An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities

Wu, Jiaen, Kuruvithadam, Kiran, Schaer, Alessandro, Stoneham, Richard, Chatzipirpiridis, George, Easthope, Chris Awai, Barry, Gill, Martin, James, Pané, Salvador, Nelson, Bradley J., Ergeneman, Olgaç and Torun, Hamdi (2021) An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities. Sensors, 21 (8). p. 2869. ISSN 1424-8220

sensors-21-02869-v2.pdf - Published Version
Available under License Creative Commons Attribution 4.0.

Download (2MB) | Preview
Official URL:


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 technologies for the assessment of gait characteristics, 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 eigenanalysis 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.

Item Type: Article
Additional Information: Funding information: This research was funded by Marie Sklodowska‐Curie grant agreement No. 764977.
Uncontrolled Keywords: gait diagnosis; wearable device; graphical descriptor; real-time monitoring; telerehabilitation; digital biomarkers
Subjects: C600 Sports Science
H300 Mechanical Engineering
H700 Production and Manufacturing Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Faculties > Engineering and Environment > Mechanical and Construction Engineering
Faculties > Health and Life Sciences > Sport, Exercise and Rehabilitation
Depositing User: Elena Carlaw
Date Deposited: 19 Apr 2021 14:17
Last Modified: 31 Jul 2021 15:50

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics