Advancements of Deep Learning Model-Based Rehabilitation Training System
Traditional therapies for rehabilitation training in modern society are difficult to track patients dynamically, so this paper introduces a rehabilitation training evaluation system under deep learning modeling to help assess the effectiveness of rehabilitation training. In this paper, one of the st...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02024.pdf |
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| _version_ | 1825206564461477888 |
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| author | Xu Chiyu |
| author_facet | Xu Chiyu |
| author_sort | Xu Chiyu |
| collection | DOAJ |
| description | Traditional therapies for rehabilitation training in modern society are difficult to track patients dynamically, so this paper introduces a rehabilitation training evaluation system under deep learning modeling to help assess the effectiveness of rehabilitation training. In this paper, one of the studies proposed the concept of posture-guided matching based on paired Siamese Convolutional Neural Networks (SCNN), abbreviated as ST-AMCNN, on a dataset of the traditional Chinese rehabilitation training Baduanjin. Another study classified the output layers of shoulder pain rehabilitation using IMU sensors with multiple training programs for different patients wearing IMUs. IMU sensors for rehabilitation training that requires some time to analyze data and feedback data, there are more efficient studies that promote finger movement by giving patients robotic gloves to wear and propose a hand rehabilitation system thus helping stroke survivors with active rehabilitation. In addition, it was suggested to use a Smart Movement and Rehabilitation Monitoring System (SMRMS) to focus more on the participants’ training precision and recuperation. The experimental results show that there is still room for the development of rehabilitation training assessment systems in terms of privacy, interpretation ability, and application scenarios, and that researchers can address the above issues by using federated learning, developing an expert system, and using transfer learning domain adaptation, respectively. |
| format | Article |
| id | doaj-art-61676a2f8adb494eb7fb4fa53a4f2db0 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-61676a2f8adb494eb7fb4fa53a4f2db02025-02-07T08:21:12ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700202410.1051/itmconf/20257002024itmconf_dai2024_02024Advancements of Deep Learning Model-Based Rehabilitation Training SystemXu Chiyu0Electronic Information Science and Technology, Wenzhou UniversityTraditional therapies for rehabilitation training in modern society are difficult to track patients dynamically, so this paper introduces a rehabilitation training evaluation system under deep learning modeling to help assess the effectiveness of rehabilitation training. In this paper, one of the studies proposed the concept of posture-guided matching based on paired Siamese Convolutional Neural Networks (SCNN), abbreviated as ST-AMCNN, on a dataset of the traditional Chinese rehabilitation training Baduanjin. Another study classified the output layers of shoulder pain rehabilitation using IMU sensors with multiple training programs for different patients wearing IMUs. IMU sensors for rehabilitation training that requires some time to analyze data and feedback data, there are more efficient studies that promote finger movement by giving patients robotic gloves to wear and propose a hand rehabilitation system thus helping stroke survivors with active rehabilitation. In addition, it was suggested to use a Smart Movement and Rehabilitation Monitoring System (SMRMS) to focus more on the participants’ training precision and recuperation. The experimental results show that there is still room for the development of rehabilitation training assessment systems in terms of privacy, interpretation ability, and application scenarios, and that researchers can address the above issues by using federated learning, developing an expert system, and using transfer learning domain adaptation, respectively.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02024.pdf |
| spellingShingle | Xu Chiyu Advancements of Deep Learning Model-Based Rehabilitation Training System ITM Web of Conferences |
| title | Advancements of Deep Learning Model-Based Rehabilitation Training System |
| title_full | Advancements of Deep Learning Model-Based Rehabilitation Training System |
| title_fullStr | Advancements of Deep Learning Model-Based Rehabilitation Training System |
| title_full_unstemmed | Advancements of Deep Learning Model-Based Rehabilitation Training System |
| title_short | Advancements of Deep Learning Model-Based Rehabilitation Training System |
| title_sort | advancements of deep learning model based rehabilitation training system |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02024.pdf |
| work_keys_str_mv | AT xuchiyu advancementsofdeeplearningmodelbasedrehabilitationtrainingsystem |