Occupants' comfort perception about the indoor environment is closely linked to their health, wellbeing and productivity. Improvement of comfort level in office buildings has significant positive impacts on both employers and employees. Human comfort in indoor environment usually can be assessed in four aspects: thermal comfort, visual comfort, acoustic comfort and respiratory comfort. In this paper, we present a literature review on the previous research contributions towards studying various aspects of human comfort with a special focus on the respective assessment criteria, data collection methods and data analysis approaches employed by former studies. Previous review work has covered the fundamental concepts associated with human comfort. However, their studies mainly focus on thermal comfort and there is limited work that covers other aspects of comfort. Moreover, few of them discuss how the data is obtained, how to extract useful information from the data and how the data is analyzed. To fill up this gap, this paper conducts the survey from the data-driven point of view. Through the survey, we find that sensor technology has been widely used in the data collection for various types of comfort, while so far the machine learning approaches are mainly applied in the area of thermal comfort study. Finally, some potential future research areas are proposed based on the current status of the research work. The established knowledge in this paper would provide useful insights for engineers or researchers who embark on their research in this area. INDEX TERMS Human comfort, thermal comfort, assessment criteria, data analysis method, sensor technology, machine learning.

No abstract

No abstract

No abstract