The potential and challenges of inferring thermal comfort at home using commodity sensors

For decades, researchers have investigated ways to infer human thermal comfort. Studies have usually required cumbersome sensors and human observers, making them inappropriate for use in naturalistic settings such as the home. Emerging wearable and smart home sensing devices offer the opportunity to develop new models of thermal comfort based on data collected in-situ. To explore this opportunity, we deployed a sensing system in seven homes and collected self-report data from 11 participants for four weeks. Our system captures many factors employed in previous thermal comfort research, as well as new factors (e.g., activity level, sweat level). Machine learning-based models derived from the collected data show improvement over previous techniques, however significant prediction errors remain. In analyzing these errors we identify six problems that pose challenges for inferring comfort in the wild. Based on our findings, we suggest techniques to improve future in-situ thermal comfort modeling efforts.