Dynamic Thermal Perception: Analyzing Skin Temperature Patterns Under Cyclical Heat Conditions Using Python

This research project has examined the variations in physiological and perceptual human skin responses to cyclical heating and cooling cycles. The study uses an open-source data repository from Vellei et al. (2022), with minute-by-minute measurements of skin temperature and thermal comfort using specified methodological comfort-thermometer ratings as observers to both the temperature and thermal comfort film. The study utilized the Python programming language to model the data and fitted its cooling period to Newton's Law of Cooling to assess thermodynamic behavior across multiple body zones. The research builds visualizations of the temperature trend upon application of specified methodology techniques, develops a comparison of the predicted and actual cooling periods, and studies how well biological thermoregulation corresponds to classical physics theory for thermodynamic behavior. This study provides an intersect into data science and biophysics.