Application of wearable EEG sensors for indoor thermal comfort measurements

Multidomain comfort theories have been demonstrated to interpret human thermal comfort in buildings by employing human-centered physiological measurements coupled with environmental sensing techniques. Thermal comfort has been correlated with brain activity through electroencephalographic (EEG) measurements. However, the application of low-cost wearable EEG sensors for measuring thermal comfort has not been thoroughly investigated. Wearable EEG devices provide several advantages in terms of reduced intrusiveness and application in real-life contexts. However, they are prone to measurement uncertainties. This study presents results from the application of an EEG wearable device to investigate changes in the EEG frequency domain at different indoor temperatures. Twenty-three participants were enrolled, and the EEG signals were recorded at three ambient temperatures: cold (16 °C), neutral (24 °C), and warm (31 °C). Then, the analysis of brain Power Spectral Densities (PSDs) was performed, to investigate features correlated with thermal sensations. Statistically significant differences of several EEG features, measured on both frontal and temporal electrodes, were found between the three thermal conditions. Results bring to the conclusion that wearable sensors could be used for EEG acquisition applied to thermal comfort measurement, but only after a dedicated signal processing to remove the uncertainty due to artifacts.

Impact of the measurement uncertainty on the monitoring of thermal comfort through AI predictive algorithms

This paper presents an approach to assess the measurement uncertainty of human thermal comfort by using an innovative method that comprises a heterogeneous set of data, made by physiological and environmental quantities, and artificial intelligence algorithms, using Monte Carlo method (MCM). The dataset is made up of heart rate variability (HRV) features, air temperature, air velocity and relative humidity. Firstly, MCM is applied to compute the measurement uncertainty of the HRV features: results have shown that among 13 participants, there are uncertainty values in the measurement of HRV features that ranges from ±0.01% to ±0.7 %, suggesting that the uncertainty can be generalized among different subjects. Secondly, MCM is applied by perturbing the input parameters of random forest (RF) and convolutional neural network (CNN) algorithm, trained to measure human thermal comfort. Results show that environmental quantities produce different uncertainty on the thermal comfort: RF has the highest uncertainty due to the air temperature (14 %), while CNN has the highest uncertainty when relative humidity is perturbed (10.5 %). A sensitivity analysis also shows that air velocity is the parameter that causes a higher deviation of thermal comfort

Estimating Terrestrial Radiation for Human Thermal Comfort in Outdoor Urban Space

Cities inadvertently create warmer and drier urban climate conditions than their surrounding areas through urbanization that replaces natural surfaces with impervious materials. These changes cause heat-related health problems and many studies suggest microclimatic urban design (MUD) as an approach to address these problems. In MUD-related research, although terrestrial radiation plays an important role in human thermal comfort and previous studies use thermal comfort models to identify human heat stress, few studies have addressed the effect of terrestrial radiation. This study develops the ground ratio factor (GRF) model to estimate the different terrestrial radiation according to different ground conditions. Three types of ground materials (asphalt, concrete, and grass) were considered in the model, and field studies were conducted in humid subtropical climate (Cfa) zone during the hot season (13 July to 19 September 2020). The model was validated by comparing the predicated terrestrial radiation (PTR) from the model with the actual terrestrial radiation (ATR). The results showed that there is a statistically significant strong correlation between PTR and ATR. The model can contribute to MUD strategies by updating existing human energy budget models, which can lead to the measurement of more accurate human thermal comfort for mitigating thermal environments.

Thermal Comfort of the Environment with Internet of Things, Big Data and Machine Learning

This research proposes to evaluate the level of thermal comfort of the environment in real time using Internet of Things (IoT), Big Data and Machine Learning (ML) techniques for collecting, storage, processing and analysis of the concerned information. The search for thermal comfort provides the best living and health conditions for human beings. The environment, as one of its functions, must present the climatic conditions necessary for human thermal comfort. In the research, wireless sensors are used to monitor the Heat Index, the Thermal Discomfort Index and the Temperature and Humidity Index of remote indoor environments to intelligently monitor the level of comfort and alert possible hazards to the people present. Machine learning algorithms are also used to analyse the history of stored data and formulate models capable of making predictions of the parameters of the environment to determine preventive actions or optimize the environment control for reducing energy consumption.