Wearable device data-driven athlete injury detection and rehabilitation monitoring algorithm
Abstract
In recent years, sports wearable technology has completely changed the way athletes prepare, compete, and recover. Wearable technology has a lot to offer in the rehabilitation process, which is essential to an athlete’s return to their best performance. Wearable devices for athlete injury detection pose potential challenges like data quality, security, and privacy, impacting accuracy, reliability, and effectiveness. To solve these problems, an innovative injury detection and rehabilitation monitoring (IDRM) system was proposed for athletes. By employing an adjustable recurrent neural network (ARNN) to detect anomalies in injury risks such as abnormal joint movements in athletes. In this study, biomechanics data was collected from sports athletes through wearable devices, and the wearable system provided feedback to the user. A redefined convolutional neural network (RCNN) was utilized to monitor the rehabilitation process. This system tracks athlete’s rehabilitation progress and ensures that progress monitors were performed correctly, and the system, feasibility was evaluated on 10 healthy subjects performing 4 different rehabilitation exercises. Each exercise was performed four times monitoring and validation. The data was preprocessed using a Gaussian filter to remove noise from the obtained data. Then the features are extracted using independent component analysis (ICA) for dimensionality reduction from preprocessed data. The proposed method is implemented using Python software. In comparative analysis, the performance of ARNN showed high performance, with an F1-measure of 91.6%, accuracy of 93.5%, recall of 92.8%, and precision of 91.4%. With a 95% accuracy rate, 98% F1 measure, 94% precision, and 93% recall, the RCNN model functioned effectively. The result showed the proposed method achieved better performance in athlete injury detection and accurately recognizing all rehabilitation monitoring. This study provides a complete approach to athlete health management by highlighting the integration of rehabilitation monitoring and injury detection into an overall structure.
References
1. Guarda T, Villao D, Augusto MF. Impact of Biometric Sensors on Physical Activity. In: International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability. Cham: Springer Nature Switzerland; 2023. pp. 128–139.
2. Lutz J, Memmert D, Raabe D, et al. Wearables for Integrative Performance and Tactic Analyses: Opportunities, Challenges, and Future Directions. International Journal of Environmental Research and Public Health. 2019; 17(1): 59. doi: 10.3390/ijerph17010059
3. Luczak T, Burch R, Lewis E, et al. State-of-the-art review of athletic wearable technology: What 113 strength and conditioning coaches and athletic trainers from the USA said about technology in sports. International Journal of Sports Science & Coaching. 2019; 15(1): 26–40. doi: 10.1177/1747954119885244
4. Fei C, Liu R, Li Z, et al. Machine and deep learning algorithms for wearable health monitoring. In: Computational intelligence in healthcare. Cham: Springer International Publishing; 2021. pp. 105–160.
5. da Silva L. Wearable Technology in Sports Monitoring Performance and Health Metrics. Journal of Sport Psychology. 2024; 33(2): 250–258.
