Non-invasive measuring method of skin temperature based on skin sensitivity index and deep learning

TL;DR

This paper introduces a non-invasive skin temperature measurement method using skin sensitivity index and deep learning, improving real-time thermal comfort assessment in intelligent buildings.

Contribution

It proposes a novel skin sensitivity index and deep learning frameworks for accurate, non-invasive skin temperature measurement, addressing individual differences and skin subtleness variations.

Findings

NISDL methods achieved over 55% accuracy within 0.25 error range.

Compared to NIPST, NISDL showed lower error distribution.

Deep learning effectively extracts skin features for temperature estimation.

Abstract

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%.