Human thermal comfort is evaluated based on the air temperature, humidity, air velocity, radiative temperature, clothing condition, and metabolic rate. Among these, the metabolic rate is determined by various human behaviors, such as sleep, rest, and exercise. It is difficult to measure the metabolic rate, however, using the conventional sensors, and expensive equipment is needed for precise measurement. Many studies have been conducted of late to come up with an easier method of predicting the metabolic rate. In this paper, a method of estimating the metabolism using a Kinect camera and artificial intelligence (AI), which can measure the metabolic rate easily, is proposed. In the experiment in this study, the subjects were made to assume the positions of lying down, sitting, walking, and running. The results of the measurement and prediction through AI are compared in this paper.
|Title of host publication||15th Conference of the International Society of Indoor Air Quality and Climate, INDOOR AIR 2018|
|Publisher||International Society of Indoor Air Quality and Climate|
|Publication status||Published - 2018|
|Event||15th Conference of the International Society of Indoor Air Quality and Climate, INDOOR AIR 2018 - Philadelphia, United States|
Duration: 2018 Jul 22 → 2018 Jul 27
|Name||15th Conference of the International Society of Indoor Air Quality and Climate, INDOOR AIR 2018|
|Conference||15th Conference of the International Society of Indoor Air Quality and Climate, INDOOR AIR 2018|
|Period||18/7/22 → 18/7/27|
Bibliographical noteFunding Information:
In this paper, a method of predicting the metabolic rate using a Kinect camera and artificial intelligence (AI) is proposed. The metabolic rate obtained from the heart rate measurement was in good agreement with the predicted value, but there was some difference at a high metabolic rate. If the number of experimenters and the learning time will be increased and made sufficient, the prediction of the indoor metabolic rate is expected to be more precise. ACKNOWLEDGEMENT This research was supported by Basic Science Research Program through a National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (NRF-2017R1A2B3012914). 5. REFERENCES ASHRAE. 1992. ANSI/ASHRAE Standard 55-1992. Atlanta: ASHRAE, Inc. Fanger PO. 1970. Thermal Comfort. Copenhagen: Danish Technical Press. ISO 8996 2004, Spitzer et al. Validation and calibration of physical activity monitors in children. Obesity, 10(3), 150-157. S.M.Ceesay et al. 1989. British Journal of Nutrition, vol.61, no 2,pp.175-786.
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