Use of Thermal Point Cloud for Thermal Comfort Measurement andHuman Pose Estimation in Robotic Monitoring
Kaichiro Nishi Mitsuhiro Demura Jun Miura Shuji Oishi
Department of Computer Science and Engineering
Toyohashi University of Technology

Abstract
This paper describes applications of thermal point cloudto lifestyle support robots. 3D information is useful for recognizing human and objects based on their shapes, whilethermal information is useful for assessing the residential
and the human states as well as for detecting human. Combining these two kinds of information will be beneficial to
the robots which live with and support people at home or incare houses. This paper shows two applications of thermal
point cloud. One is thermal comfort measurement basedon predictive mean vote (PMV) which uses, as one of thefactors, the amount of clothing estimated by thermal information. The other is human pose estimation only by depth
images, which has an advantages in terms of privacy andinsensitivity to illumination changes. We developed methods for these applications and show experimental results.

1. Introduction
Service robots are expected to operate in a near futurein our daily life as robotics technologies is becoming matured and ready for deployment. As we are facing the aged
society, one promising application is monitoring, in whicha robot lives with and takes care of the elderly who lives
alone.
Monitoring people has been an important application in
robotics and computer vision. One approach is so-called
smart house [14, 30, 16] which uses many embedded cameras and sensors, usually put on ceilings and walls, to monitor the state and the activities of residents inside. Since suchan approach requires pre-installed sensors and cannot easily applied to normal houses. Another approach is to use
wearable devices such as a thermometer and a cardiometer for health monitoring [21]. Using such devices makes it
possible to take direct and reliable real-time data, but may
impose a physical and/or mental burden on people.
One of the goals of the monitoring task is to examine
if the physical states of a residence, such as temperature,illuminance, and air cleanness, are in a comfortable condition for a resident [13, 22]. Another goal is to examine if
the resident is in a good health. Based on these examinations, responses such as advice to the resident, operation of
appliances (e.g., air conditioner), or alert to the local hospital will be made. Using a mobile robot is a promising way
to achieving such goals. By installing various sensors on
an autonomous mobile robot, it can observe wherever andwhatever it wants to collect necessary information by automatically detecting targets and navigating itself to them.
While some previous works including ours [7] deal with examining physical state of a residence, this paper deals withestimating the human state.
Image data are useful for human detection and humanstate estimation. However, it may sometimes suffer fromprivacy issues as well as sensitivity to illumination conditions. Using depth images instead will address these issues, but it may make it difficult to reliably extract human regions, due to a scarcity of informative features. We
therefore additionally use thermal information for locating
humans, which is known to be effective especially in indoor scenes (e.g., [25]). We combine these two kinds ofinformation into thermal point cloud data and apply themto human state estimation. This paper deals with two example problems, (i) thermal comfort measurement and (ii)pose estimation with various postures and occluding objects. These problems are effectively solved by using thermal point clouds. This is the contribution of the paper.
The rest of this paper is organized as follows. Section 2
describes related work. Section 3 describes human detection using a thermal point cloud. A calibration method between a thermal and a depth camera is also described. Section 4 describes an application of thermal images to estimating the human’s comfort. Section 5 describes a method of
estimating pose only from depth images using a deep neuralnetwork under large occlusions. An approach to generating
training depth images for large occlusion cases is also described. Section 6 concludes the paper and discusses futurework.

2. Related Work2.1. Generating thermal point cloud
3D point cloud has recently been widely used for many
mapping and recognition tasks thanks to the developmentof inexpensive RGB-D cameras and point cloud processing
libraries. To add thermal information to a point cloud (i.e.,
to generate a thermal point cloud), a calibration between athermal and a depth camera is necessary.
Rangel et al. [23] 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 notexist between the board and the background. Rzeszotarski
and Wiȩcek [24] put aluminium sheets on white regions in achecker board for a thermal-RGB camera calibration. Aluminium sheets provide high reflection of infrared rays andare good for making markers for infrared cameras, but thisapproach cannot be applied to depth cameras. Vidas et al.
[29] use non-colinear straight lines in the scene for calibration. They assume there are such lines visible from botha thermal and an RGB-D camera. Oreifej et al. [19] describes a method of calibrating three modalities, an opticalcamera, a thermal camera, and a 3D LIDAR, based on the
optical-thermal and the optical-LIDAR calibration.

