Establishment of a Thermal Comfort Model for Young Adults with Physiological Parameters in Cold and Hot Stimulation

Abstract

From the ASHRAE Global Thermal Comfort Database II, several researchers in East and South Asia utilized personal and environmental variables to establish the thermal comfort model. Body temperatures at several locations were the most utilized personal input. The collected papers from 2003 to 2022 were utilized to analyze the progressive development of the thermal comfort model by using VOSviewer. The results indicate that scant research discusses the relationship between multiple physiological parameters and thermal comfort index under dynamic environments and neutral thermal comfort threshold. Therefore, this study establishes the physiological thermal comfort model under cold and hot environments for young subjects in Asia. The results indicate that people are more sensitive to cold stimulation than hot due to the cold sensors of human skin closing to the surface. The human temperature-regulated mechanism operates spontaneously to manage heat conservation and dissipation during cold/hot stimulation. During cold/hot stimulations, the neutral thermal comfort threshold of three physiological parameters adjusts with the level and properties of the stimulation. For the TSV models established by the single physiological parameter, the forehead skin temperature had a closer relationship with TSV than the other two parameters. However, the TSV model established by the multiple physiological parameters is the closest one to TSV among them all. This information could benefit air conditioner manufacturers and household occupancy decision makers to select a better controlling strategy for air conditioners for saving air-conditioning electricity but not sacrificing dwelling comfort.

1. Introduction

To pursue a high-quality and comfortable life, air-conditioning products have become indispensable pieces of equipment in people’s houses. The power consumption of an air-conditioning system accounts for about 40–60% of a building’s energy consumption [1]. Therefore, making a comfortable environment and saving energy has become the improvement requirement for air-conditioner products. According to standard 55 of the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) [2], the comfort state is defined as “a psychological state of satisfaction for people in this environment”. Since feelings about the environment vary from person to person, it is impossible to satisfy everyone in the same environment. Therefore, the best thermal comfort environment can satisfy more than 80% of the occupants of the environment.

In 1970, the Danish scholar Fanger [3] published the equations of predicted mean vote (PMV) and percentage of dissatisfied (PPD) to express the human heat balance model, which is related to environmental temperature, humidity, wind speed, radiation temperature, activity, and clothing. The PMV-PPD models provided by Fanger are mostly related to environmental parameters and not much related to human physiological parameters. This measurement may result in misjudging thermal comfort value of the environment for the occupancies. A practical heat balance model comprises heat transfer between the human body and the environment as well as the heat balance of the human body [4], as shown in Equation (1). These human–environment heat transfer processes include metabolism (M) within the human body and heat transfer from inside to outside through blood, skin, and sweat. The human–environment transfer is mainly achieved through convective heat transfer (including natural and forced convection, C), radiative heat transfer (R), and evaporative heat transfer (E) and is sometimes achieved through conductive heat transfer. When the ambient temperature is lower than body temperature, the body loses heat, and body temperature decreases. Conversely, when the ambient temperature is higher, the body accepts heat from the environment, and body temperature increases. All these paths are related to human physiological parameters. However, the PMV-PPD models are not able to present them completely.

$$\mathsf{\Delta }\mathrm{H}=\mathrm{M}\pm \mathrm{R}\pm \mathrm{C}-\mathrm{E}$$

(1)

Because objective environmental factors cannot fully reflect the thermal comfort of the human body, the measurement of various physiological parameters and thermal comfort models have attracted the interest of researchers from around the whole world. For example, Wang et al. [5] in 2007 proposed that skin temperature measurements of fingers, hands, and forearms may be useful for monitoring and predicting human thermal status. In 2013, Sim et al. [6] monitored the skin temperature of fingertips and three wrists through a wearable device and compared the results with the thermal sensation voting values. They found that the correlation coefficient was 0.89 among them. In 2013, Takada et al. [7] proposed an equation to predict whole-body thermal sensation in an unsteady state based on the average skin temperature of the head, abdomen, forearm, hand, thigh, leg, and foot. In 2014, Maiti [8] combined the PMV model with skin-surface temperature and oral temperature to establish a new PMV model. The study found that the PMV model overestimated the actual subjective thermal response. In 2018, Wang et al. [9] developed an infrared thermal imaging device to monitor an individual’s face temperature and predict his thermal comfort level. Machine learning strategies were utilized to carry out data analysis for achieving the 80% satisfaction level under the ASHRAE standard. The previous research results indicated that the skin temperature of core bodies had a closer relationship with subjects’ thermal comfort than that of extremities.

