Self-Tuning Threshold Method for Real-Time Gait Phase Detection Based on Ground Contact Forces Using FSRs.

Jing Tang,Lie Yu,Jianbin Zheng,Yang Wang,Enqi Zhan,Qiuzhi Song

doi:10.3390/s18020481

Abstract

This paper presents a novel methodology for detecting the gait phase of human walking on level ground. The previous threshold method (TM) sets a threshold to divide the ground contact forces (GCFs) into on-ground and off-ground states. However, the previous methods for gait phase detection demonstrate no adaptability to different people and different walking speeds. Therefore, this paper presents a self-tuning triple threshold algorithm (STTTA) that calculates adjustable thresholds to adapt to human walking. Two force sensitive resistors (FSRs) were placed on the ball and heel to measure GCFs. Three thresholds (i.e., high-threshold, middle-threshold andlow-threshold) were used to search out the maximum and minimum GCFs for the self-adjustments of thresholds. The high-threshold was the main threshold used to divide the GCFs into on-ground and off-ground statuses. Then, the gait phases were obtained through the gait phase detection algorithm (GPDA), which provides the rules that determine calculations for STTTA. Finally, the STTTA reliability is determined by comparing the results between STTTA and Mariani method referenced as the timing analysis module (TAM) and Lopez–Meyer methods. Experimental results show that the proposed method can be used to detect gait phases in real time and obtain high reliability when compared with the previous methods in the literature. In addition, the proposed method exhibits strong adaptability to different wearers walking at different walking speeds.

Highlights

From a medical and industrial perspective, wearable devices have evolved and continue to develop in terms of providing assistance to humans [1]
This paper proposes a gait phase detection algorithm (GPDA), which provides the rules that determine calculations for self-tuning triple-threshold algorithm (STTTA)
This paper proposes a self-tuning triple-threshold algorithm that calculates adjustable thresholds to adapt to human walking

Summary

Introduction

From a medical and industrial perspective, wearable devices have evolved and continue to develop in terms of providing assistance to humans [1]. Gait analysis through wearable devices is an extensive area in the field of biomechanics that provides knowledge in terms of identifying pathologies, evaluating athletes’ performance, design of sports products and rehabilitation engineering [2]. The wearable devices used for gait analysis are designed by equipping various sensors on it. Estimated the gait events through a sensory apparatus, which combine three subsystems such as a primary phase estimator, a desired gait event detector and a phase error compensator.

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Discussion

Conclusion