Wearable rehabilitation wristband for distal radius fractures
BackgroundDistal radius fractures are a common type of fracture. For patients treated with closed reduction with splinting, a period of rehabilitation is still required after the removal of the splint. However, there is a general lack of attention and low compliance to rehabilitation training during...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1238176/full |
| _version_ | 1797686218461282304 |
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| author | Qing Zha Qing Zha Zeou Xu Zeou Xu Xuefeng Cai Guodong Zhang Xiaofeng Shen |
| author_facet | Qing Zha Qing Zha Zeou Xu Zeou Xu Xuefeng Cai Guodong Zhang Xiaofeng Shen |
| author_sort | Qing Zha |
| collection | DOAJ |
| description | BackgroundDistal radius fractures are a common type of fracture. For patients treated with closed reduction with splinting, a period of rehabilitation is still required after the removal of the splint. However, there is a general lack of attention and low compliance to rehabilitation training during this period, so it is necessary to build a rehabilitation training monitoring system to improve the efficiency of patients’ rehabilitation.MethodsA wearable rehabilitation training wristband was proposed, which could be used in the patient’s daily rehabilitation training scenario and could recognize four common wrist rehabilitation actions in real-time by using three thin film pressure sensors to detect the pressure change curve at three points on the wrist. An algorithmic framework for classifying rehabilitation training actions was proposed. In our framework, an action pre-detection strategy was designed to exclude false detections caused by switching initial gestures during rehabilitation training and wait for the arrival of the complete signal. To classify the action signals into four categories, firstly an autoencoder was used to downscale the original signal. Six SVMs were then used for evaluation and voting, and the final action with the highest number of votes would be used as the prediction result.ResultsExperimental results showed that the proposed algorithmic framework achieved an average recognition accuracy of 89.62%, an average recognition recall of 88.93%, and an f1 score of 89.27% on the four rehabilitation training actions.ConclusionThe developed device has the advantages of being small size and easy to wear, which can quickly and accurately identify and classify four common rehabilitation training actions. It can easily be combined with peripheral devices and technologies (e.g., cell phones, computers, Internet) to build different rehabilitation training scenarios, making it worthwhile to use and promote in clinical settings. |
| first_indexed | 2024-03-12T01:02:11Z |
| format | Article |
| id | doaj.art-9de7c4d22749415dbded75c034fc83f9 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-03-12T01:02:11Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-9de7c4d22749415dbded75c034fc83f92023-09-14T08:52:21ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-09-011710.3389/fnins.2023.12381761238176Wearable rehabilitation wristband for distal radius fracturesQing Zha0Qing Zha1Zeou Xu2Zeou Xu3Xuefeng Cai4Guodong Zhang5Xiaofeng Shen6School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, ChinaSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, ChinaSchool of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, ChinaSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, ChinaSuzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, ChinaSuzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, ChinaSuzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, ChinaBackgroundDistal radius fractures are a common type of fracture. For patients treated with closed reduction with splinting, a period of rehabilitation is still required after the removal of the splint. However, there is a general lack of attention and low compliance to rehabilitation training during this period, so it is necessary to build a rehabilitation training monitoring system to improve the efficiency of patients’ rehabilitation.MethodsA wearable rehabilitation training wristband was proposed, which could be used in the patient’s daily rehabilitation training scenario and could recognize four common wrist rehabilitation actions in real-time by using three thin film pressure sensors to detect the pressure change curve at three points on the wrist. An algorithmic framework for classifying rehabilitation training actions was proposed. In our framework, an action pre-detection strategy was designed to exclude false detections caused by switching initial gestures during rehabilitation training and wait for the arrival of the complete signal. To classify the action signals into four categories, firstly an autoencoder was used to downscale the original signal. Six SVMs were then used for evaluation and voting, and the final action with the highest number of votes would be used as the prediction result.ResultsExperimental results showed that the proposed algorithmic framework achieved an average recognition accuracy of 89.62%, an average recognition recall of 88.93%, and an f1 score of 89.27% on the four rehabilitation training actions.ConclusionThe developed device has the advantages of being small size and easy to wear, which can quickly and accurately identify and classify four common rehabilitation training actions. It can easily be combined with peripheral devices and technologies (e.g., cell phones, computers, Internet) to build different rehabilitation training scenarios, making it worthwhile to use and promote in clinical settings.https://www.frontiersin.org/articles/10.3389/fnins.2023.1238176/fulldistal radius fracturethin film pressure sensorrehabilitation training action recognitionautoencoderSVM |
| spellingShingle | Qing Zha Qing Zha Zeou Xu Zeou Xu Xuefeng Cai Guodong Zhang Xiaofeng Shen Wearable rehabilitation wristband for distal radius fractures Frontiers in Neuroscience distal radius fracture thin film pressure sensor rehabilitation training action recognition autoencoder SVM |
| title | Wearable rehabilitation wristband for distal radius fractures |
| title_full | Wearable rehabilitation wristband for distal radius fractures |
| title_fullStr | Wearable rehabilitation wristband for distal radius fractures |
| title_full_unstemmed | Wearable rehabilitation wristband for distal radius fractures |
| title_short | Wearable rehabilitation wristband for distal radius fractures |
| title_sort | wearable rehabilitation wristband for distal radius fractures |
| topic | distal radius fracture thin film pressure sensor rehabilitation training action recognition autoencoder SVM |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1238176/full |
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