Application of AI-Driven Systems to Biomechanical Analysis and Optimization of Physical Training in the Armed Forces of the Islamic Republic of Iran
الموضوعات : Analytical and Numerical Methods in Mechanical Design
Mohammad Sadegh Karimzadeh
1
,
Mehdi Naderi Nasab
2
,
Morteza Taheri
3
,
Seyed Abas Biniyaz
4
1 - PhD Student, Department of Sport Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 - Assistant Professor, Department of Sport Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran
3 - Professor, Faculty of Behavioral and Cognitive Sciences, University of Tehran, Tehran, Iran
4 - Assistant Professor, Department of Physical Education and Sports Sciences, Qazvin Branch, Islamic Azad University, Qazvin, Iran
الکلمات المفتاحية: Artificial intelligence, biomechanics, machine learning, motion analysis, military training, physical performance optimization,
ملخص المقالة :
With advances in machine-learning methods, fusing biomechanical data with artificial intelligence has become an efficient approach for
motion analysis and training optimization. This study set out to develop and evaluate an intelligent system for biomechanical analysis and
optimization of physical training among personnel of the Islamic Republic of Iran Army. Motion data collected during a battery of standard
military exercises were recorded using inertial measurement units (IMUs) alongside synchronized video. After preprocessing,
biomechanical features—including joint angles, angular velocity and acceleration, and ground reaction forces (GRF)—were extracted. To
identify movement patterns and assess performance indices, AI models comprising deep neural networks (DNN/CNN–LSTM) and support
vector machines (SVM) were employed. Results showed that the system achieved accuracy >92% in distinguishing optimal movements
from inefficient patterns associated with increased joint loading and muscular fatigue. Incorporating the system’s outputs into personalized
training prescriptions yielded, in pre–post evaluations, an 18% reduction in the estimated risk of musculoskeletal injury and a 15%
improvement in physical performance indices. Overall, the findings indicate that integrating AI and biomechanics offers an effective
pathway to intelligent military training, enhanced combat readiness, and reduced training-related injuries across the armed forces
[1] Delp, S. L., Anderson, F. C., Arnold, A. S., Loan, P., Habib, A., John, C. T., et al. (2007). OpenSim: Open-source software to create and analyze dynamic simulations of movement. IEEE Transactions on Biomedical Engineering, 54(11), 1940–1950. https://doi.org/10.1109/TBME.2007.901024
[2] Seth, A., Hicks, J. L., Uchida, T. K., Habib, A., Dembia, C. L., Dunne, J. J., et al. (2018). OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement. PLoS Computational Biology, 14(7), e1006223. https://doi.org/10.1371/journal.pcbi.1006223
[3] Damsgaard, M., Rasmussen, J., Christensen, S. T., Surma, E., & de Zee, M. (2006). Analysis of musculoskeletal systems in the AnyBody Modeling System. Simulation Modelling Practice and Theory, 14(8), 1100–1111. https://doi.org/10.1016/j. simpat. 2006.09.001
[4] Uhlrich, S. D., Uchida, T. K., Lee, M. R., & Delp, S. L. (2023). Ten steps to becoming a musculoskeletal simulation expert: A half-century of progress and outlook for the future. Journal of Biomechanics, 154, 111623. https://doi.org/10.1016/j.jbiomech.2023.111623
[5] Wei, S., Zhang, Z., Chen, Y., et al. (2023). The application of wearable sensors and machine learning in rehabilitation training: A systematic review. Sensors, 23(18), 7667. https://doi.org/10.3390/ s 23187667
[6] Mușat, C. L., Oancea, B., Iancu, A., et al. (2024). Diagnostic applications of AI in sports: A comprehensive review. Diagnostics, 14(22), 2516. https://doi.org/10.3390/diagnostics14222516
[7] Lloyd, D. G., Saxby, D. J., Pizzolato, C., et al. (2023). Maintaining soldier musculoskeletal health using personalised digital humans, wearables and/or computer vision. Journal of Science and Medicine in Sport, 26 (Suppl 1), S30–S39. https://doi.org/ 10.1016/j. jsams .2023.04.001
[8] Weart, A. N., Meyer, P., Kaiser, J., et al. (2025). Using wearable sensor technology to analyze running technique and prospective running-related injuries during United States military cadet basic training. Orthopaedic Journal of Sports Medicine. Advance online publication. https://doi.org/ 10.1177/ 23259671241309273
