set of supervisory rules; (3) Results: The results show that the force myography band can correctly detect more than 99.9% of gait phases with zero insertions and only four deletions over 12,965 gait phase segments. The average temporal error of gait phase detection is 55.2 ms, which translates into 2.1% error with respect to the corresponding labelled stride duration; (4) Conclusions: This proof-of-concept study demonstrates the feasibility of force myography techniques as viable solutions in developing wearable gait phase detection systems. View Full-Text
Keywords: FSR band; force sensors; gait phase; gait recognition FSR band; force sensors; gait phase; gait recognition
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Jiang, X.; Chu, K.H.; Khoshnam, M.; Menon, C. A Wearable Gait Phase Detection System Based on Force Myography Techniques. Sensors 2018, 18, 1279.

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