Thermal Adaptive Behavior-Recognition Model with Cross-Modal Knowledge Distillation

The traditional inference of thermal comfort relies mainly on either questionnaire surveys or invasive physiological signal monitoring. However, the use of these methods in real time is limited and they have a low accuracy; furthermore, they can cause an inconvenience to the daily work and life of indoor personnel. With the development of intelligent building technology, non-intrusive technology based on video analyses has gradually become a research hotspot. Not only does this type of technology avoid the limitations of traditional methods, but it can also be used to dynamically monitor thermal comfort. At present, the established and relatively complete non-intrusive recognition methods usually rely on additional equipment or cameras with specific angles, which limits their deployment and application in a wider range of scenarios. Therefore, in order to improve the non-intrusive prediction accuracy of the thermal comfort level of indoor personnel, it is necessary to establish a non-intrusive indoor personnel thermal comfort inference model. This study designed a cross-modal knowledge-distillation-based thermal adaptive behavior-recognition model. In order to avoid the difficulties of terminal deployment caused by the large model and the time-consuming nature of optical flow estimation, a multi-teacher network model was used to transfer the knowledge of different modes to a single student model. This reduced the number of model parameters and the computational complexity while improving the recognition accuracy. The experimental results show that the proposed vision-based thermal adaptation behavior-recognition model can non-invasively and accurately identify the thermal adaptation behavior of indoor personnel, which can not only improve the comfort of indoor environments, but also enable the intelligent adjustment of HVAC systems.