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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9183078 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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