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 Can Heart Rate Variability (HRV) Be Used as a Biomarker of Thermal Comfort for Mine Workers?

2021 ◽  
Vol 18 (14) ◽  
pp. 7615
Author(s):  
Guoshan Wu ◽  
Heqing Liu ◽  
Shixian Wu ◽  
Guanglei Liu ◽  
Caihang Liang
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Thermal Comfort ◽  
Thermal Sensation ◽  
Low Frequency ◽  
Predictive Biomarkers ◽  
Strong Negative Correlation ◽  
Significant Difference ◽  
Low Frequency Power ◽  
Mine Workers

This study aimed to determine whether heart rate variability (HRV) can express the thermal comfort of mine workers. Eight subjects ran on a treadmill (5.5 km/h) to simulate heavy labor in three kinds of mining environments (22 °C/90%, 26 °C/90%, 30 °C/90%), respectively. Based on the measured electrocardiogram (ECG) data, the HRV of the subjects was calculated. The results showed that the HRV indices changed obviously under different temperature environments. In the neutral and hot environment, except for the LF, TP and LF/HF, there were significant differences in each index. However, there was no significant difference between the cold and neutral environments. The R-R intervals, the very low-frequency power (VLF), pNN20 and SampEN had strong negative correlation with the thermal sensation of people from sitting to work (ρ < −0.700). These indices may be used as thermal comfort predictive biomarkers of mine workers.

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.

scholarly journals Stress Markers for Mental States and Biotypes of Depression and Anxiety: A Scoping Review and Preliminary Illustrative Analysis

2021 ◽  
Vol 5 ◽  
pp. 247054702110003
Author(s):  
Megan Chesnut ◽  
Sahar Harati ◽  
Pablo Paredes ◽  
Yasser Khan ◽  
Amir Foudeh ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Skin Conductance ◽  
Neural Circuit ◽  
Current Knowledge ◽  
Mental States ◽  
Classification Model ◽  
Depression And Anxiety ◽  
Stress Markers ◽  
Physiological Markers

Depression and anxiety disrupt daily function and their effects can be long-lasting and devastating, yet there are no established physiological indicators that can be used to predict onset, diagnose, or target treatments. In this review, we conceptualize depression and anxiety as maladaptive responses to repetitive stress. We provide an overview of the role of chronic stress in depression and anxiety and a review of current knowledge on objective stress indicators of depression and anxiety. We focused on cortisol, heart rate variability and skin conductance that have been well studied in depression and anxiety and implicated in clinical emotional states. A targeted PubMed search was undertaken prioritizing meta-analyses that have linked depression and anxiety to cortisol, heart rate variability and skin conductance. Consistent findings include reduced heart rate variability across depression and anxiety, reduced tonic and phasic skin conductance in depression, and elevated cortisol at different times of day and across the day in depression. We then provide a brief overview of neural circuit disruptions that characterize particular types of depression and anxiety. We also include an illustrative analysis using predictive models to determine how stress markers contribute to specific subgroups of symptoms and how neural circuits add meaningfully to this prediction. For this, we implemented a tree-based multi-class classification model with physiological markers of heart rate variability as predictors and four symptom subtypes, including normative mood, as target variables. We achieved 40% accuracy on the validation set. We then added the neural circuit measures into our predictor set to identify the combination of neural circuit dysfunctions and physiological markers that accurately predict each symptom subtype. Achieving 54% accuracy suggested a strong relationship between those neural-physiological predictors and the mental states that characterize each subtype. Further work to elucidate the complex relationships between physiological markers, neural circuit dysfunction and resulting symptoms would advance our understanding of the pathophysiological pathways underlying depression and anxiety.

scholarly journals Empirical Model of Human Thermal Comfort in Subtropical Climates: A First Approach to the Brazilian Subtropical Index (BSI)

Atmosphere ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 391 ◽  
Author(s):  
João Gobo ◽  
Marlon Faria ◽  
Emerson Galvani ◽  
Fabio Goncalves ◽  
Leonardo Monteiro
Keyword(s):  
Thermal Comfort ◽  
Empirical Model ◽  
Thermal Sensation ◽  
Meteorological Data ◽  
Well Being ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Subtropical Climate ◽  
Human Thermal Comfort ◽  
The City

The bioclimatic well-being of individuals is associated with the environmental characteristics of where they live. Knowing the relationships between local and regional climatic variables as well as the physical characteristics of a given region and their implications on thermal comfort is important for identifying aspects of thermal sensation in the population. The aim of this study is to develop an empirical model of human thermal comfort based on subjective and individual environmental patterns observed in the city of Santa Maria, located in the state of Rio Grande do Sul, Brazil (Subtropical climate). Meteorological data were collected by means of an automatic meteorological station installed in the city center, which contained sensors measuring global solar radiation, air temperature, globe temperature (via a grey globe thermometer), relative humidity and wind speed and direction. A total of 1720 people were also interviewed using a questionnaire adapted from the model recommended by ISO 10551. Linear regressions were performed to obtain the predictive model. The observed results proposed a new empirical model for subtropical climate, the Brazilian Subtropical Index (BSI), which was verified to be more than 79% accurate, with a coefficient of determination of 0.926 and an adjusted R2 value of 0.924.

Heart rate variability at different thermal comfort levels

2008 ◽  
Vol 103 (3) ◽  
pp. 361-366 ◽  
Author(s):  
Weiwei Liu ◽  
Zhiwei Lian ◽  
Yuanmou Liu
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Thermal Comfort ◽  
Comfort Levels
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