6. Fares MY, Khachfe HH, Salhab HA, et al. Physical Testing in Sports Rehabilitation: Implications on a Potential Return to Sport. Arthroscopy, Sports Medicine, and Rehabilitation. 2022; 4(1): e189–e198. doi: 10.1016/j.asmr.2021.09.034
7. Van Hooren B, Goudsmit J, Restrepo J, et al. Real-time feedback by wearables in running: Current approaches, challenges and suggestions for improvements. Journal of Sports Sciences. 2020; 38(2): 214–230. doi: 10.1080/02640414.2019.1690960
8. Di Paolo S, Lopomo NF, Della Villa F, et al. Rehabilitation and Return to Sport Assessment after Anterior Cruciate Ligament Injury: Quantifying Joint Kinematics during Complex High-Speed Tasks through Wearable Sensors. Sensors. 2021; 21(7): 2331. doi: 10.3390/s21072331
9. Cossich VRA, Carlgren D, Holash RJ, et al. Technological Breakthroughs in Sport: Current Practice and Future Potential of Artificial Intelligence, Virtual Reality, Augmented Reality, and Modern Data Visualization in Performance Analysis. Applied Sciences. 2023; 13(23): 12965. doi: 10.3390/app132312965
10. Kovoor M, Durairaj M, Karyakarte MS, et al. Sensor-enhanced wearables and automated analytics for injury prevention in sports. Measurement: Sensors. 2024; 32: 101054. doi: 10.1016/j.measen.2024.101054
11. Jauhiainen S, Kauppi JP, Leppänen M, et al. New Machine Learning Approach for Detection of Injury Risk Factors in Young Team Sport Athletes. International Journal of Sports Medicine. 2020; 42(02): 175–182. doi: 10.1055/a-1231-5304
12. Xie J, Chen G, Liu S. Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning. Frontiers in Neurorobotics. 2021; 15: 621196. doi: 10.3389/fnbot.2021.621196
13. Xu T, Tang L. Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention. Frontiers in Neurorobotics. 2021; 14: 620378. doi: 10.3389/fnbot.2020.620378
14. Zhu D, Zhang H, Sun Y, et al. Injury Risk Prediction of Aerobics Athletes Based on Big Data and Computer Vision. Scientific Programming. 2021; 1: 1–10. doi: 10.1155/2021/5526971
15. Cohan A, Schuster J, Fernandez J. A deep learning approach to injury forecasting in NBA basketball. Journal of Sports Analytics. 2021; 7(4): 277–289. doi: 10.3233/jsa-200529
16. Wu X, Zhou J, Zheng M, et al. Cloud-based deep learning-assisted system for diagnosis of sports injuries. Journal of Cloud Computing. 2022; 11(1). doi: 10.1186/s13677-022-00355-w
17. Zadeh A, Taylor D, Bertsos M, et al. Predicting Sports Injuries with Wearable Technology and Data Analysis. Information Systems Frontiers. 2020; 23(4): 1023–1037. doi: 10.1007/s10796-020-10018-3
18. Li N, Zhu X. Design and application of blockchain and IoT-enabled sports injury rehabilitation monitoring system using neural network. Soft Computing. 2023; 27(16): 11815–11832. doi: 10.1007/s00500-023-08677-w
19. Alghamdi WY. A novel deep learning method for predicting athletes’ health using wearable sensors and recurrent neural networks. Decision Analytics Journal. 2023; 7: 100213. doi: 10.1016/j.dajour.2023.100213
20. Chen X, Yuan G. Sports Injury Rehabilitation Intervention Algorithm Based on Visual Analysis Technology. In: Khan F (editor). Mobile Information Systems. Wiley; 2021. pp. 1–8.
21. Pu C, Zhou J, Sun J, et al. Football Player Injury Full-Cycle Management and Monitoring System Based on Blockchain and Machine Learning Algorithm. International Journal of Computational Intelligence Systems. 2023; 16(1): 41. doi: 10.1007/s44196-023-00217-6
22. Huihui X. Machine Vision Technology Based on Wireless Sensor Network Data Analysis for Monitoring Injury Prevention Data in Yoga Sports. In: Mobile Networks and Applications. Springer Professional; 2024. pp. 1–13.
23. https://www.kaggle.com/datasets/manideepreddy966/wearables-dataset/data
24. She H. Application of Big Data Analysis in Model Construction to Prevent Athlete Injury in Training. Applied Mathematics and Nonlinear Sciences. 2024; 9(1). doi: 10.2478/amns-2024-1723
25. Wang Y, Wu Q, Dey N, et al. Deep back propagation—long short-term memory network based upper-limb sEMG signal classification for automated rehabilitation. Biocybernetics and Biomedical Engineering. 2020; 40(3): 987–1001. doi: 10.1016/j.bbe.2020.05.003
Copyright (c) 2024 Yucui Pu, Long Liu
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
