NIDL: A pilot study of contactless measurement of skin temperature for intelligent building

• Human thermal comfort measurement plays a critical role in giving feedback signals for building energy efficiency. A contactless 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 contactless 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 0.476 °C and 0.343°C which is equivalent to accuracy improvements of 39.07 % and 38.76 %, respectively.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Samhällsbyggnadsteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Civil Engineering (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Samhällsbyggnadsteknik -- Husbyggnad (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Civil Engineering -- Building Technologies (hsv//eng)

Nyckelord

Contactless method

Thermal comfort measurement

Vision-based subtleness magnification

Deep learning

Intelligent building

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