Thermal Comfort Measurement using Thermal-Depth Images for Robotic
Monitoring
Jun Miura∗, Mitsuhiro Demura, Kaichiro Nishi, and Shuji Oishi
Department of Computer Science and Engineering, Toyohashi University of Technology
Toyohashi, Aichi 441-8580, Japan

Abstract
This paper describes an application of thermal-depth images to human thermal comfort measurement. A mobile
monitoring of the elderly and residents of care houses is one of the promising applications of mobile assistive robots.
Monitoring if a person feels comfortable is an important task of such robots. We rely on an established comfort measurein the architecture domain, namely, predicted mean vote (PMV). PMV is calculated mainly by six factors and one ofwhich is the clothing insulation or clo-value. Clo-values are usually measured by a thermal mannequin, a speciallydesigned apparatus for the purpose. We apply human recognition techniques in thermal-depth images to eﬃciently
measure clo-values, thereby enabling on-line assessment of thermal comfort. We evaluate the method and develop a
mobile robot system for experimental testing.
Keywords: Thermal comfort measurement, thermal-depth images, clothing insulation, mobile assistive robot.

1. Introduction
Service robots are expected to support our daily lifein the near future with the continuous development ofrobotics technologies. As we are facing the aged society,one promising application is robotic monitoring, in whicha robot lives with and takes care of the elderly who live
alone.
Monitoring people has been dealt with in robotics andcomputer vision domains. One popular approach is socalled smart house [1, 2, 3] which uses many embedded
cameras and sensors, usually put on ceilings and walls,
to monitor the state and the activity of residents inside.
Recent IoT (Internet of Things) technologies have also
been contributing to this line of research and development.
Since such an approach requires pre-installed sensors, it issometimes diﬃcult to apply to existing houses. Anotherapproach is to use wearable devices such as a thermometerand a cardiometer for health monitoring [4]. Using suchdevices makes it possible to take direct and reliable data
in real-time, but may impose a physical and/or mental
burden on people.
One of the monitoring tasks is to examine if a resident is in a comfortable state. Such a comfort depends on
various factors but they can be divided into two main factors. One is how a residence is in a good condition [5, 6],that is, for example, whether temperature, illuminance,or air cleanness are in good condition. The other is how
residents themselves in a state of good health. Some combined eﬀects of such factors will determine the comfort∗ Corresponding author

Preprint submitted to Pattern Recognition Lettersof the resident. This paper deals with thermal comfort,
which measures human satisfaction with the thermal environment [7]. We use PMV (predicted mean vote) as ameasure and develop a robot system for PMV measurement.
The sensory data we choose for measuring thermal
comfort is thermal-depth image, since usual RGB images
sometimes suﬀer from privacy issues as well as sensitivity
to illumination conditions. By using a thermal camera in
addition to a depth one, we can eﬀectively segment humanregions and calculate thermal comfort measures. The contributions of the research are: (1) to propose an approach
to mobile robot-based thermal comfort measurement, and(2) to develop an experimental system and to validate theproposed methods. This paper is an extended version ofrobotic thermal comfort measurement described as a partof our previous paper [8]. The most signiﬁcant part of thispaper compared with [8] is the experimental veriﬁcationof thermal image-based estimation of clothing insulationand thermal comfort.
The rest of the paper is organized as follows. Section
2 describes related work. Section 3 explains our robot
system for mobile thermal comfort measurement. Section 4 explains the measure of thermal comfort in detail and methods of calculating necessary factors including the clothing insulation. Section 5 shows experimental
results for evaluating thermal comfort measurements andfor robotic monitoring. Section 6 discusses the use of thesystem in actual robotic monitoring scenarios as well as
possible improvements of the proposed methods. Section
7 summarizes the paper.
January 25, 2019

2. Related Workthermal camera

2.1. Thermal comfort measurement
PMV is one of the most popular thermal comfort measures and mainly depends on four environmental factors(air temperature, mean radiant temperature, air velocity,and relative humidity) and two personal factors (clothing insulation and activity level) [5]. In the architecturedomain, these factors are usually measured using special
devices and/or a specially installed set of (ﬁxed) sensors[9].
For measuring and estimating environmental factors,
using a robot as a mobile base is a promising approach.
Previous researches deal with various applications such asodor map making [10], gas leak position localization [11],temperature and illuminance distribution mapping [12].
The activity level of a person is related to their metabolic
rate and thus to the thermal comfort. The relationships
between the metabolic rate and various activities such as
seating, standing, and cooking have been analyzed [7].
Therefore activity recognition techniques (e.g., [13, 14])
could be adopted for thermal comfort measurement inrobotic monitoring.
The clothing insulation, which is the other personal
factor, is usually measured using a thermal mannequin [7].
The clothing insulation for various materials and designs
have also been compiled in a database, which can be used
for an on-line thermal comfort measurement. Matsumoto
et al. [15] presented a method of measuring thermal comfort using the visual recognition of the material and theweight of clothing.
Several methods have been proposed for thermal comfort measurement without estimating the clothing insulation. Ranjan and Scott [16] proposed a method of detecting and predicting thermal comfort by looking for physiological markers of vasodilation and vasoconstriction inthermal images. Metzmacher et al. [17] developed a methodof thermal comfort measurement using a thermal camera
and a numeral human heat transfer model, enabling measuring thermal comfort in occluded local body regions.

