Cargando…

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

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 b...

Descripción completa

Detalles Bibliográficos
Autores principales: Wu, Jiaen, Kuruvithadam, Kiran, Schaer, Alessandro, Stoneham, Richie, Chatzipirpiridis, George, Easthope, Chris Awai, Barry, Gill, Martin, James, Pané, Salvador, Nelson, Bradley J., Ergeneman, Olgaç, Torun, Hamdi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8074136/
https://www.ncbi.nlm.nih.gov/pubmed/33921846
http://dx.doi.org/10.3390/s21082869
_version_ 1783684286953553920
author Wu, Jiaen
Kuruvithadam, Kiran
Schaer, Alessandro
Stoneham, Richie
Chatzipirpiridis, George
Easthope, Chris Awai
Barry, Gill
Martin, James
Pané, Salvador
Nelson, Bradley J.
Ergeneman, Olgaç
Torun, Hamdi
author_facet Wu, Jiaen
Kuruvithadam, Kiran
Schaer, Alessandro
Stoneham, Richie
Chatzipirpiridis, George
Easthope, Chris Awai
Barry, Gill
Martin, James
Pané, Salvador
Nelson, Bradley J.
Ergeneman, Olgaç
Torun, Hamdi
author_sort Wu, Jiaen
collection PubMed
description 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.
format Online
Article
Text
id pubmed-8074136
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-80741362021-04-27 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, Richie Chatzipirpiridis, George Easthope, Chris Awai Barry, Gill Martin, James Pané, Salvador Nelson, Bradley J. Ergeneman, Olgaç Torun, Hamdi Sensors (Basel) Article 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. MDPI 2021-04-19 /pmc/articles/PMC8074136/ /pubmed/33921846 http://dx.doi.org/10.3390/s21082869 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wu, Jiaen
Kuruvithadam, Kiran
Schaer, Alessandro
Stoneham, Richie
Chatzipirpiridis, George
Easthope, Chris Awai
Barry, Gill
Martin, James
Pané, Salvador
Nelson, Bradley J.
Ergeneman, Olgaç
Torun, Hamdi
An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities
title An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities
title_full An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities
title_fullStr An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities
title_full_unstemmed An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities
title_short An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities
title_sort intelligent in-shoe system for gait monitoring and analysis with optimized sampling and real-time visualization capabilities
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8074136/
https://www.ncbi.nlm.nih.gov/pubmed/33921846
http://dx.doi.org/10.3390/s21082869
work_keys_str_mv AT wujiaen anintelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities
AT kuruvithadamkiran anintelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities
AT schaeralessandro anintelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities
AT stonehamrichie anintelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities
AT chatzipirpiridisgeorge anintelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities
AT easthopechrisawai anintelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities
AT barrygill anintelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities
AT martinjames anintelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities
AT panesalvador anintelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities
AT nelsonbradleyj anintelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities
AT ergenemanolgac anintelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities
AT torunhamdi anintelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities
AT wujiaen intelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities
AT kuruvithadamkiran intelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities
AT schaeralessandro intelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities
AT stonehamrichie intelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities
AT chatzipirpiridisgeorge intelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities
AT easthopechrisawai intelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities
AT barrygill intelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities
AT martinjames intelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities
AT panesalvador intelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities
AT nelsonbradleyj intelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities
AT ergenemanolgac intelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities
AT torunhamdi intelligentinshoesystemforgaitmonitoringandanalysiswithoptimizedsamplingandrealtimevisualizationcapabilities