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

Non-invasive thermal comfort perception based on subtleness magnification and deep learning for energy efficiency