Abstract
The rise in global temperatures has become a significant concern, leading to an increase in heat stroke incidents, which pose severe health consequences, including mortality. The comfort levels of indoor environments fluctuate depending on various activities performed in different situations. Physiological data, encompassing heart rate, body temperature, and blood pressure, provide valuable insights into the identification of patterns and trends that may signify an elevated risk of heatstroke. However, manual analysis of such data proves impractical due to its complexity and volume. In this chapter, we present an energy-efficient machine learning-based approach to forecast individual thermal comfort sensations, enabling the early identification of individuals at risk of heatstroke before symptom manifestation. We conducted experiments using four distinct machine learning models along with one deep learning-based model, achieving an accuracy of approximately 99% on test set.