Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

An Intelligent In-Shoe System for Real-Time Gait Monitoring and Analysis

Version 1 : Received: 23 March 2021 / Approved: 25 March 2021 / Online: 25 March 2021 (13:52:03 CET)

A peer-reviewed article of this Preprint also exists.

Wu, J.; Kuruvithadam, K.; Schaer, A.; Stoneham, R.; Chatzipirpiridis, G.; Easthope, C.A.; Barry, G.; Martin, J.; Pané, S.; Nelson, B.J.; Ergeneman, O.; Torun, H. An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities. Sensors 2021, 21, 2869. Wu, J.; Kuruvithadam, K.; Schaer, A.; Stoneham, R.; Chatzipirpiridis, G.; Easthope, C.A.; Barry, G.; Martin, J.; Pané, S.; Nelson, B.J.; Ergeneman, O.; Torun, H. An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities. Sensors 2021, 21, 2869.

Journal reference: Sensors 2021, 21, 2869
DOI: 10.3390/s21082869

Abstract

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on the technologies for gait characteristic assessment, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigen-analysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics.

Keywords

gait diagnosis; wearable device; graphical descriptor; real-time monitoring; tele-rehabilitation; digital biomarkers

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.