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AIoT-Enabled Rehabilitation Recognition System—Exemplified by Hybrid Lower-Limb Exercises

Ubiquitous health management (UHM) is vital in the aging society. The UHM services with artificial intelligence of things (AIoT) can assist home-isolated healthcare in tracking rehabilitation exercises for clinical diagnosis. This study combined a personalized rehabilitation recognition (PRR) system...

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Autores principales: Lai, Yi-Chun, Kan, Yao-Chiang, Lin, Yu-Chiang, Lin, Hsueh-Chun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309886/
https://www.ncbi.nlm.nih.gov/pubmed/34300501
http://dx.doi.org/10.3390/s21144761
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author Lai, Yi-Chun
Kan, Yao-Chiang
Lin, Yu-Chiang
Lin, Hsueh-Chun
author_facet Lai, Yi-Chun
Kan, Yao-Chiang
Lin, Yu-Chiang
Lin, Hsueh-Chun
author_sort Lai, Yi-Chun
collection PubMed
description Ubiquitous health management (UHM) is vital in the aging society. The UHM services with artificial intelligence of things (AIoT) can assist home-isolated healthcare in tracking rehabilitation exercises for clinical diagnosis. This study combined a personalized rehabilitation recognition (PRR) system with the AIoT for the UHM of lower-limb rehabilitation exercises. The three-tier infrastructure integrated the recognition pattern bank with the sensor, network, and application layers. The wearable sensor collected and uploaded the rehab data to the network layer for AI-based modeling, including the data preprocessing, featuring, machine learning (ML), and evaluation, to build the recognition pattern. We employed the SVM and ANFIS methods in the ML process to evaluate 63 features in the time and frequency domains for multiclass recognition. The Hilbert-Huang transform (HHT) process was applied to derive the frequency-domain features. As a result, the patterns combining the time- and frequency-domain features, such as relative motion angles in y- and z-axis, and the HHT-based frequency and energy, could achieve successful recognition. Finally, the suggestive patterns stored in the AIoT-PRR system enabled the ML models for intelligent computation. The PRR system can incorporate the proposed modeling with the UHM service to track the rehabilitation program in the future.
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spelling pubmed-83098862021-07-25 AIoT-Enabled Rehabilitation Recognition System—Exemplified by Hybrid Lower-Limb Exercises Lai, Yi-Chun Kan, Yao-Chiang Lin, Yu-Chiang Lin, Hsueh-Chun Sensors (Basel) Article Ubiquitous health management (UHM) is vital in the aging society. The UHM services with artificial intelligence of things (AIoT) can assist home-isolated healthcare in tracking rehabilitation exercises for clinical diagnosis. This study combined a personalized rehabilitation recognition (PRR) system with the AIoT for the UHM of lower-limb rehabilitation exercises. The three-tier infrastructure integrated the recognition pattern bank with the sensor, network, and application layers. The wearable sensor collected and uploaded the rehab data to the network layer for AI-based modeling, including the data preprocessing, featuring, machine learning (ML), and evaluation, to build the recognition pattern. We employed the SVM and ANFIS methods in the ML process to evaluate 63 features in the time and frequency domains for multiclass recognition. The Hilbert-Huang transform (HHT) process was applied to derive the frequency-domain features. As a result, the patterns combining the time- and frequency-domain features, such as relative motion angles in y- and z-axis, and the HHT-based frequency and energy, could achieve successful recognition. Finally, the suggestive patterns stored in the AIoT-PRR system enabled the ML models for intelligent computation. The PRR system can incorporate the proposed modeling with the UHM service to track the rehabilitation program in the future. MDPI 2021-07-12 /pmc/articles/PMC8309886/ /pubmed/34300501 http://dx.doi.org/10.3390/s21144761 Text en © 2021 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
Lai, Yi-Chun
Kan, Yao-Chiang
Lin, Yu-Chiang
Lin, Hsueh-Chun
AIoT-Enabled Rehabilitation Recognition System—Exemplified by Hybrid Lower-Limb Exercises
title AIoT-Enabled Rehabilitation Recognition System—Exemplified by Hybrid Lower-Limb Exercises
title_full AIoT-Enabled Rehabilitation Recognition System—Exemplified by Hybrid Lower-Limb Exercises
title_fullStr AIoT-Enabled Rehabilitation Recognition System—Exemplified by Hybrid Lower-Limb Exercises
title_full_unstemmed AIoT-Enabled Rehabilitation Recognition System—Exemplified by Hybrid Lower-Limb Exercises
title_short AIoT-Enabled Rehabilitation Recognition System—Exemplified by Hybrid Lower-Limb Exercises
title_sort aiot-enabled rehabilitation recognition system—exemplified by hybrid lower-limb exercises
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309886/
https://www.ncbi.nlm.nih.gov/pubmed/34300501
http://dx.doi.org/10.3390/s21144761
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