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During COVID-19 pandemic, people’s lifestyles have been changed dramatically and an increase of depression among young adults has been observed. Most universities or colleges offered online courses instead to prevent COVID transmission. Typically, home environment is not designed for work or learning. Despite that the relationships between indoor environment quality (IEQ) and people’s physical health and work performance have been investigated in regular time, very few efforts have been taken to understand the relationship between IEQ and students’ mental health and learning performance during COVID-19 when mental health and learning have become more crucial due to the face-to-face classes suspension, stay-at-home policies. The research gap exists on how IEQ at home or similar residential buildings is related to mental health and learning. This work aims to 1) understand how indoor environmental (such as thermal, air quality, lighting, acoustic) satisfaction is related to college students’ mental health and learning performance, and 2) predict depression with IEQ satisfaction. 

Thermal comfort (TC) – how comfortable or satisfied a per- son is with the temperature of her/his surroundings – is one of the key factors influencing the indoor environmental quality of schools, libraries, and offices. We conducted an experiment to explore how TC can impact students’ learning. University students (n = 25) were randomly assigned to different temperature conditions in an office environment (25◦C → 30◦C, or 30◦C → 25◦C) that were implemented using a combination of heaters and air conditioners over a 1.25 hour session. The task of the participants was to learn from tutorial videos on three different topics, and a test was given after each tutorial. The results suggest that (1) changing the room temperature by a few degrees Celsius can stat. sig. impact students’ self-reported TC; (2) the relationship between TC and learning exhibited an inverted U-curve, i.e., should be neither too uncomfortable nor too comfortable. We also explored different computer vision and sensor-based approaches to measure students’ thermal comfort automatically. We found that (3) TC can be predicted automatically either from the room temperature or from an infra-red (IR) camera of the face; however, (4) TC prediction from a normal (visible-light) web camera is highly challenging, and only limited predictive power was found in the facial expression features to predict thermal comfort.