[9] Gahtan, B., Markovitz, A., Shmueli, E., et al. (2024). WearableMil: An end-to-end framework for military activity recognition and performance monitoring. In Proceedings of the IEEE International Conference on Healthcare Informatics (ICHI). https://doi.org/ pending (Preprint: arXiv:2406.xxxx)
[10] Rahlf, A. L., Hoenig, T., Stürznickel, J., et al. (2022). A machine learning approach to identify risk factors for running-related injuries: Study protocol for a prospective longitudinal cohort trial. BMC Sports Science, Medicine and Rehabilitation, 14(1), 75. https://doi.org/10.1186/s13102-022-00426-0
[11] Nuyts, L., De Mits, S., Dingenen, B., et al. (2022). Machine-learned prediction of lower extremity overuse injuries using plantar pressure. Frontiers in Bioengineering and Biotechnology, 10, 987118. https://doi.org/10.3389/fbioe.2022.987118
[12] Sigurdson, H., & Chan, J. (2021). Machine learning applications to sports injury: A review. In Proceedings of the 9th International Conference on Sport Sciences Research and Technology Support (icSPORTS) (pp. 157–168). https://doi.org/ 10.5220/ 0010717100003059
[13] Leckey, C., Taylor, L., Windt, J., et al. (2025). Machine learning approaches to injury risk prediction in sport: A scoping review with evidence synthesis. British Journal of Sports Medicine, 59(7), 491–500. https://doi.org/10.1136/bjsports-2024-108941
[14] Burton, W. S., II, Lenzi, T., & Fregly, B. J. (2021). Machine learning for rapid estimation of lower extremity joint and muscle forces from lab data. Journal of Biomechanics, 122, 110450. https://doi.org/10.1016/j.jbiomech.2021.110450
[15] Ding, G., Georgilas, I., & Plummer, A. (2022). Deep learning with an attention mechanism for continuous biomechanical motion estimation across varied activities. Frontiers in Bioengineering and Biotechnology, 10, 1021505. https://doi.org/ 10.3389/ fbioe. 2022. 1021505
[16] Khant, M., Gouwanda, D., et al. (2025). Integrating deep learning in stride-to-stride muscle activity estimation of young and old adults with wearable inertial measurement units. Scientific Reports. Advance online publication. https://doi.org/ 10.1038/ s 41598-024-83903-5
[17] Koşar, E., & Barshan, B. (2023). A new CNN–LSTM architecture for activity recognition employing wearable motion sensor data: Enabling diverse feature extraction. Engineering Applications of Artificial Intelligence, 124, 106529. https://doi.org/10.1016/ j.engappai .2023.106529
[18] Xia, M., Que, S., Liu, N., Wang, Q., & Li, T. (2025). Motion pattern recognition via CNN–LSTM–Attention model using array-based Wi-Fi CSI sensors in GNSS-denied areas. Electronics, 14(8), 1594. https://doi.org/10.3390/electronics14081594
[19] Rossi, A., Pappalardo, L., Cintia, P., et al. (2021). An injury forecaster in soccer: A mixed machine learning and multivariate approach. Frontiers in Bioengineering and Biotechnology, 9, 657823. https://doi.org/10.3389/fbioe.2021.657823
[20] Peters, M., Yates, J., Lande, B., et al. (2025). Wearable sensors for classification of load-handling tasks: Ergonomics and machine learning. Ergonomics. Advance online publication. https://doi.org/ 10.1080/00140139. 2025.online
[21] RTI International. (2024). Wearable sensors for service members & first responders: Considerations and applications [White paper]. Retrieved October 11, 2025, from https://www.rti.org/
[22] Johns Hopkins APL. (2022). Using sensors and ML to prevent warfighter injury. Military Embedded Systems. Retrieved October 11, 2025, from https://militaryembedded.com/
[23] Amendolara, A., Mkaouer, B., Rodríguez-Ruiz, D., et al. (2023). An overview of machine learning applications in sports. Applied Sciences, 13(19), 10973. https://doi.org/10.3390/app131910973
[24] Babangida, A. A., Nwodo, A. N., Ugwoke, P. E., et al. (2025). Sensor systems for biomechanical risk assessment: Perspectives. ACS Sensors. Advance online publication. https://doi.org/pending
[25] WIRED Editorial. (2023). Black Ghost/Equivital: Military wearable big-data for soldier monitoring. WIRED. Retrieved October 11, 2025, from https://www.wired.com/
[26] Tschida, S., Brix, L. D., Rezapour, M., et al. (2025). Wearable acquisition of objective military environmental exposure data. Military Medicine. Advance online publication. https://doi.org/pending
[27] Main, L. C., Dawson, B., Spiteri, T., et al. (2025). Soldier performance management: Insights from boots on the ground. BMJ Military Health. Advance online publication. https://doi.org/pending