In addition to the skin temperature, other physiological parameters were also attracting the attention of researchers. In 1948, Hertzman and Randall [10] compared the blood-flow rate in the skin due to regional differences. They indicated that 25–27% of total skin blood flow occurs at the head and hands, which only comprise 7% of the total skin surface. As the room temperature rose above 28 °C, the estimated skin blood flow increased linearly with the temperature rise. In 2018, Yang et al. [11] effectively integrated the differences in human skin temperature, skin conductivity, and heart rate under different indoor temperatures and established a thermal comfort model of multiple physiological parameters to overcome the shortcomings of subjective evaluation. In 2019, Vesela et al. [12] predicted local skin temperature and skin blood flow through a physiological parameter model and used the ThermoSEM software to perform foot blood flow and foot skin temperature tests under sitting, walking, and other activity conditions. They found that the accurate prediction of blood flow and local skin temperature was good for optimizing thermal comfort simulations in building environments. In addition to skin blood flow and heart rate, sweat evaporation has also been utilized by researchers. In 1980, Werner and Reents [13] investigated the human temperature-regulation mechanism through evaporative heat loss and skin blood flow. They indicate that the forehead, hand, and foot exhibit a higher rate of evaporation. In 2019, Shimazaki et al. [14] studied the relationship between human evaporative sweat and thermal sensation and calculated heat loss through static (standing) and dynamic (walking) activities. The experiment used questionnaires to assess human thermal perception, and it was found that human evaporative sweating and thermal environment assessment under rest, activity, and different environmental conditions can have good predictive effects. In 2020, Omidvar et al. [15] emphasized that the original PMV model could not predict accurate thermal sensation, so the calculation method of sweat evaporation heat loss in the original PMV model was modified to improve the prediction effect of the model. In the original PMV model, the perspiration heat loss was assumed to be zero for low-intensity (i.e., sedentary) people, and the sweat heat loss was oversimplified, resulting in structural flaws in the Fanger model, especially in hot environments. After accounting for the effects of environmental conditions (such as air temperature and humidity) on whole-body evaporative heat loss, skin moisturization was added and applied to the original PMV model, resulting in an improved version of the PMV model.

In previous studies, by incorporating physiological parameters into a comfort model, the thermal sensation can be closer to the actual feeling of the human body. In 2018, the authors [16] established a thermal comfort model by utilizing the relationship between sweating status, blood flow, three skin temperatures, and TSV under various cold wind stimuli. As for the effects of hot and cold environments on thermal comfort, Zhang et al. [17] presented a local comfort model of the body based on both local and whole-body thermal sensations. Nineteen individual body parts were heated or cooled to evaluate the coefficients of model, and the authors and validated the individual models using separate tests in an automobile testing facility. The local and averaged skin temperatures were compared with both local and whole-body thermal sensations by using the regress method. Nakamura et al. [18] investigated the relative contribution of the different local skin temperatures to thermal comfort by applying local temperature stimulation during whole-body exposure to mild heat or cold. They found that local cooling and warming of the neck can provide an effective way to reduce thermal discomfort during heat and cold exposure, respectively. Ji et al. [19] explored how a short-term thermal experience influences thermal comfort evaluation. Thermal experience from the previous thermal environment resulted in the formation of thermal sense memory in humans. A climate chamber was conducted for experiments on thermal comfort affected by previous thermal experience under cold and hot conditions.

From the ASHRAE Global Thermal Comfort Database II, there are 12 published papers in East and South Asia related to the investigation of personal thermal comfort, as shown in

Table 1. Thermal comfort studies in the ASHRAE Global Thermal Comfort Database II.

Authors	Data Location	Participants No.	Age Group	Input Personal	Input Environmental	Output
Guenther and Sawodny (2019) [20]	Singapore	18		0	Ti, fan level, To, GSR, time, day of week, variation of each parameter	Thermal sensation
Jayathissa et al. (2020) [21]	Singapore	30		NBTemp, HR, PrefH	Time, lighting, noise, Ti, RH	Thermal, visual, and aural comfort preference
Jiang and Yao (2016) [22]	China	20	20 s	MET, Clo	Ti, mTr, aSp, RH	Thermal sensation
Liu et al. (2007) [23]	China	113	20 s	0	Ti, RH, aSp, mTr	Thermal sensation
Lu et al. (2019) [24]	China	2	20 s	Clo SurfTemp, STemp, STemp difference	Ti, RH	Thermal sensation
Shan et al. (2020) [25]	China	3	20 s	STemp (wrist, neck)	0	Thermal sensation
Shan et al. (2018) [26]	Singapore	22		EEG	0	(Thermal) Mental state
Sim et al. (2016) [6]	South Korea	8	20 s	STemp (fingertip, radial artery, ulnar artery, upper wrist temperature)	0	Thermal sensation
Xu et al. (2018) [27]	China	4		0	Ti	Thermal sensation
Zhao et al. (2014) [28]	China	9		0	Ti, RH, mTr	Thermal sensation
Zhao et al. (2014) [29]	China	6 and 11	20–30 s	0	Ti, RH	Thermal complaint
Cheng et al. (2018) [16]	Taiwan	12	20 s	STemp (forehead, arm, and hands), SBF, Sweat	Ti, RH, aSp	Thermal sensation

Personal variables: STemp, skin temperature; Clo, clothing; NBTemp, near body temperature; MET, metabolic rate; HR, heart rate; SurfTemp, surface temperature; EEG, electroencephalogram; Pref, preference history. Environmental variables: Ti, indoor air temperature; RH, relative humidity; aSp, air speed; mTr, mean radiant temperature; To, outdoor air temperature; GSR, global solar radiation.

