Estimation of Thermal Sensation Based on Wrist Skin Temperatures

Sim, Soo Young; Koh, Myung Jun; Joo, Kwang Min; Noh, Samuel; Park, Sangyun; Kim, Youn Ho; Park, Kwang Suk

doi:10.3390/s16040420

Cited by 93 publications

(46 citation statements)

References 16 publications

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“…Various combinations of average sensor measurements on 26 points on the human body were explored and the average of 10 points were chosen as the most accurate measure (R-squared measure of above 0.9). Authors in [26] developed a wrist band that monitors skin temperatures from the wrist on several points such as the radial artery and ulnar artery 4 www.escholarship.org/uc/item/37d3q23w regions, and upper wrist) and the fingertip and collected data from eight subjects in different thermal sensation conditions. Accordingly, a thermal sensation estimation model based on the mean skin temperature, temperature gradient, time differential of the temperatures, and average power of frequency band were developed.…”

Section: Physiological Sensing Of Thermal Comfortmentioning

confidence: 99%

Infrared thermography of human face for monitoring thermoregulation performance and estimating personal thermal comfort

Ghahramani

Castro

Becerik-Gerber

et al. 2016

Building and Environment

197

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The common practice of defining operational settings for Heating, Ventilation and Air Conditioning (HVAC) systems in buildings is to use fixed set points, which assume occupants have same and static comfort requirements. However, thermal comfort varies from person to person and also changes due to climatic variations or acclimation, making it dynamic. In addition, thermal comfort in transient conditions are different from the steady state conditions, which makes the prediction of thermal comfort more difficult. Thus, thermal comfort has to be monitored over time. In this paper, we present a novel infrared thermography based technique to monitor an individual's thermoregulation performance and thermal comfort levels by measuring the skin temperature on several points on human face, which has a high density of blood vessels and is not usually covered by clothing. Unlike other methods, our method requires no continuous user input or interaction. Our results demonstrate that the monitored facial points behave differently under the heat and cold stresses and it can be explained based on the underlying vascular territories. We define two heuristics to describe the thermoneutral zone based on the observed behaviors and estimate thermal comfort for individuals with 95% confidence level. Considerable variations are observed in the thermoregulation performance and uncomfortably cool conditions metrics between the males and females. Females' thermoregulation system responses are less sensitive to the perception of warm conditions. However, similar behaviors are observed for uncomfortably cool conditions across genders.

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Section: Physiological Sensing Of Thermal Comfortmentioning

confidence: 99%

Infrared thermography of human face for monitoring thermoregulation performance and estimating personal thermal comfort

Ghahramani

Castro

Becerik-Gerber

et al. 2016

Building and Environment

197

View full text Add to dashboard Cite

show abstract

“…Along with the behavior-tracking, physiological signals, such as skin temperature [27][28][29][30][31], heart rate variability [32], electroencephalogram (EEG) [33], skin conductance [34], and accelerometry [35], show a strong relationship with human thermal sensation and comfort. Sim et al [36] developed personal thermal sensation models based on wrist skin temperature measured by wearable sensors. In addition, studies using more than one category are also not uncommon.…”

Section: Introductionmentioning

confidence: 99%

“…Even though all the above-reviewed studies claimed an enhanced prediction accuracy over conventional PMV and adaptive models, we identify three major drawbacks or limitations in those studies. First, subjects involved in the studies were restrained in a climate-controlled laboratory environment for a short period of time, usually in hours [36,39,40]. The dynamics of thermal comfort among daily diverse activities (e.g., dining, commuting, working, shopping) and their interactions cannot be fully captured in steady-state short-term lab tests.…”

Section: Introductionmentioning

confidence: 99%

Personal thermal comfort models with wearable sensors

Liu

Schiavon

Das

et al. 2019

Building and Environment

203

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A personal comfort model is an approach to thermal comfort modeling, for thermal environmental design and control, that predicts an individual's thermal comfort response, instead of the average response of a large population. We developed personal thermal comfort models using lab grade wearable in normal daily activities. We collected physiological signals (e.g., skin temperature, heart rate) of 14 subjects (6 female and 8 male adults) and environmental parameters (e.g., air temperature, relative humidity) for 2-4 weeks (at least 20 hours per day). Then we trained 14 models for each subject with different machine-learning algorithms to predict their thermal preference. The results show that the median prediction power could be up to 24% /78% /0.79 (Cohen's kappa/accuracy/AUC) with all features considered. The median prediction power reaches 21% /71% /0.7 after 200 subjective votes. We explored the importance of different features on the prediction performance by considering all subjects in one dataset. When all features included for the entire dataset, personal comfort models can generate the highest performance of 35% /76% /0.80 by the most predictive algorithm. Personal comfort models display the highest prediction power when occupants' thermal sensations is outside thermal neutrality. Skin temperature measured at the ankle is more predictive than measured at the wrist. We suggest that Cohen's kappa or AUC should be employed to assess the performance of personal thermal comfort models for imbalanced datasets due to the capacity to exclude random success.

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“…The results turned out that wrist was the most responsive area to infer thermal comfort with the lowest p-value. Moreover, Sim et al [13] established multiple linear regression models to estimate thermal sensations with variables based on skin temperature. As a result, the models using temperatures of the fingertips and wrist showed the highest accuracy.…”

Section: Background Of Thermal Models For Hvac System Of Office Buildmentioning

confidence: 99%

Thermal Comfort-Based Personalized Models with Non-Intrusive Sensing Technique in Office Buildings

Wang

et al. 2019

Applied Sciences

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Heating, ventilation and air-conditioning (HVAC) systems play a key role in shaping the built environment. However, centralized HVAC systems cannot guarantee the provision of a comfortable thermal environment for everyone. Therefore, a personalized HVAC system that aims to adapt thermal preferences has drawn much more attention. Meanwhile, occupant-related factors like skin temperature have not had standardized measurement methods. Therefore, this paper proposes to use infrared thermography to develop individual thermal models to predict thermal sensations using three different feature sets with the random forest (RF) and support vector machine (SVM). The results have shown the correlation coefficients between clothing surface temperature and thermal sensation are 11% and 3% higher than those between skin temperature and thermal sensation of two subjects, respectively. With cross-validation, SVM with a linear kernel and penalty number of 1, as well as RF with 50 trees and the maximum tree depth of 3 were selected as the model configurations. As a result, the model trained with the feature set, consisting of indoor air temperature, relative humidity, skin temperature and clothing surface temperature, and with linear kernel SVM has achieved 100% recall score on test data of female subjects and 95% recall score on that of male subjects.

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Estimation of Thermal Sensation Based on Wrist Skin Temperatures

Cited by 93 publications

References 16 publications

Infrared thermography of human face for monitoring thermoregulation performance and estimating personal thermal comfort

Infrared thermography of human face for monitoring thermoregulation performance and estimating personal thermal comfort

Personal thermal comfort models with wearable sensors

Thermal Comfort-Based Personalized Models with Non-Intrusive Sensing Technique in Office Buildings

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