During the COVID-19 pandemic, Indoor Environmental Quality (IEQ) was considered more seriously by the public because it pertained to the risk of infection. Especially for commercial buildings, the indoor environment
was linked with occupants’ health and productivity as well as the company’s performance according to ESG criteria. However, even though there were many studies focusing on the evaluation of IEQ, the experiments were not comprehensive to develop a predictive model between human physiological responses and indoor environmental quality. This research investigated the correlations between human physiological responses and IEQ components in two office areas with 14 participants. The IEQ data, including indoor air quality, thermal comfort, lighting, and acoustics, were collected using sensors, while simultaneously wearable devices were used to record human physiological response data. Cross-correlation analysis was applied to establish the correlations between indicators when analyzing datasets from sensors and wearable devices. Then predicted models were generated for personal and general data by using supervised machine learning for IAQ and thermal evaluation based on human bio-signals. As a result, this research confirmed that the human physiological signals provided feasibility to predict the user’s environmental satisfaction as a function of the data-driven model at both the individual and the general models based on the data of multiple human subject experiments. Even though this study provided a robust conclusion based on the analyzed results of the dataset, the prediction performance could be better by accommodating more datasets with more participants from different ages and occupied environments for a future study.