. Most of the data collection areas are from China, followed by Singapore, South Korea, and Taiwan. The participant number range of the selected studies is from 2 to 113 to develop personal comfort models. Half of the studies have up to 10 participants. This may be due to the long monitoring periods, repetitive surveys and feedback required, or continuous sensing of thermal comfort data collection processes. This process might have affected the subjects’ willingness to participate. Almost all the participants were students in their twenties and in healthy condition. The researchers wanted to avoid the influences of age, health conditions, physical disability, mental illness, and medication use. The used input variables included personal and environmental variables. For personal variables, the skin temperatures at several locations were the most utilized, followed by MET, Clo, HR, and EEG. Only the authors in the previous and this study utilized SBF and sweat variables to establish the thermal comfort model. For environmental variables, Ti and RH were the common variables, followed by To, fan level, aSp, mTr, GSR, time, lighting, and noise. The most used output variable was thermal sensation, followed by (thermal, visual, and aural) comfort preference, mental state, and thermal complaint.

To overview the progressive development of thermal comfort model-related research, the utilized keywords in the above-published papers were analyzed by using VOSviewer [30]. From the utilized 25 terms, 8 terms occurred at least two times. The resulting scientific landscape, clustered by co-occurrences of the keywords in these papers, is presented in Figure 1. Four clusters can be identified based on the published year, which are occupant survey and skin temperature, thermal sensation, thermal comfort, and thermal adaptation. Thermal comfort is the most central and largest one of these four clusters, indicating that thermal comfort is the goal of the research. The thermal sensation, including whole-body and local thermal sensations, were investigated by occupant surveys or by measuring the skin temperature. Wearable devices were also utilized for measuring physiological parameters. Recent studies are related to thermal adaption and regulation through heat transfer or dissipation. Several types of research focused on the local climate effect on thermal comfort, including India, China, the southeastern Mediterranean basin [31], Europe [32], etc. The non-steady-state effect on the thermal sensation was also investigated. Neutral thermal sensation [33], defined as when the subjects feel neutral regarding the thermal environment, and its temperature threshold [34] were investigated and proposed for acceptable comfort in air-conditioned buildings. However, scant research discusses the relationship between multiple physiological parameters and the thermal comfort index under dynamic stimulation and the neutral thermal comfort threshold. Therefore, this study established the physiological thermal comfort model under hot and cold environments for young subjects in Asia. The measured results of forehead surface temperature, skin blood flow, and sweat under hot and cold environments are compared with the thermal sensation voting values. The neutral thermal comfort threshold of multiple physiological parameters and how the threshold is affected by the stimulations are discussed. The thermal comfort models according to physiological parameters under cold/hot stimulations are established and verified.

2. Experiments

2.1. Location and Climate

The test location was at the National Taipei University of Technology, Taiwan (N25.042498, E121.534461). The testing season was from September to December 2019 in Taipei City, Taiwan, with the outdoor average temperature dropping from 27.3 to 19.1 °C, relative humidity 76.5 ± 2.5%, and wind speed from 2.5 to 3.2 m/s based on the meteorological data of the Taiwan Central Weather Bureau.

2.2. Experimental Chamber and Measuring Instruments

The utilized climatic chamber is located on the 6th floor of the Everlight Building on the campus of the National Taipei University of Technology, Taiwan. A picture of the climatic chamber is shown in Figure 2a. An air-conditioner can control the air temperature at 22 °C and the relative humidity between 60~65%. The settings of temperature and relative humidity in the climatic chamber were kept in the same conditions during the entire test. The physiological parameters of forehead skin temperature, skin blood flow (SBF), and sweat are presented in Figure 2b–d, respectively. The surface thermometer (SG 900, Gigarise, New Taipei City, Taiwan) with measuring range and accuracy of −50~180 °C and ±0.2%, respectively, measured the skin surface temperature. A laser Doppler flowmeter (LDF, moorVMS-LDF1, Moor Instruments, Axminster, UK) with an output signal bandwidth of 3 kHz was utilized to continuously measure the skin blood flow. A digital universal serial bus (USB) microscope (USB Web camera, Yukai, Taoyuan, Taiwan) with magnification and focusing range of 50~500 times and 0~40 mm, respectively, measured the sweat of the thumb surface. The indoor air temperature and humidity were measured in front of the subject’s chest by using the globe temperature meter (TM-188D, Tenmars, Taipei, Taiwan) with an accuracy of ±0.8 °C and resolution of 0.1 °C.

2.3. Testing Procedure

The testing procedure of two stages of cold/hot stimulation is shown in

Table 2. Experimental procedure of two-step cold/hot stimulation.

