  In human-centered intelligent building, real-time measurements of human
thermal comfort play critical roles and supply feedback control signals for
building heating, ventilation, and air conditioning (HVAC) systems. Due to the
challenges of intra- and inter-individual differences and skin subtleness
variations, there is no satisfactory solution for thermal comfort measurements
until now. In this paper, a non-invasive measuring method based on skin
sensitivity index and deep learning (NISDL) was proposed to measure real-time
skin temperature. A new evaluating index, named skin sensitivity index (SSI),
was defined to overcome individual differences and skin subtleness variations.
To illustrate the effectiveness of SSI proposed, two multi-layers deep learning
framework (NISDL method I and II) was designed and the DenseNet201 was used for
extracting features from skin images. The partly personal saturation
temperature (NIPST) algorithm was use for algorithm comparisons. Another deep
learning algorithm without SSI (DL) was also generated for algorithm
comparisons. Finally, a total of 1.44 million image data was used for algorithm
validation. The results show that 55.6180% and 52.2472% error values (NISDL
method I, II) are scattered at [0, 0.25), and the same error intervals
distribution of NIPST is 35.3933%.
