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

2019 ◽  
Vol 9 (7) ◽  
pp. 1375 ◽  
Author(s):  
Xiaogang Cheng ◽  
Bin Yang ◽  
Kaige Tan ◽  
Erik Isaksson ◽  
Liren Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Individual Differences ◽  
Thermal Comfort ◽  
Skin Temperature ◽  
Real Time ◽  
Saturation Temperature ◽  
Measuring Method ◽  
Sensitivity Index ◽  
Skin Sensitivity ◽  
Human Thermal Comfort

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 has not been any satisfactory solution for thermal comfort measurements until now. In this paper, a contactless measuring method based on a skin sensitivity index and deep learning (NISDL) was proposed to measure real-time skin temperature. A new evaluating index, named the skin sensitivity index (SSI), was defined to overcome individual differences and skin subtleness variations. To illustrate the effectiveness of SSI proposed, a 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 55.62% and 52.25% error values (NISDL method I, II) are scattered at (0 °C, 0.25 °C), and the same error intervals distribution of NIPST is 35.39%.

scholarly journals Effect of Landscape Microclimates on Thermal Comfort and Physiological Wellbeing

2019 ◽  
Vol 11 (19) ◽  
pp. 5387 ◽  
Author(s):  
Binyi Liu ◽  
Zefeng Lian ◽  
Robert D. Brown
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Thermal Comfort ◽  
Skin Temperature ◽  
Skin Conductance ◽  
Thermal Sensation ◽  
Global Climate ◽  
Evidence Base ◽  
Climate Conditions ◽  
Human Thermal Comfort

Global climate change and intensifying heat islands have reduced human thermal comfort and health in urban outdoor environments. However, there has been little research that has focused on how microclimates affect human thermal comfort, both psychologically and physiologically. We investigated the effect of a range of landscape microclimates on human thermal comfort and health using questionnaires and physiological measurements, including skin temperature, skin conductance, and heart rate variability, and compared the results with the effect of prevailing climate conditions in open spaces. We observed that in landscape microclimates, thermal sensation votes significantly decreased from 1.18 ± 0.66 (warm–hot) to 0.23 ± 0.61 (neutral–slightly warm), and thermal comfort increased from 1.18 ± 0.66 (uncomfortable–neutral) to 0.23 ± 0.61 (neutral–comfortable). In the landscape microclimates, skin temperature and skin conductance decreased 0.3 ± 0.8 °C and 0.6 ± 1.0 μs, respectively, while in the control, these two parameters increased by 0.5 ± 0.9 °C and 0.2 ± 0.7 μs, respectively. Further, in landscape microclimates, subject heart rate variability increased significantly. These results suggest landscape microclimates improve human thermal comfort and health, both psychologically and physiologically. These findings can provide an evidence base that will assist urban planners in designing urban environments for the health and wellbeing of residents.

scholarly journals A Deep Learning-Based Camera Approach for Vital Sign Monitoring Using Thermography Images for ICU Patients

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1495 ◽  
Author(s):  
Simon Lyra ◽  
Leon Mayer ◽  
Liyang Ou ◽  
David Chen ◽  
Paddy Timms ◽  
...  
Keyword(s):  
Deep Learning ◽  
Respiratory Rate ◽  
Skin Temperature ◽  
Real Time ◽  
Vital Sign ◽  
Low Cost ◽  
Vital Signs ◽  
Contactless Method ◽  
Detector Performance ◽  
Computational Performance

Infrared thermography for camera-based skin temperature measurement is increasingly used in medical practice, e.g., to detect fevers and infections, such as recently in the COVID-19 pandemic. This contactless method is a promising technology to continuously monitor the vital signs of patients in clinical environments. In this study, we investigated both skin temperature trend measurement and the extraction of respiration-related chest movements to determine the respiratory rate using low-cost hardware in combination with advanced algorithms. In addition, the frequency of medical examinations or visits to the patients was extracted. We implemented a deep learning-based algorithm for real-time vital sign extraction from thermography images. A clinical trial was conducted to record data from patients on an intensive care unit. The YOLOv4-Tiny object detector was applied to extract image regions containing vital signs (head and chest). The infrared frames were manually labeled for evaluation. Validation was performed on a hold-out test dataset of 6 patients and revealed good detector performance (0.75 intersection over union, 0.94 mean average precision). An optical flow algorithm was used to extract the respiratory rate from the chest region. The results show a mean absolute error of 2.69 bpm. We observed a computational performance of 47 fps on an NVIDIA Jetson Xavier NX module for YOLOv4-Tiny, which proves real-time capability on an embedded GPU system. In conclusion, the proposed method can perform real-time vital sign extraction on a low-cost system-on-module and may thus be a useful method for future contactless vital sign measurements.

scholarly journals Neural network-based thermal comfort prediction for the elderly

2021 ◽  
Vol 237 ◽  
pp. 02022
Author(s):  
JinJin Zhang ◽  
Hong Liu ◽  
YuXin Wu ◽  
Shan Zhou ◽  
MengJia Liu
Keyword(s):  
Neural Network ◽  
Thermal Comfort ◽  
Skin Temperature ◽  
Thermal Sensation ◽  
The Elderly ◽  
Learning Technology ◽  
Local Skin ◽  
Human Thermal Comfort ◽  
Network Algorithm ◽  
Neural Network Algorithm

Machine learning technology has become a hot topic and is being applied in many fields. However, in the prediction of thermal sensation in the elderly, there is not enough research on the neural network to predict the effect of human thermal comfort. In this paper, two neural network algorithms were used to predict the thermal expectation of the elderly, and the accuracy of the two algorithms was compared to find a suitable neural network algorithm to predict human thermal comfort. The dataset was collected from the laboratory study and included 10 local skin temperatures of the subjects, thermal perception voted at three temperatures (28/30/32°C), different wind speeds, and two forms of wind. Thirteen subjects with an average age of 63.5 years old were recruited for the subjective survey. These subjects sat for long periods of summer working conditions, wore uniform thermal resistance clothing, and collected votes on thermal sensation, as well as skin temperature. The results showed that the prediction accuracy of the two algorithms was related to the added influence factors, and the RBF neural network algorithm was the most accurate in predicting thermal sensation of the elderly. The main influencing factors were average skin temperature, wind speed and body fat rate.

Real-time human skin temperature analysis using thermal image recognition for thermal comfort assessment

2018 ◽  
Vol 158 ◽  
pp. 1063-1078 ◽  
Author(s):  
Henning Metzmacher ◽  
Daniel Wölki ◽  
Carolin Schmidt ◽  
Jérôme Frisch ◽  
Christoph van Treeck
Keyword(s):  
Thermal Comfort ◽  
Skin Temperature ◽  
Real Time ◽  
Human Skin ◽  
Image Recognition ◽  
Thermal Image ◽  
Temperature Analysis

Analysis of the Influence of Age on Human Thermal Comfort

ICCREM 2020 ◽  
2020 ◽  
Author(s):  
Boshuai Dong ◽  
Chunjing Shang ◽  
Ming Tong ◽  
Jianhong Cai
Keyword(s):  
Thermal Comfort ◽  
Human Thermal Comfort

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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