The authors have declared that no competing interests exist.
Indoor comfort is influenced by airflow direction, but subjective evaluations can differ. This study evaluates the airflow comfort with subjective assessments and physiological measurements, including skin temperature, electroencephalograms, and electrocardiograms. Nineteen participants entered a test room at 20°C after staying in a room at 32°C for acclimation. They were exposed to
Airflow direction, along with velocity, can influence the comfort level in a cooling environment [
The discomfort in indoor airflow velocities has long been researched, and the satisfaction varied depending on temperature, climate, and individuals [
To assess indoor pleasantness, including airflow comfort, subjective assessments or rating scales are widely used. A common international scale, the Predicted Mean Vote (PMV) [
Electroencephalography (EEG) is a strong candidate for the objective evaluation of airflow comfort. Some components of EEG signals are known to reflect mental states directly [
The main purpose of this study is to evaluate how airflow direction influences the comfort feeling in a cooling environment produced by air-conditioners using subjective and objective measurements, including skin temperature, EEG, and ECG. The relationship between the subjective evaluation and physiological indicators, especially EEG, is also investigated. The physiological responses were measured under two airflow directions: direct and indirect cool air to the face. For EEG analysis, beta and gamma activities were extracted as per our previous report [
We recruited 19 university students to participate in our experiments (female, n = 8, male, n = 11; mean age ± SD = 21 ± 2.1 years). All participants were without neurological deficits based on self-reports. The experiments were approved by the Joint Ethics Committee of the Faculty of Arts and Science and Center for Health Sciences and Counseling at Kyushu University (201815, 201815–1). Written informed consent was obtained from each participant. All methods were performed per the approved guidelines.
Due to a schedule conflict between participants and the experimental room, the experiments were conducted in two periods, from 21 January 2019 to 1 February 2019, and from 10 May 2019 to 23 May 2019 in the same experimental room at Mitsubishi Heavy Industries LTD., Aichi, Japan. The data measured in the two periods were pooled. Environmental parameters, including room temperature, wind direction, and wind velocity, were monitored and controlled simultaneously from a control room outside the experimental room.
The indoor condition was controlled using a commercially available air-conditioner with special panels (Draft Prevention Panel, Mitsubishi Heavy Industries Thermal Systems LTD., Tokyo, Japan) to avoid the direct impact of air from the indoor unit. Two different airflow conditions, see
a) Experimental environment. b) Sensor locations of the physical measurements and PMV meter. The thermography camera was located in front of the participants. c) Airflow directions of the two conditions, direct and indirect. d) Experimental procedure. The order of airflow settings was pseudo-randomized across participants. We obtained permission from the participants for printing the photographs of their faces.
To simulate the outside temperature, during the summer in Japan, each participant was asked to stay in a waiting room with the temperature set to 32°C for 20 min. This acclimation to hot temperature counteracted the effect of variations in outside temperature during the experiment period. The participant then moved to the experimental room, where the room temperature was set to 20°C. 20 min after the participant had entered the experimental room, physiological measurements and the experiment were started (
The participants wore an EEG headset in the experimental room and performed tasks under the different airflow conditions (
In the rest session, the participants were instructed to keep their eyes closed for 1 min. After resting, each participant answered the questionnaire about thermal sensation (1: very cold, 7: very hot), pleasantness (0: very unpleasant, 100: very pleasant), fatigue (0: best condition and no fatigue, 100: worst condition and cannot do anything with extreme fatigue), sleepiness (1: fully awake, 9: very sleepy), and anxiousness (0: not feel anxious at all, 100: feel extremely anxious). The thermal sensation and sleepiness were assessed with 7 and 9 step discrete scales, respectively, and others with horizontal visual analog scales with 10-cm lines.
The CA and CM sessions were designed to measure psychological time. In the CA session, each participant was required to press a button following time alerts every 10 s for 60 s. That is, they pressed the button six times in a session. In the CM session, the participants were asked to estimate 10 s as accurately as possible and press the button after counting silently for 10 s; this task was repeated six times (~ 60 s). The measured psychological time in the CA and CM sessions were analyzed as described in section 2.6.
In the Cal session, each participant was asked to consecutively subtract 13 from a four-digit number (e.g., 1012−13 = 999, 999−13 = …) as quickly as possible for 60 s. The four-digit number was randomly assigned, ranging from 1000 to 1012 for each participant.