RGB-D camera(a) Robot and cameras.

(b) Test scene and thermal-depth data.

Figure 1: Robot and an example thermal-depth data.

cameras, but this approach cannot be applied to depth
cameras. Oreifej et al. [20] describes a method of calibrating three modalities, an optical camera, a thermal camera,
and a 3D LIDAR, based on optical-thermal and opticalLIDAR calibration.
3. Thermal Comfort Monitoring Robot
Fig. 1(a) shows our monitoring robot prototype. The
robot is quipped with a thermal camera and an RGB-D
camera as well as thermal and humidity sensors. Fig. 1(b)shows an example of thermal-depth data, viewed from adiﬀerent viewpoint from the original.
3.1. Thermal-depth calibration
We use PI-160 (Optris, 160 × 120 pixels) and Kinect
v2 (Microsoft) as a thermal and a depth camera, respectively. Since it is diﬃcult to make a calibration board
which is visible from both cameras, we additionally use an
RGB camera of Kinect v2 and calculate the relative pose
between the thermal and the depth camera from those of
the thermal-RGB and the RGB-depth camera pair. As aresult, we use diﬀerent calibration boards for each pair.
A popular checker board is used for the RGB-depth calibration, because the depth camera of Kinect v2 can also
produce NIR (near infrared) images, in which the board
is suﬃciently visible. Fig. 2 shows images of the board inboth camera images.
For the thermal-RGB calibration, we follow the workby Rzeszotarski and Wiȩcek [19]. In addition to putting
aluminium sheets on white regions, we heat up the board
to make the temperature diﬀerence clearer for the thermal
camera. Fig. 3 shows images of the board, which arevisible by both the RGB and the thermal camera. Notethat this aluminium-pasted board is diﬃcult to be properly
observed in NIR images, and therefore we cannot directly
calibrate the thermal-depth camera pair using this board.

2.2. Thermal-depth image generation
Combination of thermal and depth images has recently
been widely used for various applications such as people
detection and disaster response. Using two kinds of cameras entails a procedure for calibrating their relative pose
(i.e., extrinsic parameters). We need calibration patterns
which are visible from both types of cameras.
Rangel et al. [18] used a board with circular holes fora thermal-depth calibration. These holes can easily be detected by a depth camera, but could be diﬃcult for a thermal cameras when a suﬃcient temperature diﬀerence does
not exist between the board and the background. Rzeszotarski and Wiȩcek [19] put aluminium sheets on white
regions in a checker board for a thermal-RGB camera calibration. Aluminium sheets provide high reﬂection of infrared rays and are good for making markers for infrared2

Table 1: PMV and thermal sensation scale.

(a) RGB image.

PMV
+3
+2
+1
0
-1
-2
-3

(b) NIR image.

Figure 2: Calibration board captured by the RGB and the NIR
camera.

(a) RGB image.

Thermal Sensation Scale
hot
warm
slightly warm
neutralslightly cool
cool
cold

[21]), the measured position is the center of the ﬂoor in
the space and its height is 100[cm], and it is desirable
to additionally measure the temperature at the heights of
10[cm] and 60[cm] when sitting and 10[cm] and 170[cm]
when standing. Considering both the requirement in architecture and the robot structure, the thermocouples are
set at the heights of 10[cm], 70[cm], and 170[cm].

(b) Thermal image.

Figure 3: Calibration board captured by the RGB and the thermal
camera.

4. Thermal Comfort Measurement(a) Scene.

(b) Thermal image.

