Adaptive Thermal Clothing with IoT and Random Forest Regression for Dynamic Outdoor Comfort

This research presents a novel method for improving outdoor comfort by combining adaptive thermal apparel with Internet of Things (IoT) and Random Forest Regression. Wearing inflexible traditional outdoor gear may be a real pain for those living in different parts of the world. As a solution, we present an intelligent garment system that can adapt its thermal insulation in real-time using data gathered from IoT sensors. A Random Forest Regression model is used to ascertain the most appropriate thermal settings for the garments based on the gathered data, which encompasses environmental factors such as humidity, temperature, and wind speed. By taking each user's tastes and physiological needs into account, the adaptive system strives to provide individualized comfort. The suggested technique successfully maintains the target comfort levels throughout varied outside situations, according to the experimental findings. In an age of rising climatic fluctuation, our study offers a potential answer for dynamic outdoor comfort to the expanding area of smart fabrics and wearable technologies.