Multimodal Clustering and Spatiotemporal Analysis of Wearable Sensor Data for Occupational Health Risk Monitoring

Accurate interpretation of multimodal wearable data remains challenging in occupational environments due to heterogeneous sensing modalities, motion artifacts, and dynamic work conditions. This study proposes and validates an adaptive multimodal clustering framework for occupational health monitoring. The framework jointly models physiological, activity, and location data from 24 highway-maintenance workers, incorporating a silhouette-guided feature-weighting mechanism, multi-scale temporal change-point detection, and KDE-based spatial analysis. Specifically, the analysis identified three distinct and interpretable behavioral–physiological states that exhibit significant physiological differences (p < 0.001). Notably, it reveals a predominant yet heterogeneous baseline state alongside acute high-intensity and episodic surge states, offering a nuanced view of occupational risk beyond single-modality thresholds. The integrated framework provides a principled analytical workflow for spatiotemporal health risk assessment in field settings, particularly for vibration-intensive work scenarios, while highlighting the complementary role of physiological indicators in low- or static-motion tasks. This framework is particularly effective for vibration-intensive tasks involving powered tools. However, to mitigate potential biases in detecting static heavy-load activities with limited wrist motion (e.g., lifting or carrying), future extensions should incorporate complementary weighting of physiological indicators such as heart rate variability.