Predicting indoor personalized heat stress using wearable sensors and data-driven models

Rising global temperatures pose heightened heat stress challenges, especially impacting vulnerable populations such as older adults in low-income urban areas of color, who disproportionately face energy inefficiencies and environmental burdens. Traditional research often overlooks individual metabolic rates and behaviors in heat stress assessments, relying instead on isolated environmental measures in laboratory settings. This paper presents a novel, occupant-centric approach that integrates wearable sensor data, building information, and air-conditioning usage to predict extreme heat events and assess their impact on occupants through calibrated thermal building simulations for seven older adults in Houston, Texas. We developed a personalized heat stress model based on existing heat index equations and dynamic predicted mean votes, enhanced by measured metabolic rates. A data-driven machine learning algorithm was then used to predict extreme heat events with this model serving as the ground truth. Our findings reveal that elevated activity levels significantly contribute to discomfort during extreme heat events, often undetected by the heat index equation—an environmental metric—alone. Furthermore, we observe that combining metabolic rates with measured indoor temperatures consistently predicts extreme heat events with over 95 % accuracy, demonstrating almost 13 % improvement compared to using indoor temperature in isolation. This study provides a valuable proof of concept that underscores the merits of integrating multiple data sources to raise the accuracy and predictability of indoor extreme heat events. Our research not only offers potential to enhance early warning systems and guide architectural decisions but also influences building material choices, significantly mitigating the adverse effects of extreme heat. •Integrates metabolic rates with building data for personalized heat stress models•Explores interplay of building physics, activity rates, and climate in heat exposure•Data-driven approach predicts heat events for the elderly, emphasizing vulnerability•Elevated activity levels significantly increase discomfort during extreme heat events•Metabolic rates and indoor climate predict heat events with over 95% accuracy