Sweat Detection with Thermal Imaging for Automated Climate Control

and Individual Thermal Comfort in Vehicles

Diana Schif

, Ulrich Theodor Schwarz

and Holger Forst

BMW AG, Knorrstraße, Munich, Germany

Department of Physics, TU Chemnitz, Chemnitz, Germany

Keywords:

Sweat, Perspiration, Thermal Imaging, Thermal Comfort, Vehicle.

Abstract:

In addition to autonomous driving, the automation of comfort functions is currently one of the development

focuses of the automotive industry. In particular, the automation of the climate function is considered, as

manual operation often leads to distraction from the driving task. In order to implement this automation,

various data about the vehicle interior and the occupants are needed. Besides interior temperature, gender or air

speed, the sweat status of the occupants is relevant. In this work it is examined to what extent the sweat status

can be detected with the help of a thermal imaging camera. The aim is to show if it is possible to distinguish

the status not sweating, shortly before sweating and sweating using thermal imaging. For this the part of the

thermal image showing the forehead is analyzed. More speciﬁcally, the difference between minimum and

maximum temperature is compared for the different sweat statuses. At an ambient air temperature of 21 °C

the thermal comfort level and sweat status of 20 subjects is inquired and skin temperature is measured by a

thermal camera during sport activity. Results indicate that there is a signiﬁcant difference (p < 0.05) between

status not sweating and shortly before sweating and also between status not sweating and sweating. Sweat

can therefore be detected with the help of thermal imaging cameras. This result provides important input for

automated air conditioning. If sweat is detected for one or more occupants, then with the climate control a

corresponding regulation can take place to dry the sweat and to prevent further sweating.

1 INTRODUCTION

Autonomous driving is currently the focus of the au-

tomotive industry. But in addition to the autonomous

vehicle, the interior is more and more automated to

keep the overall value within the vehicle constant. So

the aim is to design and set an ideal interior for each

occupant. Functions such as the automatic seat ad-

justment, individual massage functions, and also the

independent adjustment of the climate control are de-

veloped. Especially when setting the air condition-

ing, the driver can easily be distracted from the road

and the driving task. Another problem with manually

handling the climate in the car is that a wrong tem-

perature tends to reduce the attention state (Dentel

and Dietrich, 2013), (van Hoof, 2008). Both results

in an increased risk for car accidents. To minimize

this risk, the climate control in vehicle should have a

personalized temperature, personalized air outlets and

personalized air ﬂow. Therefore an automated system

has to recognize the individual demand for thermal

comfort. Thermal comfort is a subjective sensation

and is deﬁned as the condition in which satisfaction is

expressed with the thermal environment (ASHRAE,

2004), (Epstein and Moran, 2006). To ensure this,

much information about the individual behavioral fac-

tors and also the interior of the vehicles, so called

environmental factors, has to be known. For the in-

cabin space, the ambient temperature, radiant temper-

ature, air humidity and air movement speed is relevant

(Fanger, 1970), (Epstein and Moran, 2006). Currently

all these environmental parameters are measured by

different sensors in the car. But to automate the com-

plete system, we also need information about the be-

havioral factors from the individual persons in the car.

These include:

• Age, Origin, Sex (Nakano et al., 2002), (Kar-

jalainen, 2007), (De Dear, 2004);

• Clothing (Fanger, 1970);

• Arousal Level, Emotion (Fanger, 1970), (Ebisch

et al., 2012), (Merla and Romani, 2007);

• Size, Weight (Fiala et al., 2001);

Schif, D., Schwarz, U. and Forst, H.

Sweat Detection with Thermal Imaging for Automated Climate Control and Individual Thermal Comfor t in Vehicles.

DOI: 10.5220/0009324004250431

In Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2020), pages 425-431

ISBN: 978-989-758-419-0

425

• Sweating (Fanger, 1970), (Djongyang et al.,

2010).

