PhysioAgent: An Uncertainty-Driven Adaptive Agent for Physiological Comfort Recommendation

We present PhysioAgent, a spatiotemporal continual-learning agent that learns environment-to-physiology mappings from labeled datasets and transfers this knowledge to smart office environments without physiological occupant data. Integrating an uncertainty-aware TCN+GCN architecture with graph-based spatial reasoning and intra-day adaptation, PhysioAgent transforms raw sensor streams into confidence-bounded physiological comfort estimates and seating recommendations. In a real-world 25-node deployment across four days, we compare naive fine-tuning and experience replay under environmental drift. Both strategies reduce prediction uncertainty within each day through intra-day adaptation, but only the experience replay strategy, by learning from previous-day knowledge, maintains a monotonically decreasing trend across days. By the final task, experience replay reduces HR uncertainty by 16.7% and EDA uncertainty by 26.7% relative to naive fine-tuning, demonstrating robust continual adaptation to drastic environmental and behavioral shifts while generating health-optimized recommendations.