Using wearable technology to optimize sports performance and prevent injuries
Abstract
Purpose: Wearable devices, as emerging computing platforms, have gradually penetrated into people’s daily life, especially in the field of medical health management showing excellent potential. Methods Motion state recognition is performed by deep fusion CNN-LSTM model, CNN is used to obtain the most representative feature information characteristics of the local space of the motion data, while the LSTM layer is used to capture the long-term temporal correlation of these local features, and both of them are combined to obtain the more representative temporal-spatial correlation transportation state feature information implicit in the wearable gait data. An injury prevention method for exercise example parameters is designed, including patient training load characterization, and a Bi-LSTM network structure is used to design lightweight acceleration features to predict abnormalities in exercise physiological indicators. Results: Monitoring parameters such as heart rate rise slope, 1-minute heart rate recovery value, blood oxygen drop area, and 1-minute oxygen saturation recovery value, the false alarm rate of wearable device health data warning were kept at 2.55%. After exercise status and detected abnormalities in physiological parameters, personalized breathing training was performed, and the contribution ratio of abdominal breathing increased by 27% after training, and the patient’s heart rate decreased by 8.5 bpm and oxygen saturation increased by 2.4% compared to the pre-training period. Conclusion: The methodology in this paper can be more comprehensively optimized for sports performance and injury prevention, and is widely applicable in practical applications.
References
1. Carrier B, Helm MM, Davis DW, et al. Validation of VO2max And Lactate Threshold Estimates In Wearable Technology In High-Level Runners. Medicine & Science in Sports & Exercise. 2022; 54(9S): 79-79. doi: 10.1249/01.mss.0000876020.81991.3a
2. Zhang Y, Pi Y, Wang Q, et al. Application of video behavior fast detection based on wearable motion sensor devices in sports training. Measurement: Sensors. 2024; 33: 101096. doi: 10.1016/j.measen.2024.101096
3. Alduaij MY. Towards a Wearable Technology Model. International Journal of Information Systems in the Service Sector. 2022; 14(1): 1-25. doi: 10.4018/ijisss.295869
4. Xiao N, Yu W, Han X. Wearable heart rate monitoring intelligent sports bracelet based on Internet of things. Measurement. 2020; 164: 108102. doi: 10.1016/j.measurement.2020.108102
5. Botonis OK, Harari Y, Embry KR, et al. Wearable airbag technology and machine learned models to mitigate falls after stroke. Journal of NeuroEngineering and Rehabilitation. 2022; 19(1). doi: 10.1186/s12984-022-01040-4
6. Lown M, Brown M, Brown C, et al. Machine learning detection of Atrial Fibrillation using wearable technology. Tolkacheva EG, ed. PLOS ONE. 2020; 15(1): e0227401. doi: 10.1371/journal.pone.0227401
7. Yuan J, Zhu R. A fully self-powered wearable monitoring system with systematically optimized flexible thermoelectric generator. Applied Energy. 2020; 271: 115250. doi: 10.1016/j.apenergy.2020.115250
8. Smuck M, Odonkor CA, Wilt JK, et al. The emerging clinical role of wearables: factors for successful implementation in healthcare. npj Digital Medicine. 2021; 4(1). doi: 10.1038/s41746-021-00418-3
9. Rajinikanth A, Clark DK, Kapsetaki ME. A Novel System to Monitor Tic Attacks for Tourette Syndrome Using Machine Learning and Wearable Technology: Preliminary Survey Study and Proposal for a New Sensing Device. JMIR Neurotechnology. 2023; 2: e43351. doi: 10.2196/43351
10. Xie Y, Lu L, Gao F, et al. Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare. Current Medical Science. 2021; 41(6): 1123-1133. doi: 10.1007/s11596-021-2485-0
11. Ferguson C, Inglis SC, Breen PP, et al. Clinician Perspectives on the Design and Application of Wearable Cardiac Technologies for Older Adults: Qualitative Study. JMIR Aging. 2020; 3(1): e17299. doi: 10.2196/17299
12. Jin X, Li L, Dang F, et al. A survey on edge computing for wearable technology. Digital Signal Processing. 2022; 125: 103146. doi: 10.1016/j.dsp.2021.103146
13. Jeon H, Lee D. A New Data Augmentation Method for Time Series Wearable Sensor Data Using a Learning Mode Switching-Based DCGAN. IEEE Robotics and Automation Letters. 2021; 6(4): 8671-8677. doi: 10.1109/lra.2021.3103648
14. Ni J, Tang H, Ngu AH, et al. Physical-aware cross-modal adversarial network for wearable sensor-based human action recognition. Available online: https://arxiv.org/abs/2307.03638 (accessed on 2 June 2024).
15. Mishra SR, Mishra TK, Sanyal G, et al. Real time human action recognition using triggered frame extraction and a typical CNN heuristic. Pattern Recognition Letters. 2020; 135: 329-336. doi: 10.1016/j.patrec.2020.04.031
16. Hyun JE, Lim T, Kim SH, et al. Wearable ion gel based pressure sensor with high sensitivity and ultra-wide sensing range for human motion detection. Chemical Engineering Journal. 2024; 484: 149464. doi: 10.1016/j.cej.2024.149464
17. Lu H, Feng X, Zhang J. Early detection of cardiorespiratory complications and training monitoring using wearable ECG sensors and CNN. BMC Medical Informatics and Decision Making. 2024; 24(1). doi: 10.1186/s12911-024-02599-9
18. Koşar E, Barshan B. A new CNN-LSTM architecture for activity recognition employing wearable motion sensor data: Enabling diverse feature extraction. Engineering Applications of Artificial Intelligence. 2023; 124: 106529. doi: 10.1016/j.engappai.2023.106529
19. Xu Y, Zhao L. Inception-LSTM Human Motion Recognition with Channel Attention Mechanism. Jan N, ed. Computational and Mathematical Methods in Medicine. 2022; 2022: 1-12. doi: 10.1155/2022/9173504
20. Jørgensen SL, Mechlenburg I. Effects of Low-Load Blood-Flow Restricted Resistance Training on Functional Capacity and Patient-Reported Outcome in a Young Male Suffering From Reactive Arthritis. Frontiers in Sports and Active Living. 2021; 3. doi: 10.3389/fspor.2021.798902
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