It is possible to detect most of the required data

like age, sex, clothing, and arousal with an RGB cam-

era or a time of ﬂight camera. But there is little in-

formation in literature about measuring methods for

sweat especially without direct contact to the human

skin. Sweating or perspiration is apart from shiv-

ering the main mechanism for regulating body core

temperature (Kuno, 1934). If the body core temper-

ature is too high, down-regulation of this tempera-

ture is the most important role of perspiration. Dur-

ing sweating, thermal energy is released by evapora-

tion of water from the surface of the skin, so that the

skin and also the body core temperatures are lowered.

Besides for regulating the body core temperature be-

cause of extreme ambient temperature or physical ac-

tivity, sweating can be stimulated by emotional stress

or spicy food (Kuno, 1934), (Wilke et al., 2007). The

cooling process on the skin is especially visible on

the human face, because of a high number of sweat

glands (Wilke et al., 2007). Hence the process of

sweating should be visible on the thermal image of

the face. In the vehicle a contact free measurement

is important to not interfere with the driver and the

other passengers. So placing a humidity sensor or a

thermocouple on the forehead is obviously not fea-

sible. Another possibility is to include these sensors

in the car seat. But then the sensor cannot measure

the temperature on the skin because it is covered with

clothes. In addition one goal of the automobile in-

dustry is to detect many attributes with one sensor,

instead of having specialized sensors for each func-

tion. Concerning this matter a camera for example

can not only detect sex, body parts, and emotions, it

can also detect temperature and sweat, especially with

a thermal camera. The thermal camera is measuring

the skin temperature with infrared thermography. In-

frared cameras generate thermal images by electro-

magnetic waves (Fern

andez-Cuevas et al., 2015). The

measured radiation is directly related to skin temper-

ature. The research question is to ﬁnd out if there is

a signiﬁcant difference on the thermal images during

not sweating, shortly before sweating and sweating.

The dependent variable is the sweat status and the in-

dependent variable is the facial skin temperature.

2 STUDY DESIGN AND METHOD

2.1 Subjects

14 male and 6 female volunteers between 20 and

45 years were recruited to participate in the experi-

ment. All subjects were free of any known cardiac

abnormalities. Verbal and written informed consent

was obtained from each subject. Subjects wore regu-

lar sportswear like a sport shirt, trainers and running

shorts or leggings.

2.2 Instrumentation

Subjects face skin temperature was measured with a

thermal camera (Thermal Expert Q1). The face was

chosen as a measuring point, as it is usually not cov-

ered by clothing. Especially in the forehead area

where you can ﬁnd a lot of sweat glands (Machado-

Moreira et al., 2008). Another positive aspect of fo-

cusing onto the forehead is that in most cases the area

is not covered by facial hair. The representation of

the thermal camera image is a false color image. In

this study we used a color map where colder tempera-

tures are represented as more red to purple colors and

higher temperatures are represented as more orange

and yellow. The camera was installed in a car’s rear

view mirror area. During the measurement the subject

was seated in a car, viewing to the camera and breath-

ing deeply while not talking. The measure interval

was 2.5 minutes (Sammito and B

ockelmann, 2015),

(Liu et al., 2011).

Besides of thermal image measurement, different

biofeedback indicators (HasoMed) were tracked. A

blood-volume-pulse sensor was attached to the left

hand and a skin conductance sensor on the right hand.

The biofeedback sensors were included in the mea-

surement for having a reference for sweating. In this

paper only the subjective sweat status from the ques-

tionnaire was used to analyze the possibility of sweat

measurement via thermal image and not the biofeed-

back data. Air temperature and air humidity were kept

as stable as possible throughout the experiments.

2.3 Experimental Protocol

The experiment was performed in a climate chamber

with 21 °C and approximately 50 % air humidity dur-

ing May to July. Each subject needed 1.5 hours for

the experiment. The experiment was done for one or

maximum two subjects on a single day.

Before the experiment was started, the subject

had 15 minutes time to accommodate to the climate

chambers temperature. During this time the subject

changed clothes and ﬁlled out the letter of consent.

After that, the main part of the study started. First

question for each participant was about thermal com-

fort with ASHRAE scale. ASHRAE scale is a 7-point

scale used for occupant thermal sensation vote. The

scale is based on a measure how cold or how hot the

VEHITS 2020 - 6th International Conference on Vehicle Technology and Intelligent Transport Systems

426

subjects feel. Subjects can choose between cold, cool,

slightly cool, neutral, slightly warm, warm, and hot.