Process Time (min)	0~10	10~30	30~50	50~70	70~90
State of subject	Sedentary	Stimulation 1	Recovery 1	Stimulation 2	Recovery 2

, including one constant period, two stimulation periods, and two recovery periods. The constant period lasted for 10 min, and the rest of the periods lasted for 20 min. The subject was sedentary in the center of the chamber and performed typical office activities. The two-step cold/hot stimulations were designed to evaluate the relationship between the subject’s physiological conditions and the thermal comfort response. The cold stimulation level 1 was provided by a fan set at a speed of 2.42 m/s. After a recovery period of 20 min, cold stimulation level 2 of air at a speed of 3.83 m/s was applied to the subject. The conditions for hot stimulation were provided by a heater fan set at a speed of 0.26 m/s and temperature at 33 °C and 40 °C for levels 1 and 2, respectively. The settings of cold stimuli (air at 2.42 m/s and 3.83 m/s, which are the light and gentle breeze based on the Beaufort wind force scale, respectively) were the normal wind-speed conditions during autumn and winter, and those of the hot stimuli (air at 0.26 m/s with 33 and 40 °C) were the normal and hottest weather in cities of north Taiwan, based on the meteorological data of the Taiwan Central Weather Bureau.

2.4. Subjects

Based on the previous research, 10 subjects comprised the proper number to establish a personal thermal comfort model [35]. By using the open-source tool G*Power (version 3.1.9.7) [36] with settings of α at 0.1 and power at 0.8 under t-tests for linear multiple regression, the suggested sample size was 12. Therefore, 15 young adults were selected as the subjects in this study. The age range of selected subjects in this study was between 20 to 30 years old, with a mean age of 23.2 years old and standard deviation of 0.7 years. They wore T-shirts with a clothing thermal resistance of 0.4 clo. If the subjects suffered from skin disease, fever, or disorders or used skin medicinal products, they were excluded from the test. Before the experiment, the subjects were briefed on the purpose and procedure of this study. The chosen subjects were forbidden from applying any products that could affect the skin physiology at measuring locations. They were also prohibited from taking intense exercise or drinking coffee or wine. Every subject took one test per day during working hours.

2.5. Questionnaire Survey

During the test, the physiological variations of subjects were measured by installed equipment, and the thermal sensation vote (TSV) based on the seven-point ASHRAE thermal sensation scale was complete every minute. The seven-point ASHRAE thermal sensation scale includes hot (+3), warm (+2), slightly warm (+1), neutral (0), slightly cool (−1), cool (−2), and cold (−3).

2.6. Parametric Data Analysis

The measured physiological parameters of participated subjects and environmental variation were presented in the form of the mean value ($\overline{\mathrm{x}}$), and the differences between selected parameters and the estimated model are indicated by the root mean square error (RMSE) or standard deviation (σ). They are calculated based on the following equations [37,38]:

$$\overline{\mathrm{x}}=({\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{x}}_{\mathrm{i}})/\mathrm{n}$$

(2)

$$\mathrm{RMSE}=\sqrt{{{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{n}}{\left({\mathrm{x}}_{\mathrm{i}}-\overline{\mathrm{x}}\right)}^2/\mathrm{n}}$$

(3)

To convert the physiological parameters with various units into pure numbers to facilitate comparisons between variables, the normalization was performed in the following equation:

$$\mathrm{x}´=\frac{\mathrm{x}-\overline{\mathrm{x}}}{\mathsf{\sigma }}$$

(4)

The relationship between thermal sensation vote (TSV) and each measured physiological parameter was calculated according to the multiple regression method, as shown in the following equation:

$$\mathrm{y}={\mathsf{\beta }}_0+{\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathsf{\beta }}_{\mathrm{i}}{\mathrm{x}}_{\mathrm{i}}$$

(5)

where y is the thermal sensation vote, x_i is the ith measured physiological parameter, β_i is the ith regression coefficient, and β₀ is the offset term. The statistical relationship between y and x_i variables could be determined by the correlation coefficient marked as r, R, or Pearson’s r. The correlation coefficient could be calculated according to the following equation:

$$\mathrm{R}=[{\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{n}}\left({\mathrm{x}}_{\mathrm{i}}-\overline{\mathrm{x}}\right)\left({\mathrm{y}}_{\mathrm{i}}-\overline{\mathrm{y}}\right)]/[\sqrt{{\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{n}}{\left({\mathrm{x}}_{\mathrm{i}}-\overline{\mathrm{x}}\right)}^2}\sqrt{{\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{n}}{\left({\mathrm{y}}_{\mathrm{i}}-\overline{\mathrm{x}}\right)}^2}]$$

(6)

The adjusted coefficients of determination, marked as adjusted R² or ${\mathrm{R}}_{\mathrm{adj}}^2$, indicate how well the measured physiological parameters fit the regression line but adjust for the number of parameters in a model. It is calculated according to the following equation:

$${\mathrm{R}}_{\mathrm{adj}}^2=1-\left[\left(1-{\mathrm{R}}^2\right)\left(\mathrm{n}-1\right)\right]/\left(\mathrm{n}-\mathrm{k}-1\right)$$

(7)

where n is the number of data points, and k is the number of the physiological parameters. These correlations and analysis of variance (ANOVA) among multiple parameters were obtained by using Statistical Package for the Social Sciences (SPSS) Amos, version 25.0 (IBM, Armonk, NY, USA) [39].

