Ankle Positions Classification using Force Myography: An Exploratory Investigation

Xianta Jiang1, Hon Tang Chu1, Zhen Gang Xiao1, Lukas-Karim Merhi1, Carlo Menon

  • 1Simon Fraser University

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Contributed 4-Page Papers (Poster)

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12:00 - 14:00 | Thu 10 Nov | Maya Ballroom Foyer | ThPO

HI-POCT Poster Session and POC Technologies Demonstrations

Abstract

Monitoring the movements of the ankle may be highly relevant for applications such as sport injury prevention, rehabilitation, and gait analysis. This paper explores the feasibility of employing force myography (FMG) on the distal end of the lower leg to detect ankle position. FMG signals corresponding to 7 different ankle positions were recorded from three healthy volunteers. Using a linear discriminant analysis (LDA) classifier, the system achieved averaged prediction accuracies of 94% and 85% in cross validation and cross-trial evaluation, respectively. The results of this proof-of-concept study demonstrate the feasibility of using FMG to detect ankle position and its consequent potential use for acquiring information relevant to leg movement and gait.

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