A hybrid linear discriminant analysis and genetic algorithm to create a linear model of aging when performing motor tasks through inertial sensors positioned on the hand and forearm.


Journal

Biomedical engineering online
ISSN: 1475-925X
Titre abrégé: Biomed Eng Online
Pays: England
ID NLM: 101147518

Informations de publication

Date de publication:
16 Oct 2023
Historique:
received: 22 05 2023
accepted: 01 10 2023
medline: 23 10 2023
pubmed: 17 10 2023
entrez: 16 10 2023
Statut: epublish

Résumé

During the aging process, cognitive functions and performance of the muscular and neural system show signs of decline, thus making the elderly more susceptible to disease and death. These alterations, which occur with advanced age, affect functional performance in both the lower and upper members, and consequently human motor functions. Objective measurements are important tools to help understand and characterize the dysfunctions and limitations that occur due to neuromuscular changes related to advancing age. Therefore, the objective of this study is to attest to the difference between groups of young and old individuals through manual movements and whether the combination of features can produce a linear correlation concerning the different age groups. This study counted on 99 participants, these were divided into 8 groups, which were grouped by age. The data collection was performed using inertial sensors (positioned on the back of the hand and on the back of the forearm). Firstly, the participants were divided into groups of young and elderly to verify if the groups could be distinguished through the features alone. Following this, the features were combined using the linear discriminant analysis (LDA), which gave rise to a singular feature called the LDA-value that aided in verifying the correlation between the different age ranges and the LDA-value. The results demonstrated that 125 features are able to distinguish the difference between the groups of young and elderly individuals. The use of the LDA-value allows for the obtaining of a linear model of the changes that occur with aging in the performance of tasks in line with advancing age, the correlation obtained, using Pearson's coefficient, was 0.86. When we compare only the young and elderly groups, the results indicate that there is a difference in the way tasks are performed between young and elderly individuals. When the 8 groups were analyzed, the linear correlation obtained was strong, with the LDA-value being effective in obtaining a linear correlation of the eight groups, demonstrating that although the features alone do not demonstrate gradual changes as a function of age, their combination established these changes.

Sections du résumé

BACKGROUND BACKGROUND
During the aging process, cognitive functions and performance of the muscular and neural system show signs of decline, thus making the elderly more susceptible to disease and death. These alterations, which occur with advanced age, affect functional performance in both the lower and upper members, and consequently human motor functions. Objective measurements are important tools to help understand and characterize the dysfunctions and limitations that occur due to neuromuscular changes related to advancing age. Therefore, the objective of this study is to attest to the difference between groups of young and old individuals through manual movements and whether the combination of features can produce a linear correlation concerning the different age groups.
METHODS METHODS
This study counted on 99 participants, these were divided into 8 groups, which were grouped by age. The data collection was performed using inertial sensors (positioned on the back of the hand and on the back of the forearm). Firstly, the participants were divided into groups of young and elderly to verify if the groups could be distinguished through the features alone. Following this, the features were combined using the linear discriminant analysis (LDA), which gave rise to a singular feature called the LDA-value that aided in verifying the correlation between the different age ranges and the LDA-value.
RESULTS RESULTS
The results demonstrated that 125 features are able to distinguish the difference between the groups of young and elderly individuals. The use of the LDA-value allows for the obtaining of a linear model of the changes that occur with aging in the performance of tasks in line with advancing age, the correlation obtained, using Pearson's coefficient, was 0.86.
CONCLUSION CONCLUSIONS
When we compare only the young and elderly groups, the results indicate that there is a difference in the way tasks are performed between young and elderly individuals. When the 8 groups were analyzed, the linear correlation obtained was strong, with the LDA-value being effective in obtaining a linear correlation of the eight groups, demonstrating that although the features alone do not demonstrate gradual changes as a function of age, their combination established these changes.

Identifiants

pubmed: 37845723
doi: 10.1186/s12938-023-01161-4
pii: 10.1186/s12938-023-01161-4
pmc: PMC10580547
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

98

Subventions

Organisme : Conselho Nacional de Desenvolvimento Científico e Tecnológico
ID : 302942/2022-0
Organisme : Conselho Nacional de Desenvolvimento Científico e Tecnológico
ID : 309525/2021-7
Organisme : Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
ID : CAPES/DFATD-88887.159028/2017-00

Informations de copyright

© 2023. BioMed Central Ltd., part of Springer Nature.

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Auteurs

Veronica de Lima Gonçalves (V)

Postgraduate Program in Electrical and Biomedical Engineering, Faculty of Electrical Engineering, Centre for Innovation and Technology Assessment in Health, Federal University of Uberlândia, Uberlândia, Brazil.

Caio Tonus Ribeiro (CT)

Postgraduate Program in Electrical and Biomedical Engineering, Faculty of Electrical Engineering, Centre for Innovation and Technology Assessment in Health, Federal University of Uberlândia, Uberlândia, Brazil.

Guilherme Lopes Cavalheiro (GL)

Postgraduate Program in Electrical and Biomedical Engineering, Faculty of Electrical Engineering, Centre for Innovation and Technology Assessment in Health, Federal University of Uberlândia, Uberlândia, Brazil.

Maria José Ferreira Zaruz (MJF)

Postgraduate Program in Electrical and Biomedical Engineering, Faculty of Electrical Engineering, Centre for Innovation and Technology Assessment in Health, Federal University of Uberlândia, Uberlândia, Brazil.

Daniel Hilário da Silva (DH)

Postgraduate Program in Electrical and Biomedical Engineering, Faculty of Electrical Engineering, Centre for Innovation and Technology Assessment in Health, Federal University of Uberlândia, Uberlândia, Brazil.

Selma Terezinha Milagre (ST)

Postgraduate Program in Electrical and Biomedical Engineering, Faculty of Electrical Engineering, Centre for Innovation and Technology Assessment in Health, Federal University of Uberlândia, Uberlândia, Brazil.

Adriano de Oliveira Andrade (A)

Postgraduate Program in Electrical and Biomedical Engineering, Faculty of Electrical Engineering, Centre for Innovation and Technology Assessment in Health, Federal University of Uberlândia, Uberlândia, Brazil.

Adriano Alves Pereira (AA)

Postgraduate Program in Electrical and Biomedical Engineering, Faculty of Electrical Engineering, Centre for Innovation and Technology Assessment in Health, Federal University of Uberlândia, Uberlândia, Brazil. adriano.pereira@ufu.br.

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