3. Results and Discussion

To explore the effect of cold/hot stimulation on physiological parameters and thermal comfort and build a physiological thermal comfort model for estimating thermal sensation vote (TSV), the variations of physiological parameters are presented first. Then, the comparison and relationship between these physiological parameters and TSV are discussed. Finally, the estimated TSV models and their verification are provided under experimental conditions.

3.1. Physiological Parameters and TSV during Cold/Hot Stimulations

Environmental temperature; three physiological parameters including forehead skin temperature, skin blood flow (SBF), and sweat; and TSV were measured during the two-step cold/hot stimulation, as shown in Figure 3. Environmental temperature and three physiological parameters were recorded per second. To compare with TSV, they were presented every minute. Figure 3a–d show that under cold stimulation, while Figure 3e–h show that under hot stimulation. Environmental and forehead skin temperatures under cold/hot stimulations are shown in Figure 3a and Figure 3e, respectively. Skin temperature is a widely used physiological parameter to report thermal response under environmental variation and duration. In Figure 3a, the environmental temperature in front of the subject’s chest reduced from 21.6 °C to 20.9 and 20.8 °C, while the forehead skin temperature dropped from 33.6 °C to 30.9 and 30.5 °C under cold stimulation 1 and 2, respectively. During the recovery stage, the forehead skin temperature was able to return to 33.6 °C. In Figure 3e, the environmental temperature increased from 22.8 °C to 27.3 and 27.4 °C, while the forehead skin temperature raised from 32.9 °C to 33.6 and 34.0 °C under hot stimulation 1 and 2, respectively. During the recovery stage, the forehead skin temperature was able to return to 33.1 °C. One can notice that even though the environmental temperature dropped only 0.7 °C, the forehead temperature dropped up to 3.1 °C during the cold stimulation because of the air velocity. A similar effect could also be observed during the hot stimulation. The forehead temperature increased only up to 1.1 °C even though the hot air temperature increased by 4.5 °C. This information could benefit the air-conditioner factories and household occupancy to design a better controlling strategy for air conditioners for saving air-conditioning electricity but not sacrificing dwelling comfort.

The skin’s capacious volume provides rapid heat exchange between blood flow and the environment and also affects human thermal comfort significantly. Therefore, exploring the variation of SBF during cold/hot stimulations was one of our goals. SBFs under cold/hot stimulations are shown in Figure 3b and Figure 3f, respectively. In Figure 3b, the averaged SBF dropped from 132.23 PU to 34.0 and 19.79 PU under cold stimulation 1 and 2, respectively. In Figure 3f, the averaged SBF rose from 101.44 PU to 113.73 and 112.94 PU under hot stimulation 1 and 2, respectively. During the two recovery stages of cold and hot stimulations, the averaged SBF could not return to the beginning value. This may be due to short-term physiological adaptation [19]. The human body will maintain thermal regulation through shrinkage or expansion of blood vessels to adapt to the variations of the environment.

Sweat secreted from eccrine glands is evaporated by the heat released from the body to adjust the body temperature and avoid heat stress. Therefore, sweat is important in maintaining body’s thermal balance. Sweat areas under cold/hot stimulations are shown in Figure 3c and Figure 3g, respectively. In Figure 3c, the average sweat area dropped from 834.97 mm² to 700.28 and 715.63 mm² under cold stimulation 1 and 2, respectively. During the two recovery stages, the average sweat area was larger than the beginning value and reached 1201.80 and 1590.50 mm², respectively. In Figure 3g, the average sweat area rose from 921.83 mm² to 1159.74 and 3308.10 mm² under hot stimulation 1 and 2, respectively. During the two recovery stages, the average sweat area could not return to the beginning value and only dropped to 1310.83 and 1731.47 mm², respectively.

The subjects’ feeling about the thermal environment is called thermal sensation. In this study, based on ASHRAE standard 55, a seven-point thermal sensation scale was utilized in a response to the subject’s temperature sense during the two-step cold/hot stimulations. The mean values of the collected TSVs of 15 subjects were calculated [40], and the results are presented in Figure 3d and Figure 3h, respectively. In Figure 3d, the mean TSV dropped from 0 to −1.83 and −2.5 under cold stimulation 1 and 2, respectively. During the first recovery stage, the mean TSV returned to 0. In Figure 3h, the mean TSV rose from 0 to 1.2 and 1.7 under hot stimulation 1 and 2, respectively. During the two recovery stages, the mean TSV could return to 0.

