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Biofeedback Respiratory Rehabilitation Training System Based on Virtual Reality Technology
Traditional respiratory rehabilitation training fails to achieve visualization and quantification of respiratory data in improving problems such as decreased lung function and dyspnea in people with respiratory disorders, and the respiratory rehabilitation training process is simple and boring. Ther...
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/PMC10674163/ https://www.ncbi.nlm.nih.gov/pubmed/38005413 http://dx.doi.org/10.3390/s23229025 |
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author | Shi, Lijuan Liu, Feng Liu, Yuan Wang, Runmin Zhang, Jing Zhao, Zisong Zhao, Jian |
author_facet | Shi, Lijuan Liu, Feng Liu, Yuan Wang, Runmin Zhang, Jing Zhao, Zisong Zhao, Jian |
author_sort | Shi, Lijuan |
collection | PubMed |
description | Traditional respiratory rehabilitation training fails to achieve visualization and quantification of respiratory data in improving problems such as decreased lung function and dyspnea in people with respiratory disorders, and the respiratory rehabilitation training process is simple and boring. Therefore, this article designs a biofeedback respiratory rehabilitation training system based on virtual reality technology. It collects respiratory data through a respiratory sensor and preprocesses it. At the same time, it combines the biofeedback respiratory rehabilitation training virtual scene to realize the interaction between respiratory data and virtual scenes. This drives changes in the virtual scene, and finally the respiratory data are fed back to the patient in a visual form to evaluate the improvement of the patient’s lung function. This paper conducted an experiment with 10 participants to evaluate the system from two aspects: training effectiveness and user experience. The results show that this system has significantly improved the patient’s lung function. Compared with traditional training methods, the respiratory data are quantified and visualized, the rehabilitation training effect is better, and the training process is more active and interesting. |
format | Online Article Text |
id | pubmed-10674163 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106741632023-11-07 Biofeedback Respiratory Rehabilitation Training System Based on Virtual Reality Technology Shi, Lijuan Liu, Feng Liu, Yuan Wang, Runmin Zhang, Jing Zhao, Zisong Zhao, Jian Sensors (Basel) Article Traditional respiratory rehabilitation training fails to achieve visualization and quantification of respiratory data in improving problems such as decreased lung function and dyspnea in people with respiratory disorders, and the respiratory rehabilitation training process is simple and boring. Therefore, this article designs a biofeedback respiratory rehabilitation training system based on virtual reality technology. It collects respiratory data through a respiratory sensor and preprocesses it. At the same time, it combines the biofeedback respiratory rehabilitation training virtual scene to realize the interaction between respiratory data and virtual scenes. This drives changes in the virtual scene, and finally the respiratory data are fed back to the patient in a visual form to evaluate the improvement of the patient’s lung function. This paper conducted an experiment with 10 participants to evaluate the system from two aspects: training effectiveness and user experience. The results show that this system has significantly improved the patient’s lung function. Compared with traditional training methods, the respiratory data are quantified and visualized, the rehabilitation training effect is better, and the training process is more active and interesting. MDPI 2023-11-07 /pmc/articles/PMC10674163/ /pubmed/38005413 http://dx.doi.org/10.3390/s23229025 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 Shi, Lijuan Liu, Feng Liu, Yuan Wang, Runmin Zhang, Jing Zhao, Zisong Zhao, Jian Biofeedback Respiratory Rehabilitation Training System Based on Virtual Reality Technology |
title | Biofeedback Respiratory Rehabilitation Training System Based on Virtual Reality Technology |
title_full | Biofeedback Respiratory Rehabilitation Training System Based on Virtual Reality Technology |
title_fullStr | Biofeedback Respiratory Rehabilitation Training System Based on Virtual Reality Technology |
title_full_unstemmed | Biofeedback Respiratory Rehabilitation Training System Based on Virtual Reality Technology |
title_short | Biofeedback Respiratory Rehabilitation Training System Based on Virtual Reality Technology |
title_sort | biofeedback respiratory rehabilitation training system based on virtual reality technology |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10674163/ https://www.ncbi.nlm.nih.gov/pubmed/38005413 http://dx.doi.org/10.3390/s23229025 |
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