Estimation of Spatial-Temporal Gait Parameters based on the Fusion of Inertial and Film-Pressure Signals

TL;DR

This paper introduces a low-cost, wearable in-shoe system combining inertial and film-pressure sensors to accurately estimate spatial-temporal gait parameters, validated through experiments with stroke patients.

Contribution

It presents a novel multimodal sensor fusion system and algorithm for gait analysis, improving accuracy over existing inertial-only methods.

Findings

High correlation with laboratory gait analysis tools

Effective estimation of multiple gait parameters

Validated with stroke patient data

Abstract

Gait analysis (GA) has been widely used in physical activity monitoring and clinical contexts, and the estimation of the spatial-temporal gait parameters is of primary importance for GA. With the quick development of smart tiny sensors, GA methods based on wearable devices have become more popular recently. However, most existing wearable GA methods focus on data analysis from inertial sensors. In this paper, we firstly present a two-foot-worn in-shoe system (Gaitboter) based on low-cost, wearable and multimodal sensors' fusion for GA, comprising an inertial sensor and eight film-pressure sensors with each foot for gait raw data collection while walking. Secondly, a GA algorithm for estimating the spatial-temporal parameters of gait is proposed. The algorithm fully uses the fusion of two kinds of sensors' signals: inertial sensor and film-pressure sensor, in order to estimate the spatial-temporal gait parameters, such as stance phase, swing phase, double stance phase, cadence, step time, stride time, stride length, velocity. Finally, to verify the effectiveness of this system and algorithm of the paper, an experiment is performed with 27 stoke patients from local hospital that the spatial-temporal gait parameters obtained with the system and the algorithm are compared with a GA tool used in medical laboratories. And the experimental results show that it achieves very good consistency and correlation between the proposed system and the compared GA tool.