To distinguish the effect of two-step cold/hot stimulations on the physiological parameters and TSV, the differences between the initial sedentary stage and stimulation level 1 as well as recovery stage 1 to stimulation level 2 of forehead skin temperature, SBF, and sweat area concerning TSV are presented in Figure 4a–c, respectively. In Figure 4a, the forehead skin temperature has a positive relationship with TSV with the increasing slope of 0.64 and 0.25 °C per TSV during cold and hot stimulation, respectively. This indicates that people are more sensitive to cold stimulation than to hot because the cold sensors of human skin are closer to the surface, and humans have more cold sensors than hot ones [41]. In Figure 4b,c, during cold stimulation, the blood flow is kept low through the shrinkage of the blood vessels for reducing heat loss. The evaporation heat loss through sweat is also reduced through the shrinkage of capillary pores. However, the sweat amount is reduced further along with the increased cold stimulation level due to the function of cold sensors. During hot stimulation, the blood flow increases with the level of hot stimulation to dissipate heat. In the hot stimulation level 2, the sweat area difference is 8.4 times larger than that in level 1, indicating that the human temperature-regulation mechanism dissipates heat through evaporative sweat and skin blood flow [13]. The temperature-regulation mechanism, through various physiological parameters, operates spontaneously to manage heat conservation and dissipation during cold/hot stimulation.

3.2. Neutral Adaptive Thermal Comfort Threshold

From Figure 3d,h, during the sedentary and two recovery stages, the zero TSV value represents the neutral thermal comfort periods. During these periods, three physiological parameters have various responses and threshold values under cold/hot stimulations. The average values of three physiological parameters at a steady state of TSV during the five stages are presented in

Table 3. Neutral thermal comfort threshold during two-step cold/hot stimulations.

Procedure		Sedentary	Stimulation 1	Recovery 1	Stimulation 2	Recovery 2
TSV	Cold	0	−1.83	0	−2.33	−0.33
	Hot	0	1	0	1.7	0
Tfh (°C)	Cold	33.54 ± 0.11	31.17 ± 0.20	33.46 ± 0.16	30.56 ± 0.02	33.50 ± 0.06
	Hot	32.72 ± 0.18	33.61 ± 0.03	33.27 ± 0.07	34.11 ± 0.15	33.24 ± 0.09
SBF(PU)	Cold	134.20 ± 11.65	32.52 ± 8.19	66.11 ± 7.10	19.61 ± 2.51	47.38 ± 6.01
	Hot	101.44 ± 16.31	102.93 ± 8.69	62.51 ± 16.16	105.60 ± 17.37	81.59 ± 17.48
ASW (mm2)	Cold	908.92 ± 253.87	770.68 ± 118.30	1404.35 ± 184.26	739.18 ± 111.03	1909.04 ± 80.23
	Hot	921.83 ± 169.92	1390.23 ± 167.13	1295.80 ± 268.31	3452.00 ± 204.30	1662.96 ± 334.66

. In Table 3, during cold stimulation, the TSV cannot return to 0 at the second recovery stage, indicating the subjects feel cold after two-step cold stimulation. However, the forehead skin temperature maintained the same value of 33.5 °C at these three stages. This indicates that, after two-step cold stimulation, to maintain neutral TSV, the forehead skin temperature needs to increase. During the hot stimulation, the TSV can return to a neutral value of 0 at the sedentary and two recovery stages. However, the forehead skin temperature increased from 32.72 to 33.24 °C, indicating the neutral thermal comfort threshold of forehead skin temperature increases 0.5 °C in this experimental setting. By increasing the indoor temperature setting sequentially, the human body will adjust to fit the warmer weather and save the air-conditioner electricity.

At the neutral TSV stages, the SBF decreased from 134.20 to 66.11 PU to reduce heat loss via blood during cold stimulation. After the cold stimulation of level 2, the SBF at the recovery stage decreased further. During hot stimulation, the SBF at three neutral TSVs decreased from 101.44 to 62.51 and 81.59, respectively. This indicates that the neutral thermal comfort threshold of SBF moves down during cold/hot stimulations in this experimental setting. The short-term thermal adaptation of SBF from a hot environment to a cold one is through the shrinkage of the blood vessels. The physiological response of the sweat area for stimulation is different from SBF. During cold/hot stimulation, at the neutral TSV stages, the sweat area increases with the stimulation level. The short-term thermal adaptation of the sweat areas from a cold environment to a hot one is through the expansion of sweat pores. One can notice that the forehead skin temperature at the neutral TSV stages varies less than the other two parameters due to the stable temperature maintained at the core body. Therefore, the neutral thermal comfort threshold of physiological parameters adjusts with the level and property of stimulation [42]. It is noticed that the responses of the subjects to the cold/hot stimulations include physiological and psychological [43] parts. In this study, we mainly discuss the responses of physiological parameters. The psychological response will be discussed in the future.

3.3. Estimated TSV Models by Physiological Parameters during Cold/Hot Stimulations

Based on the measured physiological parameters and personal thermal sensation vote (TSV), an estimated physiological thermal comfort model was developed. Their relationships among these parameters are presented first; then, several models based on adopted physiological parameters are listed. The relationships between three physiological parameters and TSV during the two-step cold/hot stimulation are shown in Figure 5. Figure 5a–c are under cold stimulation, while Figure 5d–f are under hot stimulation. All the physiological parameters were positively correlated with TSVs under the two stimulations. The red dashed line is the linear regression line between the physiological parameter and TSV. The relationships between forehead skin temperature and TSV under cold/hot stimulations are shown in Figure 5a and Figure 5d, respectively. The correlation coefficients between forehead skin temperature and TSV under cold/hot stimulations are 0.97 and 0.85, respectively. The relationships between SBF and TSV are shown in Figure 5b,e, and their correlation coefficients are 0.77 and 0.62, respectively. The relationships between sweat area and TSV are shown in Figure 5c,f, and their correlation coefficients are 0.61 and 0.67, respectively. For these two types of stimulations, three physiological parameters have higher correlation coefficients with TSV under cold stimulation. Among the three physiological parameters, the forehead skin temperature showed the highest correlation with TSVs. This information can provide air-conditioner manufacturers and smart controller developers with a useful indicator to save air-conditioner electricity.

