A comparative study of predicting individual thermal sensation and satisfaction using wrist-worn temperature sensor, thermal camera and ambient temperature sensor

Aryal, Ashrant; Becerik-Gerber, Burçin

doi:10.1016/j.buildenv.2019.106223

Cited by 108 publications

(57 citation statements)

References 42 publications

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“…All rights reserved. 4 comfortable environments [7] with a recent report stating that only 38% of occupants are satisfied with indoor thermal environments. [8] Low satisfaction mostly arises from interindividual biological, and physiological variances leading to a difference in thermal preference.…”

Section: Introductionmentioning

confidence: 99%

Smart Textile‐Based Personal Thermal Comfort Systems: Current Status and Potential Solutions

Tabor

Chatterjee

Ghosh

2020

Adv Materials Technologies

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Thermophysiological comfort in humans is sought universally but seldom achieved due to biological and physiological variances. Most people in developed parts of the world rely on central heating and cooling systems to achieve thermophysiological comfort which is highly energy-intensive, inefficient, and rarely satisfactory. A potential solution to this issue is a wearable personal thermal comfort system (PTCS) consisting of textile-based temperature and moisture sensors, that can sense the environment and physiology of the wearer, combined with thermal and moisture responsive actuators, and/or heating/cooling devices to provide an individualized thermal environment. Moving thermal regulation away from the built environment to the microclimate surrounding the human body with the use of textiles has the This article is protected by copyright. All rights reserved. 2 potential to provide personalized thermal comfort and energy savings. Such a system may employ thermal comfort models and leverage the Internet of Things (IoT) and machine learning (ML) to understand individuals' comfort requirements in the current environment. Herein, the current state of textile-based active and passive thermophysiological comfort systems/technologies is summarized, including their environmental impact, major thermal comfort models, and factors influencing comfort (such as heat and moisture transfer). Also, active and passive textile-based devices (sensors, actuators, and flexible heating/cooling devices) that may be incorporated into a textile-based wearable PTCS are comprehensively discussed with an emphasis on their advantages, limitations, and future prospects.

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Section: Introductionmentioning

confidence: 99%

Smart Textile‐Based Personal Thermal Comfort Systems: Current Status and Potential Solutions

Tabor

Chatterjee

Ghosh

2020

Adv Materials Technologies

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“…This study focused on the evaluation of the actual temperature measurements because previous studies have shown the usefulness of skin temperature measurements from different ROIs on face for predicting thermal sensations with 75%-85% accuracy [3,6,23]. For the dataset used in this study, the accuracy of predicting thermal sensations from facial skin temperatures extracted using facial landmark detection resulted in an accuracy of 76%.…”

Section: Discussionmentioning

confidence: 99%

“…The accuracy improved to 85% when ambient air temperature was used in addition to facial skin temperature. Readers are referred to another paper by the authors [3] which focuses on predicting thermal sensations using the same dataset as this paper. One of the limitations of this study is that we did not compare the impact of the noise in temperature measurements on predicting thermal sensations.…”

Section: Discussionmentioning

confidence: 99%

“…Earlier approaches to building personal comfort models relied mostly on the measurement of environmental parameters such as air temperature and humidity, and utilized different ML algorithms to map the environmental measurements to individual thermal sensations [3,11,16]. However, some environmental parameters such as mean radiant temperature and air speed, which influence thermal comfort, are difficult to measure accurately due to lack of inexpensive and easy to use sensors.…”

Section: Introductionmentioning

confidence: 99%

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Skin Temperature Extraction Using Facial Landmark Detection and Thermal Imaging for Comfort Assessment

Aryal

Becerik-Gerber

2019

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

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Despite the large share of energy consumption, current HVAC systems in buildings fail to meet their primary purpose of maintaining comfortable indoor conditions. Current "one size fits all" approach to control the thermal conditions in an environment lead to a high degree of occupant dissatisfaction. Advancements in Internet of Things and Machine Learning have opened the possibility of deploying different sensors at a wide scale to monitor environmental and physiological information and using collected sensor data to model individual comfort requirements. Thermal imaging has recently gained interest as one of the possible ways to monitor physiological information (skin temperature) for thermal comfort assessment. Previous studies have shown that skin temperatures from different regions of the face, such as forehead, nose, cheeks and ears can provide useful information for predicting thermal sensation at an individual level. However, existing approaches to process thermal images either rely on manual temperature extraction or use methods that are less reliable in accurately identifying different facial regions. One of the major challenges of using thermal imaging for monitoring skin temperatures in actual buildings is that occupants may move relative to the camera. It is not practical to expect building occupants to be oriented facing the cameras at all times, therefore, it is important to be able to extract as much information as possible from instances where it is feasible to extract relevant information. In this paper, we describe an approach to extract skin temperature by locating specific regions of the face in thermal images. The approach involves combining data from RGB images with thermal images and leveraging facial landmark detection in RGB images. We also evaluate our approach with existing approach of face detection used in previous studies. Our study demonstrates that facial landmark detection provides a more accurate calculation of different locations in the face compared to previous studies. We show an improvement in overall quantity and quality of temperature measurements extracted from thermal images compared to previous studies. More accurate temperature measurements from thermal images can improve the accuracy of thermal imaging for modeling and predicting thermal comfort.

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“…Different technologies for indicating thermal comfort such as air temperature sensors, wristworn devices, and thermal cameras have been compared. (14) A high-tech temperature sensor to measure the temperature of exhaled breath and detect pathogenic progression has been proposed. (15) A flexible wearable thermoelectric nanogenerator made from Bi 2 Te 3 was proposed for use in next-generation wearable temperature sensors; this material can be easily incorporated into textiles.…”

Section: Trends In Temperature Sensorsmentioning

confidence: 99%

Development of Wearable Temperature Sensor Based on Peltier Thermoelectric Device to Change Human Body Temperature

Park¹,

Yazdi²,

Lee³

2020

Sensors and Materials

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We demonstrate a wearable temperature sensor based on a Peltier thermoelectric device. The wearable temperature sensor is attached to the wrist using a bracelet, which can monitor, control, and change the human body temperature using a local temperature change. The body temperature was changed by transferring the heat from one side to the other side of the Peltier semiconductor or vice versa. The feedback control system was used to maintain the human body temperature during increases and decreases in the temperature of the surrounding environment. The results showed the changes in body temperature over 300 s in 100 trials with different rates of increase and decrease in the temperature. The highest relative increase in temperature was 86.1% with a change from 18.7 to 34.8 °C, while the lowest relative increase was 1.2% with a change from 24.6 to 24.9 °C. The average change was 33.5% with a standard deviation of 25.5%. This technology provides a basis for next-generation wearable temperature sensors to control and change the temperature of the human body.

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A comparative study of predicting individual thermal sensation and satisfaction using wrist-worn temperature sensor, thermal camera and ambient temperature sensor

Cited by 108 publications

References 42 publications

Smart Textile‐Based Personal Thermal Comfort Systems: Current Status and Potential Solutions

Smart Textile‐Based Personal Thermal Comfort Systems: Current Status and Potential Solutions

Skin Temperature Extraction Using Facial Landmark Detection and Thermal Imaging for Comfort Assessment

Development of Wearable Temperature Sensor Based on Peltier Thermoelectric Device to Change Human Body Temperature

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