A Data-Driven Framework for Postural Assessment in Combat Skill Instruction Using Embedded Inertial Micro-Sensing
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, ef...
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| Veröffentlicht in: | 2025 5th International Conference on Artificial Intelligence, Big Data and Algorithms (CAIBDA) S. 840 - 843 |
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20.06.2025
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Song, Guodong Liang, Jie Zeng, Qin |
| Author_xml | – sequence: 1 givenname: Qin surname: Zeng fullname: Zeng, Qin email: 457515840@qq.com organization: Officers College of PAP,Chengdu,China – sequence: 2 givenname: Jie surname: Liang fullname: Liang, Jie email: 774751854@qq.com organization: Officers College of PAP,Chengdu,China – sequence: 3 givenname: Guodong surname: Song fullname: Song, Guodong email: 549235322@qq.com organization: Officers College of PAP,Chengdu,China |
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| Snippet | In the domain of martial arts pedagogy and athletic conditioning, accurately capturing biomechanical movement patterns is vital for individualized feedback and... |
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| SubjectTerms | Biomechanics Data Fusion Algorithms Injuries Injury Risk Prediction Kinematics Martial Arts Posture Analysis MEMS Inertial Sensors Microelectromechanical systems Prediction algorithms Predictive models Safety Tracking Training |
| Title | A Data-Driven Framework for Postural Assessment in Combat Skill Instruction Using Embedded Inertial Micro-Sensing |
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