Analyzing sleep thermal comfort with an attention-based gated recurrent unit neural network

To provide a comfortable sleep environment, the air conditioning controller needs a feedback mechanism associated with sleep thermal comfort. A thermal comfort prediction system is therefore needed. However, there is a dearth of research in this area. Recent advancements in wearable sensor technology and deep learning provide innovative solutions for monitoring sleep comfort. In this study, we propose a novel attention-based gated recurrent unit neural network (AGRU) designed for the prediction of sleep thermal comfort. This model leverages data from air environment monitors and wearable sensors capable of detecting environmental variables and physiological signals. We conducted an eighty-day sleep experiment involving twenty subjects exposed to varying environmental conditions. The monitors and sensors recorded seven features, including air temperature, relative humidity, skin temperatures at four points, and pulse rate. The results obtained underline the practicality and efficacy of the deep learning model that draws on environmental and physiological signals, with an average accuracy, macro-precision, macro-recall and macro-F1-score of 86.99 %, 87.10 %, 86.98 % and 0.87, respectively. This research provides substantial support for the continued advancement of smart home technology and wearable technology in the field of sleep thermal comfort.