Real-time monitoring and analysis of track and field athletes based on edge computing and deep reinforcement learning algorithm
This research focuses on real-time monitoring and analysis of track and field athletes, addressing the limitations of traditional monitoring systems in terms of real-time performance and accuracy. We propose an IoT-optimized system that integrates edge computing and deep learning algorithms. Traditi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824014492 |
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| _version_ | 1846150150393692160 |
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| author | Xiaowei Tang Bin Long Li Zhou |
| author_facet | Xiaowei Tang Bin Long Li Zhou |
| author_sort | Xiaowei Tang |
| collection | DOAJ |
| description | This research focuses on real-time monitoring and analysis of track and field athletes, addressing the limitations of traditional monitoring systems in terms of real-time performance and accuracy. We propose an IoT-optimized system that integrates edge computing and deep learning algorithms. Traditional systems often experience delays and reduced accuracy when handling complex motion data, whereas our method, by incorporating a SAC-optimized deep learning model within the IoT architecture, achieves efficient motion recognition and real-time feedback. Experimental results show that this system significantly outperforms traditional methods in response time, data processing accuracy, and energy efficiency, particularly excelling in complex track and field events. This research not only enhances the precision and efficiency of athlete monitoring but also provides new technical support and application prospects for sports science research. |
| format | Article |
| id | doaj-art-12c42cebd1fd4a8fa3b4c05f6212e7b0 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-12c42cebd1fd4a8fa3b4c05f6212e7b02024-11-29T06:23:04ZengElsevierAlexandria Engineering Journal1110-01682025-02-01114136146Real-time monitoring and analysis of track and field athletes based on edge computing and deep reinforcement learning algorithmXiaowei Tang0Bin Long1Li Zhou2School of Sports Training, Wuhan Sports University, Wuhan, Hubei 430070, ChinaSchool of Sports Training, Wuhan Sports University, Wuhan, Hubei 430070, China; Corresponding author.McGill University Montréal, 27708, CanadaThis research focuses on real-time monitoring and analysis of track and field athletes, addressing the limitations of traditional monitoring systems in terms of real-time performance and accuracy. We propose an IoT-optimized system that integrates edge computing and deep learning algorithms. Traditional systems often experience delays and reduced accuracy when handling complex motion data, whereas our method, by incorporating a SAC-optimized deep learning model within the IoT architecture, achieves efficient motion recognition and real-time feedback. Experimental results show that this system significantly outperforms traditional methods in response time, data processing accuracy, and energy efficiency, particularly excelling in complex track and field events. This research not only enhances the precision and efficiency of athlete monitoring but also provides new technical support and application prospects for sports science research.http://www.sciencedirect.com/science/article/pii/S1110016824014492Real-time athlete monitoringEdge computingDeep reinforcement learningIoT optimizationTrack and field athletes |
| spellingShingle | Xiaowei Tang Bin Long Li Zhou Real-time monitoring and analysis of track and field athletes based on edge computing and deep reinforcement learning algorithm Alexandria Engineering Journal Real-time athlete monitoring Edge computing Deep reinforcement learning IoT optimization Track and field athletes |
| title | Real-time monitoring and analysis of track and field athletes based on edge computing and deep reinforcement learning algorithm |
| title_full | Real-time monitoring and analysis of track and field athletes based on edge computing and deep reinforcement learning algorithm |
| title_fullStr | Real-time monitoring and analysis of track and field athletes based on edge computing and deep reinforcement learning algorithm |
| title_full_unstemmed | Real-time monitoring and analysis of track and field athletes based on edge computing and deep reinforcement learning algorithm |
| title_short | Real-time monitoring and analysis of track and field athletes based on edge computing and deep reinforcement learning algorithm |
| title_sort | real time monitoring and analysis of track and field athletes based on edge computing and deep reinforcement learning algorithm |
| topic | Real-time athlete monitoring Edge computing Deep reinforcement learning IoT optimization Track and field athletes |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824014492 |
| work_keys_str_mv | AT xiaoweitang realtimemonitoringandanalysisoftrackandfieldathletesbasedonedgecomputinganddeepreinforcementlearningalgorithm AT binlong realtimemonitoringandanalysisoftrackandfieldathletesbasedonedgecomputinganddeepreinforcementlearningalgorithm AT lizhou realtimemonitoringandanalysisoftrackandfieldathletesbasedonedgecomputinganddeepreinforcementlearningalgorithm |