Development of a human metabolic rate prediction model based on the use of Kinect-camera generated visual data-driven approaches

Predicting thermal comfort is one of the primary building research domains due to its technical and environmental significance. A metabolic rate, one of the significant variables for predicting an individual's thermal comfort, is primarily based on the human body's activity level. While other human and environmental factors, such as air temperature and relative humidity are easily measured and collected, with the help of sensory devices, a metabolic rate varies with time, and is not easy to measure to determine an accurate thermal comfort estimation in reality. Therefore, this study investigated the potential use of Deep Learning algorithm to accurately estimate the metabolic rate for a better thermal comfort estimation. A series of chamber tests were conducted with 23 test participants. The Kinect sensor was adopted to detect a user's physical motion, by capturing the motion images. With the help of a wearable sensor, a user's heart rate was also measured to estimate a metabolic rate. This study found that males showed higher MET than females, and the high BMI group generated higher MET than the low BMI group. The result also indicated that an estimated accurate range of 77%–89% was reasonably acceptable in the self-MET prediction modeling, while it was 65% in the third-party MET prediction. Therefore, the outcome of this research confirms that it is possible to use the Kinect sensor as a remote sensing device to estimate a user's metabolic rate, based on the use of a Deep Learning algorithm developed per individual.