Using electroencephalogram to continuously discriminate feelings of personal thermal comfort between uncomfortably hot and comfortable environments

Classification of pleasantness of wind by electroencephalography

Thermal comfort of humans depends on the surrounding environment and affects their productivity. Several environmental factors, such as air temperature, relative humidity, wind or airflow, and radiation, have considerable influence on the thermal comfort or pleasantness; hence, these are generally controlled by electrical devices. Lately, the development of objective measurement methods for thermal comfort or pleasantness using physiological signals is receiving attention to realize a personalized comfortable environment through the automatic control of electrical devices. In this study, we focused on electroencephalography (EEG) and investigated whether EEG signals contain information related to the pleasantness of ambient airflow reproducing natural wind fluctuations using machine learning methods. In a hot and humid artificial climate chamber, we measured EEG signals while the participants were exposed to airflow at four different velocities. Based on the reported pleasantness levels, we performed within-participant classification from the source activity of the EEG and obtained a classification accuracy higher than the chance level using both linear and nonlinear support vector machine classifiers as well as an artificial neural network. The results of this study showed that EEG is useful in identifying people's transient pleasantness when exposed to wind.

Thermal-Adaptation-Behavior-Based Thermal Sensation Evaluation Model with Surveillance Cameras

The construction sector is responsible for almost 30% of the world's total energy consumption, with a significant portion of this energy being used by heating, ventilation and air-conditioning (HVAC) systems to ensure people's thermal comfort. In practical applications, the conventional approach to HVAC management in buildings typically involves the manual control of temperature setpoints by facility operators. Nevertheless, the implementation of real-time alterations that are based on the thermal comfort levels of humans inside a building has the potential to dramatically improve the energy efficiency of the structure. Therefore, we propose a model for non-intrusive, dynamic inference of occupant thermal comfort based on building indoor surveillance camera data. It is based on a two-stream transformer-augmented adaptive graph convolutional network to identify people's heat-related adaptive behaviors. The transformer specifically strengthens the original adaptive graph convolution network module, resulting in further improvement to the accuracy of the detection of thermal adaptation behavior. The experiment is conducted on a dataset including 16 distinct temperature adaption behaviors. The findings indicate that the suggested strategy significantly improves the behavior recognition accuracy of the proposed model to 96.56%. The proposed model provides the possibility to realize energy savings and emission reductions in intelligent buildings and dynamic decision making in energy management systems.

A feasibility study on using fNIRS brain signals to recognize personal thermal sensation and thermal comfort conditions

BACKGROUND

Many studies have shown some relationships between thermal perception (including thermal sensation and thermal comfort) and human physiological parameters, such as brain signals. However, further research is still needed on how these parameters can help recognize the state of a human's personal thermal perception.

OBJECTIVE

This study aims to investigate the potential of using fNIRS brain signals to evaluate and predict personal thermal perception and cognitive performance in a steady-state temperature.

METHODS

The present study investigated changes in the fNIRS signal during ambient temperature manipulation. Thirty healthy young individuals were selected as the subjects, and they were exposed to two steady temperatures of 28.8 and 19 °C. After acclimatizing to either temperature, the oxy/deoxy-hemoglobin changes of the prefrontal cortex (PFC) were measured in both rest and cognitive task states using 16-channel fNIRS.

RESULTS

Results showed that exposure to different temperatures was significantly associated with the brain signals recorded during the task state. Many significant correlations were discovered between fNIRS signals and thermal perception indices. Furthermore, subjects' performance changes led to changes in the fNIRS signals. Logistic regression showed that fNIRS can determine whether a person is thermally comfortable or uncomfortable.

Real-time regulation of room temperature based on individual thermal sensation using an online brain-computer interface

Regulation of indoor temperature based on neurophysiological and psychological signals is one of the most promising technologies for intelligent buildings. In this study, we developed a system for closed-loop control of indoor temperature based on brain-computer interface (BCI) technology for the first time. Electroencephalogram (EEG) signals were collected from subjects for two room temperature categories (cool comfortable and hot uncomfortable) and used to build a thermal-sensation discrimination model (TSDM) with an ensemble learning method. Then, an online BCI system was developed based on the TSDM. In the online room temperature control experiment, when the TSDM detected that the subjects felt hot and uncomfortable, BCI would automatically turn on the air conditioner, and when the TSDM detected that the subjects felt cool and comfortable, BCI would automatically turn off the air conditioner. The results of online experiments in a hot environment showed that a BCI could significantly improve the thermal comfort of subjects (the subjective thermal comfort score decreased from 2.45 (hot uncomfortable) to 0.55 (cool comfortable), p < 0.001). A parallel experiment further showed that if the subjects wore thicker clothes during the experiment, the BCI would turn on the air conditioner for a longer time to ensure the thermal comfort of the subjects. This has further confirmed the effectiveness of TSDM model in evaluating thermal sensation under the dynamic change of room temperature and showed the model's good robustness. This study proposed a new paradigm of human-building interaction, which is expected to play a promising role in the development of human-centered intelligent buildings.

