MGait: Model-Based Gait Analysis Using Wearable Bend and Inertial Sensors

TL;DR

This paper introduces MGait, a practical model for real-time gait analysis using wearable bend and inertial sensors, enabling continuous monitoring of step length with high accuracy for movement disorder assessment.

Contribution

The paper presents a novel wearable sensor-based method for accurate, real-time step length estimation suitable for daily monitoring of gait in movement disorder patients.

Findings

Estimated step length with 5.49% mean absolute percentage error

Provides accurate real-time feedback

Suitable for continuous daily gait monitoring

Abstract

Movement disorders, such as Parkinson's disease, affect more than 10 million people worldwide. Gait analysis is a critical step in the diagnosis and rehabilitation of these disorders. Specifically, step length provides valuable insights into the gait quality and rehabilitation process. However, traditional approaches for estimating step length are not suitable for continuous daily monitoring since they rely on special mats and clinical environments. To address this limitation, we present a novel and practical step-length estimation technique using low-power wearable bend and inertial sensors. Experimental results show that the proposed model estimates step length with 5.49% mean absolute percentage error and provides accurate real-time feedback to the user.