2.2. Thermal comfort measurement
Thermal comfort is human satisfaction with the thermal
environment and influenced by various factors such as physical, physiological, and psychological processes [4]. One of
the indices of assessing thermal comfort is PMV (predicted
mean vote), determined in ISO 7730. PMV is also used
for estimating another measure of thermal comfort, namely,
PPD (predicted percent of dissatisfied). PMV mainly depends on four environmental factors (air temperature, mean
radiant temperature, air velocity, and relative humidity) andtwo personal factors (clothing insulation and activity level)[13].
For measuring and estimating environmental factors, using a robot as a mobile base is an interesting research area.
Previous researches deal with various applications such asodor map making [11], gas leak position localization [5],temperature and illuminance distribution mapping [7].
The activity level of a person is related to his/her
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 [4]. Therefore activity recognition techniques (e.g.,[27, 8]) could be adopted for thermal comfort measurement
in robotic monitoring.
The clothing insulation, which is the other personal factor, is usually measured using a thermal mannequin [4]. The
clothing insulation for various materials and designs havealso been compiled in a database, which can be used foran on-line thermal comfort measurement. Matsumoto et al.
[12] presented a method of measuring thermal comfort fromthe estimation of the material and the weight of clothing.

2.3. Human pose estimation
Pose estimation is an important function for a monitoring robot to know the state of a person. For example, unusual postures such as lying and crouching could be a signof emergency.
Human pose estimation has been one of the important
problems in computer vision. A large degrees of freedom of
human structure and frequent occlusions sometimes makethe pose estimation be a difficult task. For a robust andreliable estimation, various methods have been proposed[15, 10]. Thanks to recent advances in deep learning techniques, many image-based methods have been proposed, forexample, for joint position estimation [28, 6] and part segmentation [18].
As stated above, depth image-based pose estimation has
an advantage in terms of privacy and insensitivity to illumination conditions. Shotton et al. [26] developed a humanpose estimation method using depth-based features with arandom forest classifier. Although the method shows a niceperformance, its applicability to unusual poses and heavilyoccluded situations is limited.
We proposed a method of generating human depth images with pixel-wise body part labels by a combination ofcomputer graphics and motion capture techniques [17]. We
have shown that a deep neural network trained by using thegenerated images can recognize well a variety of humanposes in real scenes.

3. Human Detection using Thermal Point
Cloud
3.1. Thermal-depth camera calibration
One method to calibrate two cameras is to use a marker
board (e.g., checker board). The relative pose between
the cameras is calculated by combining two extrinsic parameters obtained by a readily-available calibration routine.
In the case of thermal-depth camera calibration, making aboard which is visible from both cameras is an issue.
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 board which is visible fromboth cameras, we additionally use an RGB camera of Kinectv2 and calculate the relative pose between the thermal andthe depth cameras from those of the thermal-RGB and the
RGB-depth camera pair; we use different calibration boards
for each pair.
A usual checker board is used for the RGB-depth calibration, because the depth camera of Kinect v2 can also

thermal camera
RGB-D camera(a) RGB image.

(b) NIR image.

Figure 1: Calibration board captured by the RGB and the
NIR camera.

(a) Robot and cameras.

(a) RGB image.

(b) Thermal image.

(b) Test scene and thermal point cloud.

Figure 3: Camera settings and an example thermal point
cloud.