To confirm the environment for physiological measurements of the participants, wind velocity and direction were measured using a multi-channel anemometer (Multi-channel anemometer 1550 series, KANOMAX JAPAN INC. Osaka, Japan) and probe cables (MODEL 1504 probe cable velocity channel, KANOMAX JAPAN INC. Osaka, Japan). The anemometer was set up at nine points (
White circles indicate the nine locations where the wind velocity and direction were measured using an anemometer. The nine locations were combinations of the three points of the front-back direction and the three heights from the floor. Gray silhouette indicates the sitting position of a participant, and the black circle indicates the location of the PMV meter. AC indicates the location of the air conditioner.
During the experiments, room temperature, relative humidity, and wind velocities were measured by a PMV meter (AM-101, Kyoto Electronics Manufacturing Co. LTD., Japan), placed close to the participant. For analyzing time-series changes, each parameter was segmented and averaged across three repetitions under each airflow condition, and two-way ANOVA (Airflow conditions × three repetitions) performed on the averaged data. The statistical analyses were conducted using JMP® 14SW (SAS Institute Inc., Cary, NC, USA).
Thermography data, to assess skin temperature, were measured before and after each condition (InfReC R550, NIPPON AVIONICS Co. LTD., Tokyo, Japan). In each thermographic image, the face area (178 × 140 pixels) was determined and cropped. The thermal data were averaged within the area, and two-way ANOVA was conducted (airflow conditions × before/after the measurement) for within-subject analysis.
The responses to the questionnaire, measured after resting, were analyzed using a two-way ANOVA for within-subject variance (airflow conditions × three repetitions). On finding an interaction effect, we analyzed the simple-main effect of the conditions separated by repetitions for multiple comparisons.
Psychological time has been discussed in relation to negative emotions, including boredom [
The psychological time is defined as the difference between the mean response durations of the CM and CA sessions. A positive psychological time indicates that the individual perceived a slow pace of time. Their response durations after counting for 10 s were averaged at each 60 s, that is, there were three averaged data for each participant and each condition. The mean durations in CM were subtracted from the corresponding CA durations. A two-way ANOVA was then conducted for within-subject analysis (Airflow conditions × three repetitions). We did not remove any data related to the psychological time for the analysis. Each participant responded 6 times in each block, that is, counted for 10 s 18 times in an airflow condition. We did not remove any outlier from the response durations.
We recorded EEGs across 19 channels per the international 10–20 system [
The measured EEG data were re-referenced to the averages of A1 and A2 and separated by task sessions to reduce the artifacts from ECG efficiently. The EEG data during each session of 60 s were segmented and filtered with a notch-filter of 60 Hz. The data were then transformed using a discrete Fourier transform (DFT) with a rectangular window to obtain the DFT coefficients. DFT analysis was performed using the ‘fft.m’ function in MATLAB (MathWorks, Inc., Natick, USA). Amplitudes of the frequency bin calculated from the DFT coefficients were averaged within the following frequency bands: beta (14 ≤
In addition to EEG and EOG channels, we recorded ECG using AIM Generation 2 (CGX, San Diego, U.S.A.). ECG was measured by the Bipolar Limb Lead method, which used a positive electrode at the left arm, and a negative electrode at the right with a reference electrode. These data were sampled at 500 Hz.
ECG data during each session, measured for 60 s, were segmented, detrended, and smoothed by a moving window. R-R intervals of each data were calculated by finding R-peaks. Then, DFT was employed for R-R interval data to obtain heart rate variability. Using the DFT components from R-R interval data, we calculated the mean components for HF (0.15 ≤
Before the physical and physiological measurements, we confirmed the wind velocity and direction under the two conditions, i.e., direct and indirect airflow (Figs
Distance | Location | Wind Velocity [m/s]/mean of 1 min: X | Wind Velocity [m/s]/mean of 1 min: Y | Wind Velocity [m/s]/mean of 1 min: Z | Wind Velocity [m/s]/mean of 1 min: composite |
---|---|---|---|---|---|
120cm | Front | 0.084 | 0.184 | −0.217 | 0.338 |
120cm | Participant | −0.029 | 0.030 | −0.050 | 0.126 |
120cm | Back | 0.029 | 0.026 | 0.039 | 0.091 |
60cm | Front | −0.006 | 0.041 | 0.037 | 0.151 |
60cm | Participant | −0.319 | 0.094 | −0.323 | 0.481 |
60cm | Back | 0.012 | 0.036 | −0.013 | 0.093 |
17cm | Front | −0.033 | -0.042 | 0.025 | 0.137 |
17cm | Participant | 0.043 | 0.143 | −0.084 | 0.208 |
17cm | Back | −0.133 | 0.166 | 0.019 | 0.225 |
Mean of 1-min wind velocity [m/s] in each direction and composite vector length under the direct condition at each measurement position.