4.1. Predicted mean vote
Thermal comfort is human satisfaction with the thermal environment and inﬂuenced by various factors suchas physical, physiological, psychological processes. One of
the indices of assessing thermal comfort is PMV (predicted
mean vote), which is determined in ISO 7730 [22]. Thisis an index that predicts the mean value of the votes ofa large group of persons on the 7-point thermal sensation
scale (see Table 1), based on the heat balance of the humanbody between the internal heat production in the bodyand the loss of heat to the environment. PPD (predictedpercentage of dissatisﬁed) is also used as another measure,which quantitatively predicts the percentage of thermallydissatisﬁed people who feel too cool or too warm.
PMV basically depends on the following six factors[7]: thermal insulation (or thermal resistance) of clothing (called clo-value), activity level (met-value), air temperature, air velocity, radiant temperature, and humidity.
Among these, the air temperature and the humidity aremeasured by on-robot sensors. The radiant temperature
is assumed to be the same as the air temperature. The
air velocity is set to be a small value for indoor environments. The activity level is determined based on humanposture, such as sitting and standing, and actions taken.
The clo-value can approximately be measured using thermal images as explained below.

(c) Human region.

Figure 4: Human detection using thermal point cloud.

From both calibration results, we indirectly calculate
the thermal-depth calibration. As shown in Fig. 1(b), a
consistent thermal point cloud is obtained from an example of thermal-depth data, indicating that the calibration
works reasonably well.
3.2. Human region extraction
Thermal-depth images are useful for extracting humanregions in a complex background. We use a combinationof a thermal-based thresholding and a depth-based Euclidean distance clustering for human region extraction [8].
We set a temperature threshold to 23[◦ C] for human region detection, and that for clustering is set to 80[mm].
A size ﬁltering is then adopted to extract only the pointsof the humans. Fig. 4 shows a human detection result.
In the thermal image, since there are many warm regions
and some of them are connected to the front human region, human detection using only thermal image may fail.
By using the thermal point cloud, the human region iscorrectly extracted.

4.2. Estimating clo-value
4.2.1. Calculation of clo-value
The clo-value of clothing is usually measured on a thermal mannequin, but that cannot be used for on-line measurements in a daily situation. We instead adopt an estimation method based on thermal measurements. The

3.3. Temperature measurement of the environment
The robot uses three thermal sensors (thermocouples)for measuring temperatures at three diﬀerent heights. In
the architecture domain, guidelines are set for temperature measurements. According to such guidelines (e.g.,3Skeleton extraction

Clothing region extraction(a) Sitting.

(a) Experimental scene.

(b) Thermal image.

Figure 6: A thermal mannequin with a long-sleeved T-shirt

Skeleton extraction

sented in met, is then determined by referring to a precalculated table [7]. The values are, for example, 1[met]for quiet sitting and 0.7[met] for sleeping.

Clothing region extraction(b) Lying.

5. Experimental Results
Figure 5: Clothing region extraction.

5.1. Evaluation of clothing insulation measurement
Clo-values for typical clothing are suggested in the literature [7]. However, the details of materials and fabrications are diﬀerent from one clothing to another and so are
clo-values. We therefore compared the clo-value measurement using our thermal camera-based method and that byusing a thermal mannequin.
We use three types of clothing, a long-sleeved T-shirt,
the layers of that T-shirt and a cardigan, and the layers of
the T-shirt and a ﬂeece jacket. Fig. 6 shows a scene of thethermal mannequin wearing the T-shirt and its thermal
image. The target temperature of the mannequin bodyis set to 33[◦ C] and that of the room is set to 22[◦ C].
Since the clo-value changes for each part of the body, wemeasure them for the following ﬁve positions, which arevisible from our monitoring robot: chest, right and left
upper arm, right and left forearm. We also use a region
on the face for measuring skin surface temperature. We
chose ﬁve pixels in the respective positions and calculated
the averaged temperature for reducing the eﬀect of pixelwise noise.
Table 2 shows the results for the three types of clothing. The ﬁrst and the second column of the tables are thetemperature of clothing surface and skin surface, respectively. These values are measured by the thermal camera
and used for estimating the clo-values. The error in temperature measurement of the thermal camera we used isspeciﬁed as ±2[deg]. The measured temperatures of theskin surface are within this range from the target temperature (33[◦ C]).
The clo-values is separately measured by using the thermal mannequin from the target mannequin skin temperature, the measured air temperature, and the heat loss ofthe mannequin. Considering this meaurement as a target,

clo-value Iclo is given by [23]:Iclo =

ts − tcl
1,0.155 · h tcl − to(1)where h is the heat transfer coeﬃcient of the human body(set to 8.6 assuming a calm indoor environment [24]), to is
the operative temperature (approximated here by the airtemperature), ts is the skin surface temperature, and tcl is
the clothing surface temperature. The last two temperature values are measured using thermal images as follows.
4.2.2. Extracting skin and clothing regions in the thermal
image
A human region extracted in thermal-depth images(see Sec. 3.2) is then divided into skin and clothing regions. We use the GrabCut [25] algorithm for this division. Since the algorithm requires seed points, we extract
them with a simple assumption that the face is exposedand shoulders are covered by clothings. These joint positions are detected by using OpenPose [26]. Fig. 5 showsskeleton extraction and clothing region extraction for twocases (sitting and lying).
Once the skin and the clothing region are extracted,
their average temperatures are used as the skin surface
temperature (ts ) and the clothing surface temperature (tcl )for calculating eq. (1). The averaged calculation time ofclo-value is about 2.2[sec], which is fast enough for calculating the clo-values of people resting in a room.
4.3. Estimating activity level
Postures and activities can be determined by analyzing
the human region in the image. The activity level, repre4