Figure 2: Calibration board captured by the RGB and thethermal camera.
produce NIR (near infrared) images, in which the board isvisible. Fig. 1 shows images of the board in both camera
images.
For the thermal-RGB calibration, we follow the workby Rzeszotarski and Wiȩcek [24]. 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. 2 shows images of the board, which are visible by both the RGB and the thermal camera. Note that thisaluminium-pasted board is hard to be properly observed in
NIR images, and therefore we cannot directly calibrate thethermal-depth camera pair using this board.
By combining the two calibration results, we have atransformation (i.e., relative pose) between the thermal andthe depth camera. Using the transformation, we can attach
thermal data to the point cloud. Fig. 3 shows the camera settings on a mobile robot and an example thermal point cloud.
The temperature is shown in a pseudo color mapping, fromred (warm) to blue (cool).

(a) Scene.

(b) Thermal image.

(c) Human region.

Figure 4: Human detection using thermal point cloud.

Figure 5: A simple state estimation, sitting or lying.

the human region is correctly extracted. Fig. 5 show otherexamples, with a principal axis estimation of the extracted
region for a simple human state estimation (sitting or lying).

3.2. Human detection using thermal point cloud
We currently use a combination of a thresholding forthermal data and the Euclidean distance clustering for detecting a human using a thermal point cloud. We set a temperature threshold to 23◦ C for human region detection, andthat for clustering is set to 80mm. The size filtering is thenadopted to extract only points of humans.
Fig. 4 shows a detection result. In the thermal image,since there are many warm regions and some of them areconnected to the front human region, human detection using
only thermal image may fail. Using the thermal point cloud,4. Thermal Comfort Measurement
4.1. Predicted mean vote
We first briefly explain how to calculate PMV based on[4]. Table 1 shows the correspondence between PMV andthe thermal sensation scale.
PMV basically depends on the following values: thermal
insulation (or thermal resistance) of clothing (clo-value),
activity level (met-value), air temperature, air velocity, radiant temperature, and humidity. Among these, the air tem-Table 1: PMV and thermal sensation scale.
PMV
+3
+2
+1
0
-1
-2
-3

Thermal Sensation Scale
hot
warm
slightly warm
neutralslightly cool
cool
cold

(a) Thermal image.

(b) Markers.

(c) Segmentation result.

Figure 6: Detecting skin and clothing regions.

perature and the humidity are measured 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 smallvalue for indoor environments. The activity level is determined based on the human posture, such as sitting andstanding, and actions taken. The clo-value can be approximately measured using thermal images as explained below.

4.2. Estimating clo-value(a) Subject A.

The clo-value of a 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 clo-value
Iclo is given by [3]:Figure 7: Three subjects in different clothings.

Iclo = (1/0.155 · h)(ts − tcl )/(tcl − to ),(1)

where h is the heat transfer coefficient of the human body(set to 0.052), to is the operative temperature (approximatedhere by the air temperature), 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.
Assuming that an extracted human region in the thermal
image (see Sec. 3.2) is composed of the skin and the clothing regions and that the former is warmer than the latter,
we apply a binarization to that region for discriminating
skins and clothings. We first get an initial binarization using Otsu’s method [20] and then apply a thinning operation
to the segmented regions for obtaining markers to be used
for the subsequent processing of a marker-based watershedalgorithm. The output of the watershed algorithm is the final segmentation. Fig. 6 shows the process of dividing ahuman region into skin and clothing regions. The averagetemperature is calculated in the respective regions and used
for calculating the clo value in eq. (1).
We show experimental results for the clo-value calculation. Fig. 7 shows thermal images and Table 2 shows thecalculation results. Subject A layered two T shirts, a shorterone and a longer one; according to [3], their clo-values are
predicted as 0.09 and 0.12, respectively, and 0.21 in total.
Subject B had the same combination. Subject C wore only(b) Subject B.

(c) Subject C.