To develop the estimated TSV model, 80% of the physiological information from the 15 subjects, including 5,510,160 datasets, was utilized, and the rest of the 1,377,540 datasets were used to validate the model. The physiological TSV models under the two-step cold/hot stimulations are listed in

Table 4. Estimated TSV models under the two-step cold stimulation.

No.	Physiological Parameters	Estimated TSV Model	Adjusted R2
1	Forehead Temperature	TSV = −1.0574 + 0.9326 × Tfh	0.95
2	SBF	TSV = −1.0574 + 0.7201 × SBF	0.56
3	Sweat	TSV = −1.0574 + 0.5981 × Asw	0.38
4	Forehead Temperature + SBF	TSV = −1.0574 + 0.833 × Tfh + 0.143 × SBF	0.96
5	Forehead Temperature + Sweat	TSV = −1.0574 + 0.985 × Tfh − 0.076 × Asw	0.95
6	SBF + Sweat	TSV = −1.0574 + 0.665 × SBF + 0.529 × Asw	0.86
7	Forehead Temperature + SBF + Sweat	TSV = −1.0574 + 0.7791 × Tfh + 0.1759 × SBF + 0.0462 × Asw	0.96

and

Table 5. Estimated TSV models under the two-step hot stimulation.

No.	Physiological Parameters	Estimated TSV Model	Adjusted R2
1	Forehead Temperature	TSV = 0.6174 + 0.6159 × Tfh	0.78
2	SBF	TSV = 0.6174 + 0.3901 × SBF	0.31
3	Sweat	TSV = 0.6174 + 0.4131 × Asw	0.34
4	Forehead Temperature + SBF	TSV = 0.6174 + 0.548 × Tfh + 0.16 × SBF	0.82
5	Forehead Temperature + Sweat	TSV = 0.6174 + 0.638 × Tfh − 0.031 × Asw	0.78
6	SBF + Sweat	TSV = 0.6174 + 0.327 × SBF + 0.355 × Asw	0.55
7	Forehead Temperature + SBF + Sweat	TSV = 0.6174 + 0.5458 × Tfh + 0.1613 × SBF + 0.0038 × Asw	0.82

, respectively. The multiple regression method was utilized to analyze the relationship between the model goal and physiological parameters. The utilized physiological parameters include (1) forehead skin temperature, (2) SBF, (3) sweat area, (4) forehead skin temperature and SBF, (5) forehead skin temperature and sweat area, (6) SBF and sweat area, and (7) forehead skin temperature, SBF, and sweat area. The seven-point thermal sensation vote was set as the goal of the model to indicate the thermal response of the subject. The adjusted coefficient of determination, adjusted R², is able to indicate the relationship between the model goal and physiological parameters. The adjusted coefficients R² of estimated TSV models in Table 4 under cold stimulation are larger than those in Table 5 under hot stimulation. For the TSV models established by the single physiological parameter, the adjusted R² of forehead skin temperature was larger than the other two parameters. For the TSV models established by the multiple physiological parameters, the adjusted R² of the TSV model utilizing all physiological parameters was the largest one, indicating the high correlation between TSV and utilized physiological parameters.

3.4. Verification of Estimated TSV Models under Cold/Hot Stimulations

In order to evaluate and compare the performance of each estimated TSV model in Table 4 and Table 5, 20% of the measured physiological datasets were utilized to calculate the estimated TSVs. Based on the seven estimated TSV models, the estimated TSVs and the actual TSVs under cold stimulation are illustrated in Figure 6. In Figure 6, all the estimated TSVs present a positive correlation with the actual ones. One can notice that the estimated model that utilized the forehead skin temperature presented low diversities between the actual TSVs and estimated ones. The mean root means square error (RMSE), residual sum of squares, and correlation coefficients (Pearson’s r) of the performance of various estimated TSV models under cold stimulation are listed in

Table 6. Performances of estimated TSV models under the two-step cold stimulation.

No.	Physiological Parameters	Mean RMSE	Residual of Square	Pearson’s r
1	Forehead Temperature	0.33	9.80	0.92
2	SBF	0.72	46.19	0.58
3	Sweat	0.83	60.99	0.36
4	Forehead Temperature + SBF	0.33	9.85	0.92
5	Forehead Temperature + Sweat	0.31	8.87	0.93
6	SBF + Sweat	0.69	42.61	0.62
7	Forehead Temperature +SBF + Sweat	0.30	8.01	0.94

. By comparing Figure 6 and Table 6, the estimated model utilizing the forehead skin temperature presented the higher accuracy among all the estimated TSV models. This indicates that the forehead skin temperature shows a higher correlation with TSV than other physiological parameters. The higher diversity of SBF and sweat models may be due to the variation of subjects’ skin and evaporation on the skin surface affected by the ambient environment. The estimated models utilizing the multiple physiological parameters have better accuracy than those utilizing the single physiological parameter.

