A thermal comfort estimation method by wearable sensors

Mao, Haomin; Tsuchida, Shuhei; Kim, Yong-Beom; Kanada, Rintaro; Hori, Takayuki; Terada, Tsutomu; Tsukamoto, Masahiko

doi:10.1145/3412841.3441941

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…This paper presented a method to estimate subjective thermal comfort and objective thermal comfort through image data processed by the CNN model. And, since wearable sensors and environmental sensors are helpful for the estimation of human thermal comfort [3], we also verified how sensor data improves the estimation accuracy of human thermal comfort.…”

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confidence: 56%

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A Human Thermal Comfort Level Estimating Method Using Thermal Image and Sensor Data

Mao

Tsuchida

Suzuki

et al. 2021

The 23rd International Conference on Information Integration and Web Intelligence

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Recently, it has become a trend to construct a thermal environment with human thermal comfort. The advantage of human thermal comfort is that we could adjust the environment through the thermal sensation of human. Since human thermal comfort is influenced by several factors such as environmental temperature, environmental humidity, airflow, mean radiant heat, and so on, it is usually difficult to be directly evaluated. This paper proposes a method of estimating subjective thermal comfort and objective thermal comfort using CNN by RGB image data, thermal image data, and sensor data. We built a pipe-type booth in a room with a small air conditioner, two heaters, a humidifier, and a dehumidifier to acquired learning data, and we trained CNN under six different learning patterns to estimate thermal comfort. We found that image data is conducive to estimate subjective thermal comfort, and sensor data is conducive to estimating objective thermal comfort.CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing design and evaluation methods.

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confidence: 56%

“…Considering the convenience of utilization in daily life, we presented two methods of estimating human thermal comfort: using a small number of wearable sensors and using cameras. Since estimating human thermal comfort with a small number of wearable sensors has already been proposed in the reference [3], we only discuss the method with cameras in this paper.…”

mentioning

confidence: 99%

A Human Thermal Comfort Level Estimating Method Using Thermal Image and Sensor Data

Mao

Tsuchida

Suzuki

et al. 2021

The 23rd International Conference on Information Integration and Web Intelligence

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“…A PCM predicts the individual's thermal comfort response based on the collection of (i) direct feedback from occupants (thermal perception, preference, and comfort), (ii) physiological measurements, and (iii) environmental data, with the capability to adapt as new data is introduced to the model [13]. Recent studies [14][15][16][17] proposed a promising solution to predict the comfort of each occupant based on the usage of wearable sensors for physiological measurements paired with environmental sensors [18]. Despite their comfortable design, optimal for real-life application, wearable devices are more prone to collect artefacts, leading to increasing the risk of having less accuracy of the collected data [19].…”

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confidence: 99%

A novel approach based on EEG Entropy measurement for indoor human thermal comfort estimation

et al. 2023

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This paper presents a methodology for the application of electroencephalographic (EEG) Entropy measurements for indoor thermal comfort estimation. Wearables have been demonstrated to be capable of providing accurate physiological measurements to interpret individual thermal responses. Several studies demonstrated the correlation between the EEG Power Spectrum Density (PSD) variation and the subjects' responses exposed to different ambient temperatures. We present a complementary approach based on Approximate Entropy (ApEn) of EEG as a measure for the predictability of EEG series in describing the human thermal condition. We analysed the ApEn of EEG signals acquired from 24 subjects, exposed to three different temperatures (cold: 16°C; neutral: 25°C; warm: 33°C) in a controlled environment, by 4-channels wearable EEG sensors (256 Hz sampling frequency). Statistical analysis showed for both anterior frontal and temporoparietal sites significant differences between neutral, cold, and warm conditions, with a higher value of ApEn in the neutral one. In the anterior frontal area, there was a significative trend of ApEn with smaller values from the neutral to the warm condition, with the cold intermediate. The outcome opens the scenario up to innovative measurement systems, based on wearable EEG devices, for the application of personal comfort models to indoor environmental monitoring and control.

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