[28] Alenjareghi, M. J. (2025). Wearable sensors in Industry 4.0: Preventing work-related musculoskeletal disorders. Safety Science. In press. https://doi.org/pending
[29] Karimzade, M. S., Ghiahi, M. M., & Golchin, A. (2025). Machine learning for real-time exercise correction and injury prevention. Authorea. Retrieved October 11, 2025, from https://www.authorea.com/
[30] Ghiahi, M. M., Karimzade, M. S., & Golchin, A. (2025). Designing and manufacturing medical insoles with flexi material… force analysis using OpenSim software. Analytical and Numerical Methods in Mechanical Design (ANMD), 2(2), 37–41. https://doi.org/10.71626/anmd.2025.1186739 (Persian)
[31] Kloppenborg, A., Fitzpatrick, J., Malone, S., et al. (2024). Predicting running-related injuries based on runner characteristics and machine learning. SportRxiv (preprint). Retrieved October 11, 2025, from https://www.sportrxiv.org/
[32] ResearchGate dataset. (2024). Injury prediction in competitive runners with machine learning. ResearchGate. Retrieved October 11, 2025, from https://www.researchgate.net/
[33] Preatoni, E., Hamill, J., Harrison, A. J., et al. (2020). Bioengineering approaches to sport biomechanics using inertial, force, and EMG sensors: A literature overview. Sports Biomechanics, 19(2), 149–171. https://doi.org/10.1080/14763141.2020.1714245
[34] Alrashidi, M., Albarrati, A., Alqahtani, S., et al. (2023). Machine-learning progress in lower-limb running biomechanics with wearable technology: A systematic review. Sensors, 23(9), 4199. https://doi.org/10.3390/s23094199
[35] Peters, M., Lande, B., Faber, G. S., et al. (2025). Wearables for monitoring posture and fatigue in load-carriage tasks. Applied Ergonomics. In press. https://doi.org/pending
[36] Yoder, E., Harrington, A., & Taylor, M. K. (2025). Real-time monitoring of military health and readiness: A narrative review. Military Medical Research, 12, Article. https://doi.org/pending
[37] U.S. Army John F. Kennedy Special Warfare Center. (2025). Data-driven training: Digital twins for military training. Special Warfare Journal. Retrieved October 11, 2025, from https://www.swcs.mil/
[38] Zhou, Y., Chen, X., Wang, L., et al. (2025). Performance of deep-learning models incorporating knee biomechanics. Scientific Reports. Advance online publication. https://doi.org/pending
[39] Liu, Y., Zhang, Z., & Shang, P. (2024). Human motion activity recognition and pattern analysis: Deep learning models (MLP, CNN–LSTM, CNN–GRU). Computer Methods in Biomechanics and Biomedical Engineering, 27(14), 1583–1601. https://doi.org/ 10.1080/ 10255842.2024.online
[40] Uhlrich, S. D., Falisse, A., & Kidziński, Ł., et al. (2022). Simulation of biomechanical experiments in OpenSim. Preprint. Retrieved October 11, 2025, from https://simtk.org/
[41] Nikooyan, A. A., Veeger, H. E. J., Chadwick, E. K. J., Praagman, M., & van der Helm, F. C. T. (2011). Development of a comprehensive musculoskeletal model. Journal of Biomechanics, 44(12), pages pending. https://doi.org/10.1016/j.jbiomech.2011.xx.xx
[42] Rahlf, A. L., Stürznickel, J., Hoenig, T., et al. (2022). Machine-learning pipeline for predicting injury risk from combined biomechanical and loading data. BMC Sports Science, Medicine and Rehabilitation, 14(1), 75. https://doi.org/10.1186/s13102-022-00426-0
[43] Fibion Ltd. (2024). Wearable sensors for classification and monitoring in military contexts [White paper]. Retrieved October 11, 2025, from https://www.fibion.com/
[44] Barak Gahtan Group. (2024). End-to-end frameworks and hierarchical deep learning for long-term wearable data in soldiers. arXiv (preprint). Retrieved October 11, 2025, from https://arxiv.org/
[45] Nakashima, M., Kitamura, T., & Matsumoto, T. (2025). Markerless motion capture + computer vision + deep learning in biomechanics: A scoping review. Sensors, 25(4), article pending. https://doi.org/ 10. 3390/ s2504xxxx
[46] Windt, J., Gabbett, T. J., Carey, D. L., et al. (2024). AI approaches in gait and sport biomechanics: A systematic review. Sports Biomechanics, 23(6), pages pending. https://doi.org/10.1080/14763141.2024.xx.xx
[47] RTI International; Military Health System. (2024). Machine learning and wearable technology: Practical considerations for military adoption [White papers]. Retrieved October 11, 2025, from https://www.health.mil/
