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Deep Learning in the Recognition of Activities of Daily Living Using Smartwatch Data

The recognition of human activities (HAR) using wearable device data, such as smartwatches, has gained significant attention in the field of computer science due to its potential to provide insights into individuals’ daily activities. This article aims to conduct a comparative study of deep learning...

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Autores principales: Cavalcante, Ariany F., Kunst, Victor H. de L., Chaves, Thiago de M., de Souza, Júlia D. T., Ribeiro, Isabela M., Quintino, Jonysberg P., da Silva, Fabio Q. B., Santos, André L. M., Teichrieb, Veronica, da Gama, Alana Elza F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490567/
https://www.ncbi.nlm.nih.gov/pubmed/37687949
http://dx.doi.org/10.3390/s23177493
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author Cavalcante, Ariany F.
Kunst, Victor H. de L.
Chaves, Thiago de M.
de Souza, Júlia D. T.
Ribeiro, Isabela M.
Quintino, Jonysberg P.
da Silva, Fabio Q. B.
Santos, André L. M.
Teichrieb, Veronica
da Gama, Alana Elza F.
author_facet Cavalcante, Ariany F.
Kunst, Victor H. de L.
Chaves, Thiago de M.
de Souza, Júlia D. T.
Ribeiro, Isabela M.
Quintino, Jonysberg P.
da Silva, Fabio Q. B.
Santos, André L. M.
Teichrieb, Veronica
da Gama, Alana Elza F.
author_sort Cavalcante, Ariany F.
collection PubMed
description The recognition of human activities (HAR) using wearable device data, such as smartwatches, has gained significant attention in the field of computer science due to its potential to provide insights into individuals’ daily activities. This article aims to conduct a comparative study of deep learning techniques for recognizing activities of daily living (ADL). A mapping of HAR techniques was performed, and three techniques were selected for evaluation, along with a dataset. Experiments were conducted using the selected techniques to assess their performance in ADL recognition, employing standardized evaluation metrics, such as accuracy, precision, recall, and F1-score. Among the evaluated techniques, the DeepConvLSTM architecture, consisting of recurrent convolutional layers and a single LSTM layer, achieved the most promising results. These findings suggest that software applications utilizing this architecture can assist smartwatch users in understanding their movement routines more quickly and accurately.
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spelling pubmed-104905672023-09-09 Deep Learning in the Recognition of Activities of Daily Living Using Smartwatch Data Cavalcante, Ariany F. Kunst, Victor H. de L. Chaves, Thiago de M. de Souza, Júlia D. T. Ribeiro, Isabela M. Quintino, Jonysberg P. da Silva, Fabio Q. B. Santos, André L. M. Teichrieb, Veronica da Gama, Alana Elza F. Sensors (Basel) Article The recognition of human activities (HAR) using wearable device data, such as smartwatches, has gained significant attention in the field of computer science due to its potential to provide insights into individuals’ daily activities. This article aims to conduct a comparative study of deep learning techniques for recognizing activities of daily living (ADL). A mapping of HAR techniques was performed, and three techniques were selected for evaluation, along with a dataset. Experiments were conducted using the selected techniques to assess their performance in ADL recognition, employing standardized evaluation metrics, such as accuracy, precision, recall, and F1-score. Among the evaluated techniques, the DeepConvLSTM architecture, consisting of recurrent convolutional layers and a single LSTM layer, achieved the most promising results. These findings suggest that software applications utilizing this architecture can assist smartwatch users in understanding their movement routines more quickly and accurately. MDPI 2023-08-29 /pmc/articles/PMC10490567/ /pubmed/37687949 http://dx.doi.org/10.3390/s23177493 Text en © 2023 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
Cavalcante, Ariany F.
Kunst, Victor H. de L.
Chaves, Thiago de M.
de Souza, Júlia D. T.
Ribeiro, Isabela M.
Quintino, Jonysberg P.
da Silva, Fabio Q. B.
Santos, André L. M.
Teichrieb, Veronica
da Gama, Alana Elza F.
Deep Learning in the Recognition of Activities of Daily Living Using Smartwatch Data
title Deep Learning in the Recognition of Activities of Daily Living Using Smartwatch Data
title_full Deep Learning in the Recognition of Activities of Daily Living Using Smartwatch Data
title_fullStr Deep Learning in the Recognition of Activities of Daily Living Using Smartwatch Data
title_full_unstemmed Deep Learning in the Recognition of Activities of Daily Living Using Smartwatch Data
title_short Deep Learning in the Recognition of Activities of Daily Living Using Smartwatch Data
title_sort deep learning in the recognition of activities of daily living using smartwatch data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490567/
https://www.ncbi.nlm.nih.gov/pubmed/37687949
http://dx.doi.org/10.3390/s23177493
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