Table 3: Summary of clo-value comparison.

single T-shirt
T-shirt with cardiganT-shirt with ﬂeece jacket

averaged diﬀerence
-0.15
-0.34
-0.95

standard deviation
0.17
0.26
0.65

Person A

Person B
Thermalimage

Table 4: Summary of PMV comparison.
foot height
upper body heightface heightaveraged diﬀerence
-0.37
-0.93
-0.87

Clothing
regionSkinregionRobotstandard deviation
1.91
1.60
1.25

Figure 8: Thermal comfort measurement experiment.

we examine the diﬀerence between the target clo-value andthe one measured by our method.
The averaged diﬀerences and the standard deviations
are summarized in Table 3. The tables show that the proposed method underestimates and that the absolute difference increases as the amount clothing increases. The
reason of this tendency could be a complex air insulation
between clothings, but further investigation is necessary.
These diﬀerences are not very small but at least qualitatively acceptable considering the simplicity of the method.
We also analyze the eﬀect of this diﬀerence on PMV.
Let the clo-value be 0.5 in, for example, the case where
PMV is −0.20 (between normal and slightly cool). Whenthe real clo-value is larger by the maximum average difference (i.e., 0.95 in Table 3), PMV becomes 0.97 (slightlywarm), being higher by 1.17.

PersonA
B

Table 5: PMV measurement results.
Clo-value PMV (170cm) PMV (110cm) PMV (10cm)
0.21
-2.52
-2.86
-2.64
0.22
-2.43
-2.75
-2.54

5.3. Robot monitoring example
We implemented a capability of patrolling and monitoring thermal comfort on the robot, which is a combination of the proposed thermal comfort measurement
method with navigation and human detection ones.
Fig. 8 shows an experimental scene. The robot patrols a designated area of a room, and when it ﬁnds a
person, it moves to and takes a depth-thermal image ofthem, to calculate the clo-value, and then calculate PMVat three diﬀerent heights (170[cm], 110[cm], and 10[cm]from the ﬂoor) to examine the thermal comfort at various
body parts.
Table 5 summarizes the measurement results for thescene shown in Fig. 8. ISO 7730 describes that a comfortable environment has the PMV value in ±0.5 [22]. The
thermal environment of the experimental site is shown to
be rather cool for both persons (see Table 1). To raise
the lowest PMV value (-2.86) to be within the comfortable range (±0.5), for example, the air temperature needs
to be increased by nine degrees or the clo-value should be
increased by 0.8 points. A monitoring robot could take anaction such as turning on a heater or recommending to therespective person to layer another piece of clothing (e.g.,
sweater).

5.2. Evaluation of PMV measurement
The next experiment is to evaluate the PMV measurement in comparison with subjective assessments. We set
up three rooms with warm, normal, and cool temperature. Each subject spends for twenty minutes and takes
questions. The questionnaire has three questions asking
one of seven PMV scale (see Table 1) for three heights at
foot-level, upper body-level, and face-level. We obtained
data from ﬁve, seven, and ﬁve subjects for the warm, thenormal, and the cool room, respectively.
Fig. 7 shows the plot of the relationship between thepredicted and the subjective PMV values, indicating mostof predictions are reasonable. The averaged diﬀerences andthe standard deviations are summarized in Table 4. We
then convert obtained PMV values into the seven classes
using the midpoints of sensation values as thresholds. For
example, a PMV value is classiﬁed as normal if its absolutevalue is less than 0.5, and classiﬁed as warm if it is between
1.5 and 2.5. Direct comparison of predicted and subjectiveclasses results in the accuracy of 0.29, 0.29, and 0.35 forthe foot, the upper body, and the face height, respectively.
Since the maximum error of PMV due to that of clo-value
is about one (see Sec. 5.1), if we allow the matches withneighboring classes, the accuracy values become 0.65, 0.71,
and 0.65, respectively. This would be reasonable for being
used for a robot to support, as we discuss later.

