Cloud-based deep learning-assisted system for diagnosis of sports injuries
<p dir="ltr">At both clinical and diagnostic levels, machine learning technologies could help facilitate medical decision-making. Prediction of sports injuries, for instance, is a key component of avoiding and minimizing injury in motion. Despite significant attempts to forecast spor...
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2022
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| _version_ | 1864513531618000896 |
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| author | Xiaoe Wu (17541951) |
| author2 | Jincheng Zhou (1887307) Maoxing Zheng (17541954) Shanwei Chen (12031760) Dan Wang (34472) Joseph Anajemba (17541957) Guangnan Zhang (13849297) Maha Abdelhaq (735574) Raed Alsaqour (735575) Mueen Uddin (4903510) |
| author2_role | author author author author author author author author author |
| author_facet | Xiaoe Wu (17541951) Jincheng Zhou (1887307) Maoxing Zheng (17541954) Shanwei Chen (12031760) Dan Wang (34472) Joseph Anajemba (17541957) Guangnan Zhang (13849297) Maha Abdelhaq (735574) Raed Alsaqour (735575) Mueen Uddin (4903510) |
| author_role | author |
| dc.creator.none.fl_str_mv | Xiaoe Wu (17541951) Jincheng Zhou (1887307) Maoxing Zheng (17541954) Shanwei Chen (12031760) Dan Wang (34472) Joseph Anajemba (17541957) Guangnan Zhang (13849297) Maha Abdelhaq (735574) Raed Alsaqour (735575) Mueen Uddin (4903510) |
| dc.date.none.fl_str_mv | 2022-11-23T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1186/s13677-022-00355-w |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Cloud-based_deep_learning-assisted_system_for_diagnosis_of_sports_injuries/24717474 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Sports science and exercise Information and computing sciences Data management and data science Distributed computing and systems software Machine learning Deep Learning Sports Injury Prediction Sports Cloud Computing Internet of Things (IoT) |
| dc.title.none.fl_str_mv | Cloud-based deep learning-assisted system for diagnosis of sports injuries |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">At both clinical and diagnostic levels, machine learning technologies could help facilitate medical decision-making. Prediction of sports injuries, for instance, is a key component of avoiding and minimizing injury in motion. Despite significant attempts to forecast sports injuries, the present method is limited by its inability to identify predictors. When designing measures for the avoidance of work-related accidents and the reduction of associated risks, the risk of injury to athletes is a crucial consideration. Various indicators are being evaluated to identify injury risk factors in a number of different methods. Consequently, this paper proposes a Deep Learning-assisted System (DLS) for diagnosing sports injuries using the Internet of Things (IoT) and the concept of cloud computing. The IoT sensors that compose the body area network collect crucial data for the diagnosis of sports injuries, while cloud computing makes available flexible computer system resources and computing power. This research examines the brain injury monitoring framework, uses an optimal neural network to forecast brain injury, and enhances the medical rehabilitation system for sports. Using the metrics accuracy, precision, recall, and F1-score, the performance of the proposed model is assessed and compared with current models.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Cloud Computing<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/s13677-022-00355-w" target="_blank">https://dx.doi.org/10.1186/s13677-022-00355-w</a></p><p dir="ltr">Disclaimer: The University of Doha for Science and Technology replaced the now-former College of the North Atlantic-Qatar after an Amiri decision in 2022. UDST has become and first national applied University in Qatar; it is also second national University in the country.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_87169b5fccac7447bfb3d5c44e122c9c |
| identifier_str_mv | 10.1186/s13677-022-00355-w |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24717474 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Cloud-based deep learning-assisted system for diagnosis of sports injuriesXiaoe Wu (17541951)Jincheng Zhou (1887307)Maoxing Zheng (17541954)Shanwei Chen (12031760)Dan Wang (34472)Joseph Anajemba (17541957)Guangnan Zhang (13849297)Maha Abdelhaq (735574)Raed Alsaqour (735575)Mueen Uddin (4903510)Health sciencesSports science and exerciseInformation and computing sciencesData management and data scienceDistributed computing and systems softwareMachine learningDeep LearningSports InjuryPredictionSportsCloud ComputingInternet of Things (IoT)<p dir="ltr">At both clinical and diagnostic levels, machine learning technologies could help facilitate medical decision-making. Prediction of sports injuries, for instance, is a key component of avoiding and minimizing injury in motion. Despite significant attempts to forecast sports injuries, the present method is limited by its inability to identify predictors. When designing measures for the avoidance of work-related accidents and the reduction of associated risks, the risk of injury to athletes is a crucial consideration. Various indicators are being evaluated to identify injury risk factors in a number of different methods. Consequently, this paper proposes a Deep Learning-assisted System (DLS) for diagnosing sports injuries using the Internet of Things (IoT) and the concept of cloud computing. The IoT sensors that compose the body area network collect crucial data for the diagnosis of sports injuries, while cloud computing makes available flexible computer system resources and computing power. This research examines the brain injury monitoring framework, uses an optimal neural network to forecast brain injury, and enhances the medical rehabilitation system for sports. Using the metrics accuracy, precision, recall, and F1-score, the performance of the proposed model is assessed and compared with current models.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Cloud Computing<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/s13677-022-00355-w" target="_blank">https://dx.doi.org/10.1186/s13677-022-00355-w</a></p><p dir="ltr">Disclaimer: The University of Doha for Science and Technology replaced the now-former College of the North Atlantic-Qatar after an Amiri decision in 2022. UDST has become and first national applied University in Qatar; it is also second national University in the country.</p>2022-11-23T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1186/s13677-022-00355-whttps://figshare.com/articles/journal_contribution/Cloud-based_deep_learning-assisted_system_for_diagnosis_of_sports_injuries/24717474CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/247174742022-11-23T03:00:00Z |
| spellingShingle | Cloud-based deep learning-assisted system for diagnosis of sports injuries Xiaoe Wu (17541951) Health sciences Sports science and exercise Information and computing sciences Data management and data science Distributed computing and systems software Machine learning Deep Learning Sports Injury Prediction Sports Cloud Computing Internet of Things (IoT) |
| status_str | publishedVersion |
| title | Cloud-based deep learning-assisted system for diagnosis of sports injuries |
| title_full | Cloud-based deep learning-assisted system for diagnosis of sports injuries |
| title_fullStr | Cloud-based deep learning-assisted system for diagnosis of sports injuries |
| title_full_unstemmed | Cloud-based deep learning-assisted system for diagnosis of sports injuries |
| title_short | Cloud-based deep learning-assisted system for diagnosis of sports injuries |
| title_sort | Cloud-based deep learning-assisted system for diagnosis of sports injuries |
| topic | Health sciences Sports science and exercise Information and computing sciences Data management and data science Distributed computing and systems software Machine learning Deep Learning Sports Injury Prediction Sports Cloud Computing Internet of Things (IoT) |