Analysis of the Relationship between Employee Health Level and Building Office Space and Environment

The arrival of the era of big data provides new research methods and perspectives. In view of the problem that the current building office space environment design does not consider the level of human health, which leads to the low comfort and the poor environmental quality of building office space, this paper puts forward the comfort analysis and environment design of building office space considering the health level of employees. By analyzing the theoretical basis of architectural office space by big data, the paper studies the elements and the esthetic characteristics of architectural office space, understands the development and classification of architectural office space, and obtains the esthetic characteristics of architectural office space. Considering the health level of employees, the factors affecting the comfort of building office space are analyzed. The research object is selected as a building office space; the method of questionnaire survey is used for the establishment of building office space comfort evaluation factor set; and from the two aspects of enterprise employees' psychological behavior and physical environment, the comfort of building office space of the research object is analyzed. The results of case study show that the proposed method has good environmental quality of building office space and can effectively improve the comfort of building office space.

Study of the Vertical Structures, Thermal Comfort, Negative Air Ions, and Human Physiological Stress of Forest Walking Spaces in Summer

Forest walking is a popular, healthy, and light outdoor activity. The potential comprehensive relationships between the vertical structures, thermal comfort, negative air ions (NAI), and human physiological stress in forest walking spaces have not been determined. We performed an experiment in the Baishuihe National Nature Reserve, Sichuan Province, China. Thirty-two college students recruited as subjects completed a forest walk (approximately one kilometer) on the same trail divided into three vertical structure type subsections, namely: A (dense herb and shrub layers with a sparse tree layer), B (dense tree, herb and shrub layers), and C (dense tree and herb layers with a sparse shrub layer). When the subjects passed preset environmental measurement points, staff measured climatic indexes (air temperature, relative humidity, wind velocity, surface temperature and global radiation) and NAI levels, and these data were input into the Rayman model to form a comprehensive thermal comfort index, the physiologically equivalent temperature (PET). PET and NAI differences and dynamic data among the subsections were analyzed. The subjects’ brain waves, heart rates (HRs), and walking speed (S) were digitally recorded. We selected brain wave θ, γ and β-high/α rates, neuroemotional indexes (stress and relaxation) and HR as physiological indicators, and S as an auxiliary indicator. The correlations between PET and NAI with physiological and auxiliary indexes were analyzed. Forest type C showed the lowest PETs and highest NAIs along with the most stable dynamic changes. PET was negatively correlated with HR and positively correlated with γ (12 channels). NAI was positively correlated with S and relaxation and negatively correlated with γ (two channels) and the β-high/α ratio (five channels). These comprehensive relationships suggest that dense tree, sparse shrub, and high-coverage herb layers combined with optimal temporal conditions (before noon or after a light rain) form the best thermal comfort and NAI conditions conducive to reducing human physiological pressures during summer daytime forest walking. These results provide theoretical references for forest walking and spatial regulation.

Investigating the relation between electroencephalogram, thermal comfort, and cognitive performance in neutral to hot indoor environment

The relation between electroencephalogram signals, thermal comfort, and cognitive performance in neutral to hot indoor environment was investigated. The experiments were carried out at four temperatures: 26ºC, 30ºC, 33ºC, and 37ºC, and two relative humidity levels: 50% and 70%. Thirty-two subjects were exposed for 175 min. The electroencephalogram signals were measured for 30 min 25 min after the onset of exposure while the recruited subjects performed neurobehavioral tests and rated their thermal comfort. The relative power of electroencephalogram signals has a significant correlation with thermal comfort and performance of neurobehavioral tests. The ratings of acceptability of thermal environment and thermal comfort, the speed, accuracy, and PI of completing the tests are negatively correlated with the relative power of δ-band, but positively correlated with θ-band, α-band, and β-band. The ratings of thermal sensation have a better correlation with the above four bands, but the correlation trend is opposite. A linear relation was found between electroencephalogram signals and the speed. The results showed that the relative power of P7 channel located in the occipital lobe is the most suitable as a single electroencephalogram channel to reflect joint thermal comfort and cognitive performance at high temperatures, especially its α-band.

Assessment of Personal Relaxation in Indoor-Air Environments: Study in Real Full-Scale Laboratory Houses

The relationship between chemical concentrations in indoor air and the human sense of comfort and relaxation have been reported. We investigated the effect of the sum of volatile organic compounds (ΣVOCs; sum of 79 VOCs) on the level of relaxation in two laboratory houses with almost identical interior and exterior appearances. The electroencephalogram (EEG) was monitored to evaluate the degree of personal relaxation objectively. The experiments were conducted in laboratory houses (LH) A and B with lower and higher levels of ΣVOCs, respectively. A total of 168 healthy volunteers participated, who each performed the task for 20 min, followed by a 10-min break, and EEG was measured during the break. Simultaneously as subjective evaluations, the participants were asked to fill a questionnaire regarding the intensity of odor and preference for the air quality in each LH. The subjective evaluation showed a significant association between ΣVOCs and participants’ relaxation (OR: 2.86, 95%CI: 1.24–6.61), and the objective evaluation indicated that the participants were more relaxed in the LH with lower levels of ΣVOCs than that with higher levels (OR: 3.03, 95%CI: 1.23–7.50). Therefore, the reduction of ΣVOCs and odors in indoor air would have an effect, which is the promotion of relaxation.