6. Discussion
PMV is a measure obtained through a complicated calculation of various factors, and is sometimes error-prone
due to errors in estimating those factors. For example,
our approach to estimating the clothing insulation is based
simply on the temperature diﬀerence between the clothing
and the skin surface. Although the obtained clo-values are5

Table 2: Comparison of estimated clo-values for proposed and mannequin-based.

chestright forearm
left forearm
right upper arm
left upper arm

clothing surface
temperature
[◦ C]
30.8
29.2
27.7
30.9
30.2

(a) long-sleeved T-shirtskin surface
heat loss of
air temperaturetemperaturemannequin
[◦ C]
[◦ C]
[W/m2 ]
33.3
57.3
21.3
33.3
74.9
21.3
33.3
73.4
21.3
33.3
76.2
21.3
33.3
67.3
21.3

chest
right forearm
left forearm
right upper arm
left upper arm

clothing surface
temperature
[◦ C]
29.9
30.2
27.8
28.7
27.2

(b) long-sleeved T-shirt and cardiganskin surface
heat loss of
air temperaturetemperaturemannequin
[◦ C]
[◦ C]
[W/m2 ]
35.0
40.4
21.3
35.0
56.4
21.3
35.0
59.0
21.3
35.0
41.8
21.3
35.0
42.6
21.3

chest
right forearm
left forearm
right upper arm
left upper arm

clothing surface
temperature
[◦ C]
27.8
28.3
26.6
26.7
27.2

(c) long-sleeved T-shirt and ﬂeece jacket
skin surface
heat loss of
air temperaturetemperaturemannequin
[◦ C]
[◦ C]
[W/m2 ]
33.7
25.3
21.3
33.7
24.9
21.3
33.7
46.1
21.3
33.7
31.3
21.3
33.7
38.2
21.3

qualitatively reasonable, we could consider a more complicated model (e.g., [27]). It would also be necessary to examine various combinations of clothings and environments.
Air velocity at the position of a person is also diﬃcult toprecisely estimate from a mobile monitoring robot using
on-board sensors.
Another drawback when applied to robotic monitoringis that the measure (i.e., PMV) is designed to predict thethermal sensation of most persons, not a speciﬁc individual. As a result, the PMV values are certainly eﬀective
for extreme cases (too cold or too hot), but provide only
approximation. The proposed method seems to suﬃce inthis sense. Further assessments will be, however, necessary for each person. One possible approach to this issue
is to introduce an interaction and learning capability to
the monitoring robot. By actively asking a person about
their sensation and accumulating responses and/or observations of the person, the robot could make a model of theperson’s sensation which describes, for example, the mapping between the measured PMV value and the thermal
sensation.
We currently use the Euclidean distance clustering for
extracting a human region. Since the clustering performance has a large eﬀect on the accuracy of the result, it
is desirable to consider other clustering methods. As an
extension of this work, we will apply weighted k-meansclustering [28] and evaluate the results obtained by the
proposed method with two diﬀerent clustering methods.

clo-value(proposed)

clo-value(mannequin)

0.20
0.39
0.65
0.19
0.26

0.55
0.45
0.51
0.42
0.53

clo-value(proposed)

clo-value(mannequin)

0.45
0.41
0.83
0.64
1.00

1.09
0.77
0.75
1.23
1.17

clo-value(proposed)

clo-value(mannequin)

0.69
0.59
1.03
1.00
0.85

2.18
2.44
1.10
1.81
1.36

7. Summary
Thermal comfort measurement is an important taskof the robotic monitoring of residents. This paper has
described a novel approach to thermal comfort measurement using a mobile robot. We use PMV as a measureof thermal comfort, which is calculated by six main factors; two personal and four environmental ones. We developed a method of estimating one of the personal factors, that is, the clothing insulation (or clo-value) using
a depth-thermal camera system. A procedure for depththermal camera calibration is also developed. We evaluated the clothing insulation measurement using a thermalmannequin and the PMV measurement in comparison witha user questionnaire. The evaluation results show a certain
level of accuracy that could be used for robotic monitoring.
We ﬁnally discussed the use of the current system in actual
situations as well as improving measurement accuracy.
Acknowledgment
The authors would like to thank Prof. Kazuyo Tsuzukiof Toyohashi University of Technology for her support inusing the thermal mannequin. This work is in part supported by JSPS KAKENHI Grant Numbers 25280093/17H01799
and the Hibi Science Foundation.
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(b) upper body height.

(c) face height.

Figure 7: Comparison of predicted and subjective PMV’s. The horizontal axis indicates the user’s subjective assessment and the vertical axis
indicates the predicted (measured) PMV by the proposed method.

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