In literature a sensation vote of cold, cool, warm and

hot is connected with uncomfortable feeling in the

ambient condition (Liu et al., 2011). Whereas a sen-

sation vote of slight cool, neutral, and slightly warm

represents a comfortable ambient condition. The ther-

mal comfort question was followed by the question

about individual sweat status. The sweat status was

differentiated between not sweating, shortly before

sweating and sweating. The scale was a modiﬁcation

from the questionnaire by (Nielsen et al., 1989). Af-

ter the questions the ﬁrst measurement started. The

subjects were seated in a BMW 7 series on drivers

place. After the ﬁrst sensor measurement the subjects

got out of the car and started a high intensive interval

training (HIIT) for 1 minute and 50 seconds. Then

the thermal comfort and the sweat status were queried

again. If the sweat status changed, the subject had to

go back to the car seat to measure the second sweat

status (shortly before sweating). If the status didn’t

change, another HIIT part with 1 minute 50 seconds

was started. This was repeated until the sweat status

sweating was accessed. Then a last measurement in

the car was made and the study was ﬁnished. The

work resulted in a collection of 60 recordings with

three sweat status recordings for each of the 20 par-

ticipants.

2.4 Method

To ﬁnd out a signiﬁcant difference between the differ-

ent perspiration statuses, the face was recorded with

the thermal camera. For each of the 60 recordings a

part of the forehead area was cut from the image and

analyzed for speciﬁc values like the minimum temper-

ature in the area, the maximum temperature, and the

average value. The minimum temperature is impor-

tant as it represents the cooling process of sweating.

Sweating causes perspiration to come out of the sweat

glands on the forehead. The sweat, which mainly con-

sists of water (Kuno, 1934), cools directly when evap-

orating. This has a cooling effect on the body core

temperature. In addition, the cooling effect is directly

visible on the thermal image. The maximum values

on the forehead vary less strongly due to the perspi-

ration status. This increases the difference between

the minimum and maximum temperature values due

to the onset of sweating.

3 RESULTS

First, the results of the subjective perspiration status

and the associated thermal comfort vote are evaluated.

The results show that with increasing sweating the

condition is described as less comfortable. When not

sweating, most subjects feel more comfortable than

with the status shortly before sweating and sweating.

Figure 1 illlustrates this fact.

slightly cool

neutral

slightly warm

warm

hot

thermal comfort vote

frequency

not sweating

shortly before sweating

sweating

Figure 1: Thermal Comfort with Different Sweat Status

of the Individual Subjects. When Sweating Is Starting the

Comfort Level Tends to Be More Warm and Hot. Without

Sweating the Comfort State Is More Neutral and Comfort-

able.

Next, the recorded thermal images are evaluated

in detail. The subjects came on average after 2.1 sport

intervals to the status shortly before sweating and af-

ter 4.6 intervals to the status of sweating. Some par-

ticipants sweat already after the second sports inter-

val and others after the seventh. The subjects most

often start to sweat after the sixth sports interval. This

shows that the onset of sweating is individually differ-

ent, which is consistent with results from the literature

(Kuno, 1934). The thermal images show that there is a

visible difference in temperature depending on sweat

status. Not sweating forehead areas have a lower tem-

perature, visible as a more yellow color on the false

color rendering and the sweating forehead areas have

a warmer and more blotchy temperature distribution

as seen in ﬁgure 2. This is related to the high number

of sweat glands on the forehead (Machado-Moreira

et al., 2008),(Thomson, 1954).

The average values over all subjects for the min-

imum, maximum and average temperature for each

sweat status are shown in table 1.

On the sweating forehead image some locations

are still yellow caused by the less strong variation of

the maximum temperature. This is because the distri-

Sweat Detection with Thermal Imaging for Automated Climate Control and Individual Thermal Comfort in Vehicles

427

Figure 2: Thermal Images of Face during Study. From Left to Right: Status “not Sweating”, “shortly before Sweating” and

“sweating”. the Color on the Forehead Changes from Yellow to More Orange and Red. This Represents the Cooling Process

Caused by the Sweating.

