Application of AI-Driven Systems to Biomechanical Analysis and Optimization of Physical Training in the Armed Forces of the Islamic Republic of Iran
محورهای موضوعی : 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
کلید واژه: Artificial intelligence, biomechanics, machine learning, motion analysis, military training, physical performance optimization,
چکیده مقاله :
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
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
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