A Data-Driven Framework for Postural Assessment in Combat Skill Instruction Using Embedded Inertial Micro-Sensing - Zeng, Qin; Liang, Jie; Song, Guodong (2025 5th International Conference on Artificial Intelligence, Big Data and Algorithms (CAIBDA), 2025)
In the domain of martial arts pedagogy and athletic conditioning, accurately capturing biomechanical movement patterns is vital for individualized feedback and injury prevention. With the evolution of micro-electromechanical systems (MEMS), wearable sensor arrays have become increasingly compact, efficient, and suitable for long-duration, unobtrusive motion tracking. In this study, a wearable lower-limb monitoring system is introduced, which integrates tri-axial inertial sensing modules comprising accelerometric, gyroscopic, and magnetometric units. To enhance the reliability of multi-modal kinematic signals, an adaptive fusion framework is constructed, incorporating quaternion-based orientation estimation and magnetic field calibration via ellipsoid compensation techniques. This system enables the extraction of joint kinematic parameters and supports the development of a comprehensive dataset tailored to martial arts biomechanics. Building upon this dataset, a regression-based predictive model is trained to quantify technical execution quality and estimate susceptibility to musculoskeletal strain. The resulting system facilitates decision-making in instructional contexts by offering interpretable, data-driven insights that inform skill refinement and safety interventions.