Distance | Location | Wind Velocity [m/s]/mean of 1 min: X | Wind Velocity [m/s]/mean of 1 min: Y | Wind Velocity [m/s]/mean of 1 min: Z | Wind Velocity [m/s]/mean of 1 min: composite |
---|---|---|---|---|---|
120cm | Front | 0.034 | 0.024 | 0.043 | 0.087 |
120cm | Participant | −0.012 | 0.021 | 0.090 | 0.11 |
120cm | Back | 0.025 | 0.051 | 0.014 | 0.093 |
60cm | Front | 0.018 | 0.046 | 0.027 | 0.073 |
60cm | Participant | 0.0090 | 0.051 | 0.11 | 0.14 |
60cm | Back | −0.013 | −0.013 | 0.023 | 0.070 |
17cm | Front | −0.023 | 0.10 | 0.030 | 0.14 |
17cm | Participant | 0.016 | 0.081 | 0.030 | 0.10 |
17cm | Back | −0.16 | −0.15 | 0.035 | 0.23 |
Mean of 1-min wind velocity [m/s] in each direction and composite vector length under the indirect condition at each measurement position.
Room temperature, relative humidity, and air velocities near the participants were measured (
Mean and standard errors of a) room temperature, b) wind velocity, c) relative humidity, and d) predictive mean votes. The X-axis indicates three repetitions of the measurements. The U-shaped lines with asterisks show significant main effects of the airflow condition. (***: p < 0.001).
Direct | Indirect | F (main effect of the conditions) | p | |
---|---|---|---|---|
Room Temperature | 20 ± 0.091°C | 21 ± 0.082°C | 39 | <0.001 |
relative humidity | 57 ± 2.9% | 54 ± 2.6% | 1.9 | 0.17 |
wind velocity | 0.48 ± 0.029 m/s | 0.062 ± 0.0032 m/s | 682 | <0.001 |
Mean ± SEM of the physical measurements across the three repetitions, and the main effects of the airflow conditions and their p-values obtained by two-way ANOVA (Airflow conditions × three repetitions).
To compare the thermal pleasantness and sensation in the experimental room, we compared PMV scales, which were used to predict the mean of thermal comfort evaluations as the sum of environmental variables, metabolic rate, and the level of clothing insulation [
Under each airflow condition, the participants repeated the questionnaires, involving thermal sensation, pleasantness, fatigue, sleepiness, and anxiety, thrice. The mean scores were indicated by the conditions and repetitions, as given in
a) Thermal sensation, b) pleasantness, c) fatigue level, d) sleepiness, and e) anxiousness. The X-axis indicates three repetitions of the measurements. The U-shaped lines with larger asterisks show significant main effects of the airflow condition obtained by two-way ANOVA. The smaller asterisk denotes a significant difference in the airflow condition at each repetition, obtained by multiple comparisons. (*: P < 0.05, **: P < 0.01, ***: P < 0.001).
The thermal sensation and pleasantness under the indirect airflow condition were evaluated higher than that under direct airflow. The two-way ANOVA (airflow conditions × three repetitions) for within-subject analysis showed a significant main effect of the air condition in thermal sensation and pleasantness (thermal sensation:
The fatigue levels showed a significant main effect of repetition (
From the responses in the CA and CM sessions, the duration of psychological time under the indirect airflow condition was found to be longer across the three repetitions (
The Y-axis indicates the psychological time, which is the difference between the mean response time in CM and CA. The X-axis indicates three repetitions of the measurements. The U-shaped line with an asterisk shows significant main effects of the airflow condition obtained by two-way ANOVA. (*: P < 0.05).
