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The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review
The integration of wearable sensor technology and machine learning algorithms has significantly transformed the field of intelligent medical rehabilitation. These innovative technologies enable the collection of valuable movement, muscle, or nerve data during the rehabilitation process, empowering m...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10537628/ https://www.ncbi.nlm.nih.gov/pubmed/37765724 http://dx.doi.org/10.3390/s23187667 |
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author | Wei, Suyao Wu, Zhihui |
author_facet | Wei, Suyao Wu, Zhihui |
author_sort | Wei, Suyao |
collection | PubMed |
description | The integration of wearable sensor technology and machine learning algorithms has significantly transformed the field of intelligent medical rehabilitation. These innovative technologies enable the collection of valuable movement, muscle, or nerve data during the rehabilitation process, empowering medical professionals to evaluate patient recovery and predict disease development more efficiently. This systematic review aims to study the application of wearable sensor technology and machine learning algorithms in different disease rehabilitation training programs, obtain the best sensors and algorithms that meet different disease rehabilitation conditions, and provide ideas for future research and development. A total of 1490 studies were retrieved from two databases, the Web of Science and IEEE Xplore, and finally 32 articles were selected. In this review, the selected papers employ different wearable sensors and machine learning algorithms to address different disease rehabilitation problems. Our analysis focuses on the types of wearable sensors employed, the application of machine learning algorithms, and the approach to rehabilitation training for different medical conditions. It summarizes the usage of different sensors and compares different machine learning algorithms. It can be observed that the combination of these two technologies can optimize the disease rehabilitation process and provide more possibilities for future home rehabilitation scenarios. Finally, the present limitations and suggestions for future developments are presented in the study. |
format | Online Article Text |
id | pubmed-10537628 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105376282023-09-29 The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review Wei, Suyao Wu, Zhihui Sensors (Basel) Review The integration of wearable sensor technology and machine learning algorithms has significantly transformed the field of intelligent medical rehabilitation. These innovative technologies enable the collection of valuable movement, muscle, or nerve data during the rehabilitation process, empowering medical professionals to evaluate patient recovery and predict disease development more efficiently. This systematic review aims to study the application of wearable sensor technology and machine learning algorithms in different disease rehabilitation training programs, obtain the best sensors and algorithms that meet different disease rehabilitation conditions, and provide ideas for future research and development. A total of 1490 studies were retrieved from two databases, the Web of Science and IEEE Xplore, and finally 32 articles were selected. In this review, the selected papers employ different wearable sensors and machine learning algorithms to address different disease rehabilitation problems. Our analysis focuses on the types of wearable sensors employed, the application of machine learning algorithms, and the approach to rehabilitation training for different medical conditions. It summarizes the usage of different sensors and compares different machine learning algorithms. It can be observed that the combination of these two technologies can optimize the disease rehabilitation process and provide more possibilities for future home rehabilitation scenarios. Finally, the present limitations and suggestions for future developments are presented in the study. MDPI 2023-09-05 /pmc/articles/PMC10537628/ /pubmed/37765724 http://dx.doi.org/10.3390/s23187667 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 | Review Wei, Suyao Wu, Zhihui The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review |
title | The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review |
title_full | The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review |
title_fullStr | The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review |
title_full_unstemmed | The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review |
title_short | The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review |
title_sort | application of wearable sensors and machine learning algorithms in rehabilitation training: a systematic review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10537628/ https://www.ncbi.nlm.nih.gov/pubmed/37765724 http://dx.doi.org/10.3390/s23187667 |
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