Table 1: Minimum, Maximum and Average Temperature Values in Forehead Area during the Different Sweat Statuses.

Not sweating Shortly before sweating Sweating

Min

in [°C] 33.66 31.20 30.82

Max

in [°C] 35.08 33.66 33.23

Avg

in [°C] 34.41 32.45 32.19

bution of the sweat glands on the head and thus also

on the forehead is individual and not uniform (Ran-

dall, 1946). So the difference between minimum and

maximum temperature in this area is a good indica-

tor for sweating. The bigger the difference, the more

sweat is on the humans face. Because of that, the

difference between maximum and minimum forehead

area temperature is analyzed for a signiﬁcance test.

The next table 2 shows the differences for each sweat

status.

To ﬁnd out whether there is a signiﬁcant difference

on the thermal image between the different sweat sta-

tuses a 1-way analysis of variance (ANOVA) for re-

peated measures is used. The SPSS program is em-

ployed to make the statistical analysis and alpha is set

at < 0.05 level.

The variance analysis with repeated measure-

ments (assumed sphericity: Mauchly-W (2) = .844,

p = .218) shows that the sweat status is related to

the temperature difference on the forehead (F (2.28)

= 51.656, p = .00, η

= .731, n = 20). The η

says that 73.1% of the variation between the sweating

states can be explained via the temperature difference.

Bonferroni-corrected pairwise comparisons show that

the sweat status not sweating (M = 1.43, SD = 0.32) is

signiﬁcantly higher than shortly before sweating (M =

2.47, SD = 0.60) and sweating (M = 2.69, SD = 0.60).

It can be seen that the temperature difference between

not sweating and shortly before sweating (p < .05)

and not sweating and sweating (p < .05) differ signif-

icantly. In contrast, the temperature difference does

not differ signiﬁcantly between these two measuring

times shortly before sweating and sweating (p = .46).

The effect size f according to Cohen (1988) is 1.65

and corresponds to a strong effect. In order to con-

sider the unexplained variance and to check whether

another factor inﬂuences the change in the tempera-

ture difference, the residuals and their distribution are

considered. The histogram of the residuals is shown

below in Figure 3, including a normal distribution

curve (red). The residuals are normally distributed,

as assessed by the Kolmogorov-Smirnov test showed

(α = .01, p = .03). It is therefore assumed that there is

no missing variable that has an inﬂuence on the cal-

culation. This means that no data transformation has

to be carried out.

The biofeedback data is used as a kind of refer-

ence to the subjective perspiration status. The evalua-

tion of the skin conductance data indicate an increase

with increasing sweating. From the measurement of

the blood volume pulse sensor, heart rate variability

(HRV) data is considered. In particular, the time-

based RMSSD value and the frequency-based LF/HF

rate is evaluated. RMSSD is the square root of the

mean squared differences of the successive heartbeat

histogram for residuals

-1 -0.5 0 0.5 1

residue temperature difference

frequency

Figure 3: Histogram for the Residuals with Normal Distri-

bution. It Can Be Seen, That the Residues Follow a Nor-

mal Distribution. A Kolmogorov-Smirnov Test with α =

.01 Also Reﬂects This.

VEHITS 2020 - 6th International Conference on Vehicle Technology and Intelligent Transport Systems

428

Table 2: Descriptive Statistics for the Difference between Maximum and Minimum Temperature in the Forehead Area with

Mean and Standard Deviation (SD) for Different Sweat Statuses.

Not sweating Shortly before sweating Sweating

Mean ± SD in [K] 1.43 ± 0.31 2.47 ± 0.58 2.69 ± 0.58

interval (Bricout et al., 2010). LF/HF ratio reﬂects the

balance of the autonomic nervous system with the low

frequency part (LF), which is representing the parasy-

mathetic and sympathetic activities (Pomeranz et al.,

1985) and the high frequency part (HF), which is rep-

resenting the parasympathetic activity. As described

in the literature (Liu et al., 2008), (Liu et al., 2011),

(Nkurikiyeyezu et al., 2018), (Bricout et al., 2010),

the RMSSD value decreases with sweating and the

LF/HF rate increases.