Table 2: Clo-value calculation results.
Subject
A
B
CTs (◦ C)
35.83
30.77
29.76

Tc (◦ C)
31.65
28.49
28.42

Ta (◦ C)
24.50
25.50
24.75

Clom
0.20
0.26
0.12

Clot
0.21
0.21
0.09

Ts : Skin surface temperature, Tc : Clothing surface temperature,
Ta : Air temperature,
Clom : Measured clo-value, Clot : Clo-value estimated from the material.

the shorter one. We calculate the clo-values from the skinsurface, the clothing surface, and the air temperatures, andthen compared them with the predicted ones. The difference
between the two clo-values are between 0.1 to 0.5. The differences are not very small but at least qualitatively acceptable considering the simplicity of the method.

4.3. Measuring Thermal Comfort
We developed an experimental system which estimates
the thermal comfort automatically. We use a mobile robot
equipped with the thermal-depth camera system and various
sensors such as thermocouples and a humidity sensor controlled by Arduino’s. Fig. 8 shows an experimental scene
in which the robot finds and moves to each person to estimate the clo-values, with thermal image processing results.
We measured PMV values for each person at three different
heights, 170cm, 110cm, and 10cm from the floor, to see the
comfort at various body positions.
Table 3 summarizes the measurement results. ISO 7730

Person A

Person B
Thermalimage

Clothing
regionSkinregion(a) Determining the camera pose.

(b) View from the virtual camera.

Robot

Figure 9: Image generation using Maya.

Figure 8: Thermal comfort measurement experiment.

Table 3: PMV measurement results.
PersonA
B

Clo-value
0.21
0.22

PMV (170cm)
-2.52
-2.43

PMV (110cm)
-2.86
-2.75

PMV (10cm)
-2.64
-2.54

describes that a comfortable environment has the PMVvalue in ±0.5. 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 an action such as turning on a heater or recommending the respective person to layer another clothing (e.g.,
sweater).

5. Pose Estimation using Depth Images with
Occlusions
5.1. Generating training data with occlusions for a
depth image-based pose estimation
We use our depth image-based human pose estimation
method [17]. The method trains a deep neural network using a set of depth images with pixel-wise body part labels.
In such a learning-based method, the amount and the qualityof training data is a key to a high estimation performance. Ina usual residential environment, people often use furnitureor other apparatus and their bodies may be largely occluded.
When we see a person working at a desk from his/her front
side, for example, the lower part of the body is almost occluded. Such largely-occluded data should also be includedin the training data.
Since our method [17] uses a computer graphics tool(i.e., Maya [2]) for generating data, we can create various largely-occluded situations by putting arbitrary objects

around a human. As typical cases, we consider desks andchairs as objects occluding a human body. Fig. 9 shows
how the labeled human body image is generated. We set thecamera pose to the one on our robot system (30 degrees of
downward looking angle and 110cm height, see Fig. 3(a)).
We moved the camera on a circle around the vertical axis
at the human model as shown in the left figure, and generate images from various viewing directions. The right figure shows the view from a camera pose, indicating the casewhere the upper legs are completely occluded.
We change the following three factors on the object
placement to generate a variety of occlusions: the type ofchairs, the existence of a desk, and the distance between ahuman and a desk. Fig. 10 shows the variations of scenes
obtained by changing these factors. Object regions in theimages are treated as background in the final images. Fig.
11 shows example pairs of depth image and body part labels.

5.2. Experimental results
5.2.1 Classifier and dataset
We used the same FCN (fully connected network) as theone used in [17]. The input to the network is a depth image with 212 × 212 pixels and the output is twelve labels(eleven for body parts and one for background). We generated 764,832 images and trained the network using three
TITAN X GPU boards. The learning process converged
about in seven days. The generated dataset and others canbe downloaded at [1].
5.2.2 Evaluation using synthetic data
We generated 29,608 test images using in the same way as
the training image generation. Fig. 12 shows example estimation results. Table 4 summarizes the results in a confusion matrix. These results show that the body parts are wellrecognized even under occlusion.
5.2.3 Results for real data
We then tested the trained classifier for the depth image regions extracted using the thermal point cloud (see Sec. 3.2).

