  Human thermal comfort measurement plays a critical role in giving feedback
signals for building energy efficiency. A non-invasive measuring method based
on subtleness magnification and deep learning (NIDL) was designed to achieve a
comfortable, energy efficient built environment. The method relies on skin
feature data, e.g., subtle motion and texture variation, and a 315-layer deep
neural network for constructing the relationship between skin features and skin
temperature. A physiological experiment was conducted for collecting feature
data (1.44 million) and algorithm validation. The non-invasive measurement
algorithm based on a partly-personalized saturation temperature model (NIPST)
was used for algorithm performance comparisons. The results show that the mean
error and median error of the NIDL are 0.4834 Celsius and 0.3464 Celsius which
is equivalent to accuracy improvements of 16.28% and 4.28%, respectively.
