A Contactless Measuring Method of Skin Temperature based on the Skin Sensitivity Index and Deep Learning

Abstract

Featured Application: The NISDL method proposed in this paper can be used for real time noninvasively measuring human skin temperature, which reflect human body thermal comfort status and can be used for control HVAC devices.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 °C, 0.25 °C), and the same error intervals distribution of NIPST is 35.3933%.

A Systematic Literature Review of Non-Invasive Indoor Thermal Discomfort Detection

“…There is a lack of explanation behind the selection of the population samples in the processed studies. Additionally, some studies [33,37,[39][40][41]48,57] discuss in detail the limitations posed by the number of participants and that the number of people should be increased in order to make their code/models perform better in real life applications. In addition, the sex of participants is not discussed in detail.…”

“…ANN is a group of nodes connected with each other in a way that mimics brain behavior and function [60,65]. The deep neural network, which was used in the study by Cheng et al [40], represents a more complex architecture of neural network layers. Multiple layers were used to extract higher level features from the raw data.…”

Special Issue on Indoor Air Quality

“…In particular, authors presented an extension of Systems-Theoretic Accident Model and Process (STAMP) while considering physical processes in an indoor environment as temperature changes. Thermal comfort was also the subject of the study of Cheng X. et al [7] titled 'A Contactless Measuring Method of Skin Temperature based on the Skin Sensitivity Index and Deep Learning'. In this study, a skin sensitivity index was proposed to describe individual sensitivity of thermal comfort, and the index was combined with skin images for deep learning network training.…”

“…This low-cost wireless solution for indoor environment quality supervision incorporates mobile computing technologies for data consulting, easy installation, significant notifications for enhanced living conditions, and laboratory activities. Further, Cheng X. et al [7] study (presented in the previous paragraph) belongs to this sub-group as it included training of deep learning network. Finally, the study 'A Promising Technological Approach to Improve Indoor Air Quality' authored by Maggos T. et al [13] presents an innovative paint material which exhibits intense photocatalytic activity under direct and diffused visible light for the degradation of air pollutants, suitable for indoor use.…”

Vision-Based Contactless Pose Estimation for Human Thermal Discomfort

“…In order to solve the above problems, some scholars use the non-contact method to carry out relevant research on thermal comfort. Combining video magnification and deep learning, Cheng [16][17][18] tried to establish the relationship between the skin changes and skin temperature under different conditions, and proposed non-contact skin temperature detection methods to assess human thermal comfort.…”