Figure 11: Example pairs of body part label images (first row) and depth images (second row).
Table 4: Confusion matrix for synthetic data.
Estimated

Actual

HD

TRS

LUA

RUA

LFA

RFA

Hip

LUL

RUL

LLL

RLL

BG

HD
TRS
LUA
RUA
LFA
RFA
Hip
LUL
RUL
LLL
RLL
BG

0.97
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.00
0.95
0.00
0.01
0.00
0.00
0.02
0.00
0.00
0.00
0.00
0.02
0.00
0.06
0.87
0.00
0.03
0.00
0.00
0.00
0.00
0.00
0.00
0.04
0.01
0.10
0.00
0.82
0.00
0.04
0.00
0.00
0.00
0.00
0.00
0.03
0.00
0.01
0.02
0.00
0.88
0.03
0.00
0.00
0.00
0.00
0.00
0.06
0.01
0.00
0.00
0.03
0.00
0.92
0.00
0.00
0.00
0.00
0.00
0.04
0.00
0.04
0.00
0.00
0.02
0.00
0.89
0.00
0.01
0.00
0.00
0.04
0.00
0.00
0.00
0.00
0.00
0.00
0.03
0.88
0.03
0.01
0.01
0.04
0.00
0.00
0.00
0.00
0.00
0.01
0.03
0.00
0.92
0.00
0.01
0.03
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.88
0.05
0.06
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.01
0.92
0.05
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
HD: Head, TRS: Torso, LUA: left upper arm, RUA: right upper arm, LFA: left forearm, RFA: right forearm
LUL: left upper leg, RUL: right upper leg, LLL: left lower leg, RLL: right lower leg, BG: backgroundFig. 13 shows results for several real scenes. The columns
shows the following from left to right: scene images, thermal point clouds, extracted human regions in the depth image, the estimation results. Note that RGB images in thefirst column are not used in pose estimation. The estimation results are reasonably good even in sometimes heavy
occlusions.

6. Conclusions and Discussion
This paper describes two applications of thermal point
cloud in robotic monitoring task: thermal comfort measurement and human pose estimation. Thermal point cloud isgenerated by combining data from a calibrated pair of thermal and depth cameras and effective in extracting humanregions. Thermal comfort is evaluated using PMV, whichis measured based on a combination of environmental andpersonal factors. One of the personal factors, clothing insulation, is automatically estimated by analyzing an extracted
human thermal image. Pose estimation is carried out using
a depth image-based deep neural network. Considering frequent largely-occluded situations, we developed a methodof generating training data with various occlusion cases.
The classifier trained by using the data is shown to be effective in the experiments using a synthetic and a real data
set.
Currently, the factors in measuring thermal comfort areapproximated or assumed; for example, we use a simple
temperature difference-based method for estimating clo-

(a) Changing chair types.

(b) Switching the existence of a desk.

Figure 12: Estimation results for synthetic data. Left: input
depth images, Center: correct part labels, Right: estimation
results.
(c) Changing distances to the desk: 50cm, 60cm, and 70cm from left to right.

Figure 10: Object placement variations.
values. Although the obtained clo-values are reasonable,
we need to consider a more complicated model (e.g., [9]) or
more various combinations of clothings and environments.
Applying pose estimation, activity recognition, and activity classification techniques to determine the activity levelis also an interesting research direction.
The current pose estimation method seems to work well,
but needs to be tested for a more variety of situations. Connecting the pose estimation results to assistive/supportive
operations, such as vital sign recognition using a specificbody parts (e.g., head part), is also necessary for real application situations.

Acknowledgment
This work is in part supported by JSPS KAKENHI Grant
Numbers 25280093/17H01799 and the Hibi Science Foundation.

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