Predicting Neck Motion Direction With Explainable AI
Keywords:
Explainable Artificial Intelligence (XAI), Neck Movement Prediction, Head and Neck Injuries, Machine Learning, XGBoost, Neural Networks, Biomechanical Analysis, OpenSim, Sensor Data, Head Impact, Sports Safety, Automotive Safety, Injury Prevention, RehabilitationAbstract
Head and neck injuries are major concerns in sports, healthcare, and automotive safety, making accurate prediction of neck movement during head impacts essential for injury prevention and safety enhancement. This study proposes an Explainable Artificial Intelligence (XAI)-based system for predicting neck movement direction using machine learning techniques, including Neural Networks and XGBoost, integrated with biomechanical sensor data. Experimental data representing mild head impacts, including flexion and lateral movements commonly observed in sports activities, are analyzed using OpenSim for biomechanical modeling and assessment. The proposed XGBoost classifier achieves 98% prediction accuracy, demonstrating its effectiveness in identifying neck movement directions. The key contribution of the system is the integration of Explainable AI, which provides transparent and interpretable insights into model predictions, supporting clinicians, safety engineers, and researchers in making informed decisions. The proposed approach has potential applications in sports injury prevention, athlete training, automotive safety design, clinical diagnosis, and rehabilitation. By combining machine learning, biomechanical analysis, sensor-based measurements, and explainability, the research provides a reliable and interpretable framework for neck movement prediction and contributes to the development of safer and more effective injury-prevention solutions.
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