For the case of hot stimulation, the seven estimated TSVs calculated from the models and the actual TSVs are illustrated in Figure 7. In Figure 7, even though all the estimated TSVs presented a positive correlation with the actual ones, their interrelated periods between the actual TSVs and the estimated ones were shorter than those under cold stimulation. Otherwise, from the cold stimulation, the diversities between the actual TSVs and the estimated ones utilizing the SBF and sweat were lesser. The mean root means square error (RMSE), residual sum of squares, and correlation coefficients (Pearson’s r) of the performance of various estimated TSV models under hot stimulation are listed in

Table 7. Performances of estimated TSV models under the two-step hot stimulation.

No.	Physiological Parameters	Mean RMSE	Residual of Square	Pearson’s r
1	Forehead Temperature	0.40	14.52	0.82
2	SBF	0.55	26.76	0.63
3	Sweat	0.46	18.83	0.75
4	Forehead Temperature + SBF	0.31	8.68	0.89
5	Forehead Temperature + Sweat	0.41	15.27	0.81
6	SBF + Sweat	0.39	13.90	0.82
7	Forehead Temperature + SBF + Sweat	0.31	8.60	0.89

. By comparing Figure 7 and Table 7, the estimated model utilizing the forehead skin temperature presented the higher accuracy among all the estimated TSV models. However, comparing Table 6 and Table 7, the accuracies of the estimated models utilizing sweat under hot stimulation were higher than those under the cold one. This indicates that the sweat under hot stimulation shows a higher correlation with TSV than the case under cold stimulation. In summary, under cold and hot stimulations, the forehead skin temperature equips a higher correlation with TSV than other physiological parameters. Therefore, the estimated models utilizing the forehead skin temperature have better accuracy than those utilizing other physiological parameters and the estimated models utilizing the multiple physiological parameters have better accuracy than those utilizing the single physiological parameter.

3.5. Limitations

Limitations in the applicability of these measured results lie in the properties of subjects and the climatic conditions of the selected area. In this study, the selected subjects were 15 young Asian students. These subjects were all between the age of 23 and 30 years old, with a mean age of 23.2 years old and standard deviation of 0.7 years. All of them were in healthy condition. The testing season was from September to December 2019 in Taipei City, Taiwan, with the outdoor average temperature dropping from 27.3 to 19.1 °C, relative humidity 76.5 ± 2.5%, and wind speed from 2.5 to 3.2 m/s. The built models in this study would be suitable for the selected group. The selected cold stimuli (air velocity at 2.42 and 3.83 m/s under 22 °C) are the normal temperature and wind-speed conditions during autumn and winter, and those of the hot stimuli (air velocity at 0.26 m/s under 33 and 40 °C) are the normal and hottest weather in cities of north Taiwan, based on the Taiwan Central Weather Bureau. The responses of the subjects to the cold/hot stimulations include physiological and psychological parts. In this study, the responses of physiological parameters and TSV of the selected group to cold/hot stimulations were discussed in the designated area.

4. Conclusions

In this study, 15 young subjects in Asia were selected to pass the two-step cold and hot stimulations for the establishment of a physiological thermal comfort model. Three physiological parameters, including forehead skin temperature, SBF, and sweat area, were collected under two-step cold and hot stimulations. Overall, 80% of the collected physiological data and TSV were used to establish the thermal comfort models, and 20% of those were used for model validation. The results indicate that people are more sensitive to cold stimulation than to hot because the cold sensors of human skin are closer to the surface and are greater in number than hot ones. The human temperature-regulation mechanism operates spontaneously to manage heat conservation and dissipation during cold/hot stimulation. During cold stimulation, blood flow and sweat are kept at a low amount for reducing the evaporation heat loss through the blood vessel and capillary pores and vice versa to dissipate heat during hot stimulation. During cold/hot stimulations, the neutral thermal comfort threshold of three physiological parameters adjusts with the level and property of stimulation. During the hot stimulation, the neutral thermal comfort threshold of forehead skin temperature increases by 0.5 °C. During cold/hot stimulations, the neutral thermal comfort threshold of SBF moves down, and the sweat area increases in this experimental setting. The forehead skin temperature at the neutral thermal comfort stages varies less than the other two parameters due to the stable temperature being maintained at the core body. For the TSV models established by the single physiological parameter, the forehead skin temperature had a closer relationship with TSV than the other two parameters. However, the TSV models established by the multiple physiological parameters are the ones closest to TSV among them all. The verification process also proves the estimation results. This information could benefit air-conditioner manufacturers and household occupancy decision makers to select a better controlling strategy of air conditioners for saving air-conditioning electricity but not sacrificing dwelling comfort.