Number of Subjects | Mean Duration in CM [sec] | Mean Duration in CA [sec] | Psychological Time (CM—CA) | Spearman-Brown reliability coefficient | |
---|---|---|---|---|---|
Direct | 19 | 10.1 | 10.0 | 0.18 | 0.93 |
Indirect | 19 | 10.6 | 10.0 | 0.77 | 0.78 |
Mean of CM duration, CA duration, psychological time (CM-CA), and reliability coefficients of the psychological time. The reliability coefficients were calculated by split-half method and the correlation coefficients were corrected by spearman-brown correction.
To evaluate the performance of mental calculation during the Cal session, the number of correct answers and the total mental calculations done by each participant were counted. A Chi-square test did not reject the null hypothesis that the correct rate was higher under one condition than under the other in each iteration (
At the beginning and end of each experiment, the skin temperature of each participant’s face was measured by a thermographic camera. The measured face temperatures under the indirect airflow condition were higher than under the direct airflow (
a) An example of face area selection. b) Mean and standard errors of the face temperatures. The Y-axis shows the face temperature, which was calculated by averaging the thermography data of a face area. Thes X-axis shows two time points, before and after the experiment. The U-shaped line with larger asterisks shows significant main effects of the airflow condition obtained by two-way ANOVA. The smaller asterisk shows the significant difference before and after each airflow condition from multiple comparisons. (**: P < 0.01, ***: P < 0.001).
In the EEG analysis, we focused on beta and gamma frequency bands since our previous study showed a relationship between these bands and airflow sensations [
In the rest session, the subtracted gamma amplitudes showed minimum value at F7 and maximum at C4 (
Gamma amplitudes during a) Rest and b) CM. The pop column shows the topographies of the mean difference between indirect and direct airflows. White letters and circles indicate the maximum and minimum electrodes of the subtracted amplitudes. The color bar shows subtracted amplitudes (μV). The middle column shows the mean and standard errors of the amplitudes at the maximum electrodes. The X-axis shows the repetition, and the Y-axis shows the μV of the EEG amplitudes. The bottom column shows the mean and standard errors at the minimum electrodes. The U-shaped lines with larger asterisks show significant main effects of the airflow condition obtained by two-way ANOVA. The smaller asterisk shows the significant difference in the airflow condition at each repetition, obtained by multiple comparisons. (**: P < 0.01).
In the CM session, the subtracted gamma amplitudes showed a minimum value at T4 and the maximum at T3 (
In the CA and Cal sessions, the gamma amplitudes did not show any significant main or interaction effects at the electrodes of maximum (Fp2 during CA, Fp1 during Cal) and minimum (Pz during CA, F7 during Cal) (
The beta amplitudes were analyzed in the same way as the gamma amplitudes (
The top column shows the topographies of the mean difference between indirect and direct airflows. White letters and circles indicate the maximum and minimum electrodes of the subtracted amplitudes. The color bar shows the subtracted amplitudes (μV). The middle column shows the mean and standard errors of the amplitudes at the maximum electrodes. The X-axis shows the repetition, and the Y-axis shows the μV of the EEG amplitudes. The bottom column shows the mean and standard errors at the minimum electrodes. The U-shaped line with asterisks shows significant main effects of the airflow condition obtained by two-way ANOVA. (**: P < 0.01).
In the beta amplitudes of the CA session, the maximum site was the T3 electrode. The beta values showed a significant main effect of repetition (
In the CM and Cal sessions, the amplitudes did not show any significant main or interaction effects at the electrodes of maximum (T3 during CM and Cal) and minimum (Pz during CM, and F7 during Cal) (
To investigate the airflow effects on the autonomic nervous system, parameters from ECG recordings were analyzed. HF reflects the parasympathetic activity, and LF reflects the modulation of sympathetic and parasympathetic activity [
During the CA session, HF under the indirect airflow condition was lower than that under direct airflow (
ECG parameters of HF and LF/HF during a) CA and b) Cal. The right row shows the mean and standard error of HF, and the left row shows that of LF/HF. The X-axis shows the repetition. The U-shaped lines with larger asterisks show significant main effects of the airflow condition obtained by two-way ANOVA. (*: P < 0.05).
