From Thermal Preference Prediction to Adaptive Thermal Intervention: A Reinforcement Learning Approach Using Physiological and Environmental Sensing

The paper proposes a two-stage personalized thermal comfort system that combines multimodal physiological and environmental sensing with reinforcement learning for decision-making. It aims to move beyond static HVAC setpoints and generic comfort models by tailoring thermal interventions to individual physiological variability. The approach seeks to improve occupant wellbeing and enable more responsive building-control strategies.

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• reinforcement-learning
• multimodal