Existing heating, ventilation, and air-conditioning systems have difficulties in considering occupants’ dynamic thermal needs, thus resulting in overheating or overcooling with huge energy waste. This situation emphasizes the importance of occupant-oriented microclimate control where dynamic individual thermal comfort assessment is the key. Therefore, in this paper, a vision-based approach to estimate individual clothing insulation rate (\(I_{\rm{cl}}\)) and metabolic rate (M), the two critical factors to assess personal thermal comfort level, is proposed. Specifically, with a thermal camera as the input source, a convolutional neural network (CNN) is implemented to recognize an occupant’s clothes type and activity type simultaneously. The clothes type then helps to differentiate the skin region from the clothing-covered region, allowing to calculate the skin temperature and the clothes temperature. With the two recognized types and the two computed temperatures, \(I_{\rm{cl}}\) and M can be estimated effectively. In the experimental phase, a novel thermal dataset is introduced, which allows evaluations of the CNN-based recognizer module, the skin and clothes temperatures acquisition module, as well as the \(I_{\rm{cl}}\) and M estimation module, proving the effectiveness and automation of the proposed approach.

现有的供暖，通风和空调系统难以考虑乘员的动态热需求，从而导致过热或过冷，并浪费大量能源。这种情况强调了以乘员为导向的微气候控制的重要性，其中动态个人热舒适性评估是关键。因此，本文采用一种基于视觉的方法来估计个人服装的隔热率（\（I _ {\ rm {cl}} \））和新陈代谢率（M），提出了评估个人热舒适水平的两个关键因素。具体而言，以热像仪为输入源，实现了卷积神经网络（CNN）以同时识别乘员的衣服类型和活动类型。然后，衣服类型有助于区分皮肤区域和衣服覆盖区域，从而可以计算皮肤温度和衣服温度。利用两种公认的类型和两种计算出的温度，可以有效地估算\（I _ {\ rm {cl}} \）和M。在实验阶段，引入了一个新颖的热数据集，该数据集可以评估基于CNN的识别器模块，皮肤和衣服温度获取模块以及\（I _ {\ rm {cl}} \）和M估计模块，证明了该方法的有效性和自动化性。