In the Cal session, HF under the indirect airflow condition was lower than that under the direct airflow condition (
For the other sessions, the ECG parameters did not show any significant main and interaction effects (
The EEG activities, especially in the gamma band, have demonstrated many relations with various mental states or cognitive functions. To investigate the relevance of the observed EEG responses and indoor comfort, we conducted a correlation analysis with the subjective assessments and EEG amplitudes. In this analysis, the direct and indirect airflow conditions data were pooled to show the meaning of the EEG function, not to compare between the groups. The analytic frequency bands and electrodes were selected by the statistical significance of the two-way ANOVA (airflow conditions × three repetitions).
The gamma amplitudes and subjective assessments were averaged across the repetition in each session and analyzed for correlations. In the rest session, gamma amplitudes at F7 were negatively correlated with thermal sensation (
Yellow lines show the 95% density ellipse. Each point indicates individual mean data across three repetitions.
The beta amplitudes at F7 in the rest session were analyzed for correlation with subjective assessments (
Yellow lines show the 95% density ellipse. Each point indicates individual mean data across three repetitions.
In this study, we aimed to evaluate the effects of the environment, which were induced by different airflow directions, on indoor comfort with subjective assessments and physiological measurements. As expected, we confirmed that the subjective assessments and some physical measurements indicate higher pleasantness under the environment with indirect airflow. Furthermore, EEG and ECG activities showed different patterns between the airflow conditions. The gamma and beta amplitudes correlated with some subjective evaluations, including pleasantness and thermal sensation. The parameters of ECG, such as HF and LF/HF, were discussed in line with sympathetic modulation and parasympathetic activity, respectively. The different airflow environment could induce these differences in physiological responses, which are reflected in the airflow comfort levels.
From the physical measurements, we confirm that the indirect airflow maintained a mild room temperature and low wind velocity around the participants. The room temperature under the direct airflow condition was significantly lower, probably because of the higher wind velocity. A significant main effect occurred because there was a small variance in the room temperature due to the tight control of the environment. The difference in the room temperature was not large enough to influence the mental state or subjective pleasantness. The differences in indoor parameters modulated the dissociation of PMV between the airflow conditions. Furthermore, the different airflow directions influenced face temperature changes. The airflow hitting the face lowered the face temperatures under the direct condition, while these temperatures were maintained under the indirect condition. The large changes in face temperature may be one of the main factors that contributed to the pleasantness ratings discussed as follows.
The subjective assessments indicate that the indoor environment with indirect airflow was more pleasant than that with direct airflow. The room temperature around the participants was significantly varied between the airflow conditions, although the air-conditioner was set to the same temperature (20°C). The differences in subjective assessments and skin temperatures reflected the airflow conditions and room temperature, respectively. The participants evaluated that the environment under the indirect airflow condition was more pleasant and less cold than that under the direct airflow condition. This evaluation conformed with the scores of PMV. The mean pleasantness level was kept during the three repetitions under the indirect airflow condition, while it decreased with the repetitions under the direct airflow condition. Based on these results, we suggest that indirect airflow can maintain comfort feelings, while direct airflow reduces comfort feelings over time. The gradual decrease in face temperatures was similar to the time change of pleasantness ratings under the direct airflow condition, while the room temperatures did not show any time-series change. The lowering of face temperatures may contribute to the time-series dissociation of the pleasantness ratings between the airflow directions. Thus, skin temperature change can influence airflow comfort.
The duration of the psychological time was longer under the indirect airflow condition, and the results of the mental states supported the higher comfort under the indirect airflow condition. Our previous study has shown that the psychological time is longer in a no-airflow environment than under direct airflow exposure [
The EEG responses also supported the pleasantness of the indirect airflow. The beta and gamma amplitudes of EEG activity showed lower amplitudes under the indirect airflow condition than under the direct airflow condition. We have reported that EEGs of gamma and beta amplitudes were lower in a no-airflow environment than those under a directed airflow from an air-conditioner [
HF and LF/HF indicate a higher activation of the sympathetic nervous system under the indirect airflow condition during the execution of some tasks. Under the indirect airflow condition, HF showed lower responses during the CA and Cal sessions, and LF/HF also showed lower responses during the CA session. From the HF responses, we conclude that indirect airflow inhibited parasympathetic activity during task engagement in the CA and Cal sessions. Furthermore, the LF/HF responses indicated sympathetic nerve predominance under indirect airflow during the CA session. The differences in sympathetic/parasympathetic activity were not observed in the rest session but in the tasks to control external and internal attention. The results from the ECG measurements support that indirect airflow will induce a higher comfort in performing something that demands attention. These results can add different advantages from the EEG results that reflected comfort levels during relaxing.
