Exploration of Gait Parameters Affecting the Accuracy of Force Myography-Based Gait Phase Detection

Abstract

Gait analysis has been considered in various scenarios to provide information about the ambulatory physical activity. In this regard, studying gait phases can provide valuable information about the quality of gait. Force myography (FMG) techniques have been successfully employed to detect gait events using pattern recognition methods. This paper explores how the accuracy of detecting gait phases is correlated with the parameters of gait and FMG signal. To this end, FMG data were collected from 11 volunteers walking on a treadmill with a custom-designed FMG ankle band. The collected FMG data were classified into four gait phases using Linear Discriminant Analysis (LDA) algorithm. The correlation between the error in classification and the parameters of gait and FMG signal was then investigated. The results show that in comparison with other studied parameters, variations in stride length have the most impact on the accuracy of gait phase classification with a coefficient of determination (R2) of 0.80. Such an effect is more pronounced when signal power-related features, such as root mean square (RMS), are used in the classification algorithm. This study provides insight into the factors affecting the accuracy of FMG-based techniques for gait analysis and is a preliminary step towards developing high performance FMG-based wearable ambulatory activity monitoring systems.