A comprehensive review of impact assessment of indoor thermal environment on work and cognitive performance - Combined physiological measurements and machine learning

Ensuring occupants’ work or cognitive performance and maintaining thermal comfort are important targets of indoor thermal environment management. Physiological indicators are susceptible to minor differences in air temperature and humidity and play an essential role in thermal environment studies. In recent years, advanced sensing technologies based on physiological measurements and machine learning (ML) approaches have provided a more precise and efficient way to assess the link between the indoor thermal environment and the performances of occupants. A review of this emerging field can assist in filling knowledge gaps and offer insight into future study and practice. This review work integrates the results of cognitive tests related to the thermal environment and performance, summarizes the application of existing physiological indicators, and the practice of using sensing technologies and ML technology to assess occupant performance and predict indoor thermal comfort. Cognitive testing results indicate that personal control of temperature and humidity appears to be a critical factor in environmental satisfaction. And the introduction of ML technology innovatively integrates various physiological and environmental parameters, with a median prediction accuracy of up to 84%. Among all variables, skin temperature (ST) is the most significant physiological variable influencing thermal sensation, air temperature and relative humidity are the most popular environmental input variables. In summary, these observations support the prospects of novel sensing technologies and thermal comfort prediction models, and indicate the weakness of current works and future directions for improvement. •A literature review of the indoor thermal environment on work and cognitive performance was performed.•The growing trend of research on physiological measurements and wearable sensors.•Machine learning implementations in indoor environment assessment.•Challenges and opportunities in the field are discussed.