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Article

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

1
Institute of Robotics and Intelligent Systems, ETH Zurich, 8092 Zurich, Switzerland
2
Magnes AG, Selnaustrasse 5, 8001 Zurich, Switzerland
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Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
4
Cereneo Foundation, Center for Interdisciplinary Research (CEFIR), 6354 Vitznau, Switzerland
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Department of Mechanical and Construction Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
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Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Bijan Najafi
Sensors 2021, 21(8), 2869; https://doi.org/10.3390/s21082869
Received: 19 March 2021 / Revised: 11 April 2021 / Accepted: 15 April 2021 / Published: 19 April 2021
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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. View Full-Text
Keywords: gait diagnosis; wearable device; graphical descriptor; real-time monitoring; telerehabilitation; digital biomarkers gait diagnosis; wearable device; graphical descriptor; real-time monitoring; telerehabilitation; digital biomarkers
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MDPI and ACS Style

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. https://doi.org/10.3390/s21082869

AMA Style

Wu J, Kuruvithadam K, Schaer A, Stoneham R, Chatzipirpiridis G, Easthope CA, Barry G, Martin J, Pané S, Nelson BJ, 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(8):2869. https://doi.org/10.3390/s21082869

Chicago/Turabian Style

Wu, Jiaen, Kiran Kuruvithadam, Alessandro Schaer, Richie Stoneham, George Chatzipirpiridis, Chris A. Easthope, Gill Barry, James Martin, Salvador Pané, Bradley J. Nelson, Olgaç Ergeneman, and Hamdi Torun. 2021. "An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities" Sensors 21, no. 8: 2869. https://doi.org/10.3390/s21082869

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