Wearable Sensing for Personal Thermal Comfort in the Built Environment: A Systematic Review of the Gap from Sensing to Actuation

Abstract

Highlights What are the main findings? Skin temperature and heart rate dominate, used in 66% and 59% of studies. Multimodal sensing shows higher accuracy, but not uniformly across tasks. What are the implications of the main findings? Validation strategy affects accuracy more than algorithm choice. Only 5 of 117 studies reach wearable-informed building control stage. Abstract Wearable sensors enable continuous, non-invasive monitoring of physiological and near-body environmental parameters, offering an occupant-centric alternative for thermal comfort assessment and building control. This systematic review analyses 117 studies (2008–2026) using body-worn sensors, tracing them along a four-stage pipeline: sensing, signal integration, comfort modeling, and building actuation. This reveals a previously unquantified bottleneck: while all 117 studies perform sensing and 83 (71%) integrate physiological and environmental signals, only 52 (44%) build predictive models and just five (4%) reach the building-actuation stage, of which only three implement closed-loop, wearable-informed control. Skin temperature (77 studies, 66%) and heart rate (69, 59%) are the most monitored signals, predominantly at the wrist (70, 60%); multimodal configurations are associated with higher accuracy than single-domain approaches. Machine learning models reach median classification accuracies near 90%, though validation strategy matters: leave-one-subject-out reaches 85% versus 90% for within-subject k-fold. Five structural limitations are identified: small, homogeneous samples (median 16 participants), laboratory-dominated designs (73 studies, 62%), inconsistent validation, limited open data, and unresolved multi-occupant aggregation. The field has learned to measure the occupant but not yet to act on the measurement. Closing this gap requires open benchmark datasets, standardized validation including leave-one-subject-out testing and PMV benchmarking, transfer learning, multi-occupant integration, and inclusive recruitment of vulnerable populations.