In our experiments, the wind direction was changed as an independent variable, but the room temperature varied depending on the airflow condition. Therefore, we could not conclude that the observed responses were induced only by airflow sensation. Generally, it is very difficult to maintain the room temperature under different airflow directions in offices or schools using a commercial air-conditioner. Our experimental environment was closer to such general scenarios. However, more restricted controls will be necessary to reveal the physiological mechanism of airflow sensation in detail. Regarding the EEG analysis, we did not focus on other frequency bands like theta and alpha. Theta and other lower frequency bands can be influenced by various external noises; hence, we did not target lower frequency bands in the data measured near the indoor unit of the air-conditioner. For the alpha band, we previously reported that the amplitudes did not show differences under the different airflow environments for cooling [
This study investigated the comfort of airflow direction by subjective and objective measurements, including face temperature, EEG, and ECG. The subjective assessments showed a relatively higher thermal sensation and pleasantness under indirect airflow. The mean face temperature was maintained under the indirect airflow conditions, but significantly decreased under direct airflow. The face-temperature changes supported the difference of pleasantness ratings between the airflow conditions. The longer psychological time under indirect airflow indicates low stress levels. The EEG and ECG responses indicated the different airflow effects in each task. The gamma and beta EEG were inhibited under indirect airflow, and the amplitudes negatively correlated with subjective assessments like pleasantness and thermal sensation during the rest session. The EEG results suggested a higher airflow comfort under the indirect airflow condition for the resting state. The ECG reactions indicated a predominant sympathetic activity during the CA and Cal sessions. Thus, indirect airflow can contribute to a better producibility in an indoor environment. These results provide reliable evidence to evaluate the comfort of an indirect airflow. In this study, we suggest a higher comfort under indirect airflow to faces from several aspects. Our procedures and results emphasize the effectiveness of combined subjective and objective measurements to reveal various aspects of airflow comfort.
Gamma amplitudes during a) CA and b) Cal. The right row shows the topographies of the mean difference between indirect and direct airflow. White letters and circles indicate the maximum and minimum electrodes of the subtracted amplitudes. The color bar shows subtracted amplitudes (μV). The middle row shows the mean and standard errors of the amplitudes at the maximum electrodes. The X-axis shows the repetition, and the Y-axis shows the μV of the EEG amplitudes. The left row shows the mean and standard errors at the minimum electrodes.
(DOCX)
Beta amplitudes during a) CA, b) CM, and c) Cal. The right row shows the topographies of the mean difference between indirect and direct airflow. White letters and circles indicate the maximum and minimum electrodes of the subtracted amplitudes. The color bar shows subtracted amplitudes (μV). The middle row shows the mean and standard errors of the amplitudes at the maximum electrodes. The X-axis shows the repetition, and the Y-axis shows the μV of the EEG amplitudes. The left row shows the mean and standard errors at the minimum electrodes.
(DOCX)
ECG parameters of HF and LF/HF during a) Rest and b) CM. The right row shows the mean and standard error of HF, and the left row shows that of LF/HF. The X-axis shows the repetition.
(DOCX)
Gray lines show the 95% density eclipse. Each point indicates individual mean data across three repetitions. There was no significant correlation (
(DOCX)
Gray lines show the 95% density eclipse. Each point indicates individual mean data across three repetitions. There was no significant correlation (
(DOCX)
The authors would like to thank Prof. Taeko Ogawa, Assoc. Prof. Sachiko Kiyokawa, Prof. Hiroshi Ito, and Assoc. Prof. Shinji Yamagata for assistance with recruiting participants.
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Physiological and subjective comfort evaluation under different airflow directions in a cooling environment
PONE-D-21-07568
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PONE-D-21-07568
Physiological and subjective comfort evaluation under different airflow directions in a cooling environment
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