SHAP-driven insights into multimodal data: behavior phase prediction for industrial safety applications

and EMF exhibit accelerated growth patterns, whereas Range and RMS show boundary effects. Decision tree segmentation reveals relationships between feature values and SHAP contributions, providing actionable rules to improve safety. This study uses university student participants, which may limit generalizability; future validation with actual miners is recommended. Overall, the results highlight the predictive power of physiological features and the potential of wearable monitoring systems for real-time safety management.