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Personalised Gait Recognition for People with Neurological Conditions

There is growing interest in monitoring gait patterns in people with neurological conditions. The democratisation of wearable inertial sensors has enabled the study of gait in free living environments. One pivotal aspect of gait assessment in uncontrolled environments is the ability to accurately re...

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Detalles Bibliográficos
Autores principales: Ingelse, Leon, Branco, Diogo, Gjoreski, Hristijan, Guerreiro, Tiago, Bouça-Machado, Raquel, Ferreira, Joaquim J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9183078/
https://www.ncbi.nlm.nih.gov/pubmed/35684600
http://dx.doi.org/10.3390/s22113980
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author Ingelse, Leon
Branco, Diogo
Gjoreski, Hristijan
Guerreiro, Tiago
Bouça-Machado, Raquel
Ferreira, Joaquim J.
author_facet Ingelse, Leon
Branco, Diogo
Gjoreski, Hristijan
Guerreiro, Tiago
Bouça-Machado, Raquel
Ferreira, Joaquim J.
author_sort Ingelse, Leon
collection PubMed
description There is growing interest in monitoring gait patterns in people with neurological conditions. The democratisation of wearable inertial sensors has enabled the study of gait in free living environments. One pivotal aspect of gait assessment in uncontrolled environments is the ability to accurately recognise gait instances. Previous work has focused on wavelet transform methods or general machine learning models to detect gait; the former assume a comparable gait pattern between people and the latter assume training datasets that represent a diverse population. In this paper, we argue that these approaches are unsuitable for people with severe motor impairments and their distinct gait patterns, and make the case for a lightweight personalised alternative. We propose an approach that builds on top of a general model, fine-tuning it with personalised data. A comparative proof-of-concept evaluation with general machine learning (NN and CNN) approaches and personalised counterparts showed that the latter improved the overall accuracy in 3.5% for the NN and 5.3% for the CNN. More importantly, participants that were ill-represented by the general model (the most extreme cases) had the recognition of gait instances improved by up to 16.9% for NN and 20.5% for CNN with the personalised approaches. It is common to say that people with neurological conditions, such as Parkinson’s disease, present very individual motor patterns, and that in a sense they are all outliers; we expect that our results will motivate researchers to explore alternative approaches that value personalisation rather than harvesting datasets that are may be able to represent these differences.
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spelling pubmed-91830782022-06-10 Personalised Gait Recognition for People with Neurological Conditions Ingelse, Leon Branco, Diogo Gjoreski, Hristijan Guerreiro, Tiago Bouça-Machado, Raquel Ferreira, Joaquim J. Sensors (Basel) Article There is growing interest in monitoring gait patterns in people with neurological conditions. The democratisation of wearable inertial sensors has enabled the study of gait in free living environments. One pivotal aspect of gait assessment in uncontrolled environments is the ability to accurately recognise gait instances. Previous work has focused on wavelet transform methods or general machine learning models to detect gait; the former assume a comparable gait pattern between people and the latter assume training datasets that represent a diverse population. In this paper, we argue that these approaches are unsuitable for people with severe motor impairments and their distinct gait patterns, and make the case for a lightweight personalised alternative. We propose an approach that builds on top of a general model, fine-tuning it with personalised data. A comparative proof-of-concept evaluation with general machine learning (NN and CNN) approaches and personalised counterparts showed that the latter improved the overall accuracy in 3.5% for the NN and 5.3% for the CNN. More importantly, participants that were ill-represented by the general model (the most extreme cases) had the recognition of gait instances improved by up to 16.9% for NN and 20.5% for CNN with the personalised approaches. It is common to say that people with neurological conditions, such as Parkinson’s disease, present very individual motor patterns, and that in a sense they are all outliers; we expect that our results will motivate researchers to explore alternative approaches that value personalisation rather than harvesting datasets that are may be able to represent these differences. MDPI 2022-05-24 /pmc/articles/PMC9183078/ /pubmed/35684600 http://dx.doi.org/10.3390/s22113980 Text en © 2022 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
Ingelse, Leon
Branco, Diogo
Gjoreski, Hristijan
Guerreiro, Tiago
Bouça-Machado, Raquel
Ferreira, Joaquim J.
Personalised Gait Recognition for People with Neurological Conditions
title Personalised Gait Recognition for People with Neurological Conditions
title_full Personalised Gait Recognition for People with Neurological Conditions
title_fullStr Personalised Gait Recognition for People with Neurological Conditions
title_full_unstemmed Personalised Gait Recognition for People with Neurological Conditions
title_short Personalised Gait Recognition for People with Neurological Conditions
title_sort personalised gait recognition for people with neurological conditions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9183078/
https://www.ncbi.nlm.nih.gov/pubmed/35684600
http://dx.doi.org/10.3390/s22113980
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