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

Jiaen Wu,Richie Stoneham,James Martin,Hamdi Torun,Olgaç Ergeneman,Chris Awai Easthope,Salvador Pané,Bradley J Nelson,George Chatzipirpiridis,Alessandro Schaer,Gill Barry,Kiran Kuruvithadam

doi:10.3390/s21082869

Jiaen Wu, Richie Stoneham + Show 10 more

Open Access

https://doi.org/10.3390/s21082869

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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 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.

Highlights

Human locomotion is one of the most important abilities that must be acquired and maintained to perform activities of daily life and, despite requiring little thought, implies a complex series of coordinated events within the body
The amplitude indicates the strength of the frequency components relative to noise
This shows that a sampling rate of 100 Hz was not adequate to capture all the relevant information for gait analysis

Summary

Introduction

Human locomotion is one of the most important abilities that must be acquired and maintained to perform activities of daily life and, despite requiring little thought, implies a complex series of coordinated events within the body. This involves the communication of intricate sensory information, which is integrated in the nervous system and results in motor commands that control muscle activation and, joint movement. Clinical gait analysis is widely used to assess the overall health status of both pediatric and adult patients [2,3]. Clinical gait analysis has shown effectiveness in pretreatment evaluation, surgical decision making, and post-operative rehabilitation, and can be used to recognize deterioration of walking patterns that are associated with a variety of orthopedic and neurological disorders, such as ankle sprains, rheumatoid arthritis, Parkinson’s disease, cerebral palsy, dementia, and multiple sclerosis [2,4,5,6,7,8,9]

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