4 DISCUSSION

The results show, that there is a signiﬁcant difference

between status not sweating and shortly before sweat-

ing and also between status not sweating and sweat-

ing. The study found no signiﬁcant difference be-

tween the status shortly before sweating and sweat-

ing. One possible reason for this could be the study

design, because the subject has passed about 2 min-

utes between the query of the perspiration status and

the actual measurement. During this time, the sub-

ject was sit into the vehicle and the sensors were put

on. One possibility would be, that the sweating has

set within this 2 minute time break and thus the sec-

ond measurement time actually represents the status

of sweating. Therefore, in the following discussion

only between not sweating and sweating is differenti-

ated. The status shortly before sweating is counted to

status sweating.

For a ﬁnal decision if a person is sweating or not,

the temperature difference is not perfect, because of

overlapping values for both statuses. In the follow-

ing boxplot (ﬁgure 4) it is possible to see the critical

values. Critical values are all the values appearing

in status not sweating and also status sweating. For

ﬁnding a criterion to differ between the statuses only

with the temperature difference, it is possible to use

the boxplot boxes without the whiskers as a criterion

for status sweating or not sweating. If the tempera-

ture difference on the forehead is lower than 1.58 K

the subject is detected as not sweating. If the value is

higher than 1.96 K the subject is identiﬁed as sweat-

ing. Between these two values the status is unclear.

For these data sets, with these limits, sweat sta-

tus can be correctly distinguished between status not

sweating and status sweating in 78% of cases. In 17%

of the recorded images, no precise statement can be

made because the value lies between the two limits.

Only 5% of the images would be misclassiﬁed with

the decision support.

With the given thermal images, other data can be

used to differentiate the sweating status in addition

to the temperature. For example, spotting can be de-

tected using a frequency analysis and can therefore be

used as a further decision factor. The results of this

will be presented in further publications.

For having a more accurate decision of the sys-

tem, other values like heart rate variability or skin

conductance are interesting to know. In addition to

the method of sweat detection based on the tempera-

ture difference on the forehead, there are other ways

to detect whether a person is sweating or not. For ex-

ample, the average temperature value of the forehead

area can be considered or also the spatial frequency

over the area can be analyzed. These possibilities are

currently being evaluated in detail.

not sweating shortly before sweating sweating

time of measurement

1.5

2.5

3.5

temperture difference in [K]

Figure 4: Boxplot of the Different Sweat Status. The Values

Show the Difference between the Minimum and Maximum

Temperature for a Forehead Area. The Higher the Differ-

ence Value, the More Sweating.

Further investigation handle with sweat detection

using a machine learning algorithm. For this, the

recorded images of the subjects and the forehead ar-

eas are labeled and then a network is taught.

5 CONCLUSION

This work investigates whether it is possible to detect

in the vehicle if an occupant is sweating or not. To

Sweat Detection with Thermal Imaging for Automated Climate Control and Individual Thermal Comfort in Vehicles

429

test this, a study was designed that records the indi-

vidual subjects in different sweat status using a ther-

mal imaging camera. For evaluation a forehead area

is used to ﬁnd out the difference between the maxi-

mum and minimum temperature. Due to the set deci-

sion thresholds, 78% of the images can be classiﬁed

correctly. This means that then the status not sweat-

ing was distinguished from sweating. The result also

shows that there is a signiﬁcant difference between

the sweating and the non-sweating subjects based on

a temperature difference in a forehead area. With this

result, an important step in the direction of climate

automation is done. For this automation, in addi-

tion to already known interior data and personal data,

data about the sweat status of the various occupants

is required. So far, there was no way to detect this

status without contact. Only humidity sensors and

thermocouples were previously used for sweat detec-

tion. With the non-contact sweat detection by thermal

imaging cameras, it can be easily detected in the vehi-

cle, which climate setting is suitable for the individual

occupants.

Since the presented method found only a recog-

nition accuracy of 78%, further alternative methods

are examined to increase the accuracy. Other alterna-

tives of sweat detection without thermal camera are

currently under investigation.

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