Integrating Machine Learning and Wearable Technology to Prevent Musculoskeletal Injuries in Military Training: A Natural Science Approach

Rita Komalasari(1*), Cecep Mustafa(2),

(1) Department of Public Health, Faculty of Medicine, YARSI University, Jakarta
(2) Department of Public Health, Faculty of Medicine, YARSI University, Jakarta
(*) Corresponding Author

Abstract


Musculoskeletal injuries remain a substantial challenge in military training programs worldwide, undermining both operational readiness and soldier well-being. This paper explores the potential of an innovative approach to address this issue by integrating machine learning and wearable technology within the framework of natural sciences. The purpose of this study is to develop a comprehensive model capable of predicting and preventing musculoskeletal injuries in military personnel. The design/methodology/approach involves blending principles from biomechanics, physiology, kinesiology, and anatomy to create a predictive model, fueled by real-time data collected through wearable technology. This data is then analyzed using machine learning techniques to generate insights for injury prevention. The results showcase the viability of such an approach, offering the prospect of significantly reducing injuries and enhancing military preparedness. By revolutionizing injury prevention strategies through an interdisciplinary approach, this study underscores the potential to create a paradigm shift in safeguarding the physical health of military personnel on a global scale.

Keywords


Musculoskeletal Injuries; Military Training; Natural Sciences; Wearable Technology; Machine Learning

Full Text:

PDF

References


Ahmed, A., Aziz, S., Abd-Alrazaq, A., Farooq, F., & Sheikh, J. (2022). Overview of artificial intelligence-driven wearable devices for diabetes: scoping review. Journal of Medical Internet Research, 24(8), e36010. https://doi.org/10.2196/36010

Alahmari, K. A., Reddy, R. S., Tedla, J. S., Samuel, P. S., Kakaraparthi, V. N., Rengaramanujam, K., & Ahmed, I. (2020). The effect of Kinesio taping on cervical proprioception in athletes with mechanical neck pain-a placebo-controlled trial. BMC musculoskeletal disorders, 21(1), 1-9. https://doi.org/10.1186/s12891-020-03681-9

Gao, L., Zhang, G., Yu, B., Qiao, Z., & Wang, J. (2020). Wearable human motion posture capture and medical health monitoring based on wireless sensor networks. Measurement, 166, 108252. https://doi.org/10.1016/j.measurement.2020.108252

Lovalekar, M., Hauret, K., Roy, T., Taylor, K., Blacker, S. D., Newman, P., ... & Canham-Chervak, M. (2021). Musculoskeletal injuries in military personnel-Descriptive epidemiology, risk factor identification, and prevention. Journal of Science and Medicine in Sport, 24(10), 963-969. https://doi.org/10.1016/j.jsams.2021.03.016

Bindu, S., Mazumder, S., & Bandyopadhyay, U. (2020). Non-steroidal anti-inflammatory drugs (NSAIDs) and organ damage: A current perspective. Biochemical pharmacology, 180, 114147. https://doi.org/10.1016/j.bcp.2020.114147

Buller, M. J., Delves, S. K., Fogarty, A. L., & Veenstra, B. J. (2021). On the real-time prevention and monitoring of exertional heat illness in military personnel. Journal of Science and Medicine in Sport, 24(10), 975-981. https://doi.org/10.1016/j.jsams.2021.04.008

Chan, V. C., Ross, G. B., Clouthier, A. L., Fischer, S. L., & Graham, R. B. (2022). The role of machine learning in the primary prevention of work-related musculoskeletal disorders: A scoping review. Applied Ergonomics, 98, 103574. https://doi.org/10.1016/j.apergo.2021.103574

Deng, S., Zhao, H., Fang, W., Yin, J., Dustdar, S., & Zomaya, A. Y. (2020). Edge intelligence: The confluence of edge computing and artificial intelligence. IEEE Internet of Things Journal, 7(8), 7457-7469. https://doi.org/10.1109/JIOT.2020.2984887

Nassis, G., Verhagen, E., Brito, J., Figueiredo, P., & Krustrup, P. (2022). A review of machine learning applications in soccer with an emphasis on injury risk. Biology of sport, 40(1), 233-239. https://doi.org/10.5114/biolsport.2023.114283

Patel, V., Chesmore, A., Legner, C. M., & Pandey, S. (2022). Trends in workplace wearable technologies and connected‐worker solutions for next‐generation occupational safety, health, and productivity. Advanced Intelligent Systems, 4(1), 2100099. https://doi.org/10.1002/aisy.202100099

Randolph, G. W., Kamani, D., Wu, C. W., & Schneider, R. (2021). Surgical anatomy and monitoring of the recurrent laryngeal nerve. In Surgery of the thyroid and parathyroid glands (pp. 326-359). Elsevier. https://doi.org/10.1016/B978-0-323-66127-0.00036-3

Sharma, K., Anand, D., Sabharwal, M., Tiwari, P. K., Cheikhrouhou, O., & Frikha, T. (2021). A disaster management framework using internet of things-based interconnected devices. Mathematical Problems in Engineering, 2021, 1-21. https://doi.org/10.1155/2021/9916440

Smith, C., Doma, K., Heilbronn, B., & Leicht, A. (2023). Impact of a 5-Week Individualised Training Program on Physical Performance and Measures Associated with Musculoskeletal Injury Risk in Army Personnel: A Pilot Study. Sports, 11(1), 8. https://doi.org/10.3390/sports11010008

Supriya, M., & Deepa, A. J. (2020). Machine learning approach on healthcare big data: a review. Big Data and Information Analytics, 5(1), 58-75. https://doi.org/10.3934/bdia.2020005

Thakur, A., & Konde, A. (2021). Fundamentals of neural networks. International Journal for Research in Applied Science and Engineering Technology, 9, 407-26. https://doi.org/10.22214/ijraset.2021.37362

Tottoli, E. M., Dorati, R., Genta, I., Chiesa, E., Pisani, S., & Conti, B. (2020). Skin wound healing process and new emerging technologies for skin wound care and regeneration. Pharmaceutics, 12(8), 735. https://doi.org/10.3390/pharmaceutics12080735

Wang, B., Li, Y., & Freiheit, T. (2022). Towards intelligent welding systems from a HCPS perspective: A technology framework and implementation roadmap. Journal of Manufacturing Systems, 65, 244-259. https://doi.org/10.1016/j.jmsy.2022.09.012

Wardle, S. L., & Greeves, J. P. (2017). Mitigating the risk of musculoskeletal injury: a systematic review of the most effective injury prevention strategies for military personnel. Journal of science and medicine in sport, 20, S3-S10. https://doi.org/10.1016/j.jsams.2017.09.014


Refbacks

  • There are currently no refbacks.


Image
Office Address:
Faculty of Military Mathematics and Natural Sciences
Republic of Indonesia Defense University
Indonesian Peace and Security Center Complex, Sentul, Bogor 16810, Indonesia
Email: munisi.unhanri@gmail.com | WhatsApp: +6285742313964
Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Munisi: Military Mathematics and Natural Sciences View