Skin Temperature Measurement based on
Human Skeleton Extraction and Infra-red Thermography
An Application of Sensor Fusion Methods in the Field of Physical Training
Julia Richter
1
, Christian Wiede
1
, Sascha Kaden
1
, Martin Weigert
2
and Gangolf Hirtz
1
1
Department of Electrical Engineering and Information Technology,
Chemnitz University of Technology, Reichenhainer Str. 70, 09126 Chemnitz, Germany
2
Department of Behavioural and Social Sciences, Chemnitz University of Technology,
Reichenhainer Str. 70, 09126 Chemnitz, Germany
Keywords:
Infra-red Thermography, Skin Temperature, Sensor Fusion, Human Skeleton, Physical Training.
Abstract:
Skin temperature measurements play a vital role in the diagnosis of diseases. This topic is also increasingly
investigated for applications in the field of physical training. One of the limitations of state-of-the-art methods
is the manual, time-consuming way to measure the temperature. Moreover, extant literature gives only little
insight into the skin temperature behaviour after the training. The aim of this study was to design an automatic
method to measure the skin temperature during and after training sessions for the biceps brachii. For this pur-
pose, we fused thermal images and skeleton data to locate this muscle. We could successfully demonstrate the
working principle and observed a temperature increase even several minutes after the end of the training. This
study therefore contributes to the automation of skin temperature measurements. A transfer of our approach
could be beneficial for other application fields, such as medical diagnostics, as well.
1 INTRODUCTION
Infra-red thermography plays an increasingly impor-
tant role in a wide range of application fields. In
medicine, it is a non-invasive diagnostic method to
detect abnormal body temperatures, which are indi-
cators for a variety of diseases, such as breast cancer
and diabetic vascular disorder (Lahiri et al., 2012) or
arthritis (Ring and Ammer, 2012). In sports medicine,
infra-red thermography is applied to measure the de-
gree of regeneration or to detect overuse reactions in
order to avoid injuries (Hildebrandt et al., 2012).
Recently, researchers have shown an increased in-
terest in the investigation of the relationship between
skin temperature and muscle activity during training.
To date, however, only few studies examined the tem-
perature profile after training sessions. Moreover, the
methods presented in previous work determined the
skin temperature of a region in a manual way and are
therefore time-consuming. The purpose of this study
is to facilitate skin temperature determination of a cer-
tain region by introducing an automatic method. We
fused images from a thermal camera as well as skele-
ton data from the Kinect sensor. For this, we have
developed a calibration method that enables this sen-
sor fusion. We demonstrated the working principle
for the biceps brachii and analysed the heat develop-
ment during and after the training with biceps curls.
Therefore, this study makes a major contribution to
research on the relationship between skin temperature
and muscle activity in the field of sports science. At
this point, we assumed that the muscle is not covered
by clothes. The study has been organised as follows:
Section 2 examines the extant literature on infra-red
thermography and calibration. Section 3 begins by
presenting the sensor system and the calibration pro-
cedure, including both intrinic and extrinsic calibra-
tion as well as the mapping process. Subsequently,
this section explains the skin temperature measure-
ment. Section 4 is concerned with the evaluation
methodology, while Section 5 presents and discusses
the results. We close the paper with conclusions and
an outlook at future work in Section 6.
2 RELATED WORK
2.1 Infra-red Thermography
More recent attention has focused on the evaluation
of surface temperature changes in training diagnos-
Richter J., Wiede C., Kaden S., Weigert M. and Hirtz G.
Skin Temperature Measurement based on Human Skeleton Extraction and Infra-red Thermography - An Application of Sensor Fusion Methods in the Field of Physical Training.
DOI: 10.5220/0006095100590066
In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), pages 59-66
ISBN: 978-989-758-227-1
Copyright
c
2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
59
tics. Variations in temperature can indicate the grade
of muscle activity, which can be visualised by evaluat-
ing infra-red thermography images. In the following,
existing literature is reviewed with the focus on the
applied analysis methods.
Bartuzi et al. revealed statistically significant cor-
relations between the skin temperature and EMG pa-
rameters for the biceps brachii by employing infra-
red thermography (Bartuzi et al., 2012). They ad-
justed a thermal camera in such a way that the cap-
tured thermal images showed only the muscle itself.
The skin temperature was then determined using a
special software that analysed manually defined re-
gions. Formenti et al. examined the skin temperature
development of the region that covers the calf mus-
cle after standing calf raise exercises (Formenti et al.,
2013). They observed an increasing skin tempera-
ture during and also two minutes after the exercise,
which is an indicator that the active muscle emitted
heat. Their temperature determination works semi-
automatically: they manually selected a region of in-
terest (ROI), which was located on the calf and then
automatically determined the five warmest pixels. In
the next step, they considered a region of five by five
pixels around each of these warmest pixels and av-
eraged them. This average was regarded as the final
temperature for the previously selected ROI. Ludwig
et al. thereupon verified a correlation between this
very method and a method that simply averages the
pixels belonging to the selected ROI (Ludwig et al.,
2014).
Other approaches were based on anatomical infor-
mation, which allowed the derivation of muscle lo-
cations in the thermal image. Two examples are the
studies of Bandeira et al. and Neves et al.: accord-
ing to the arrangement of the different muscles in the
thigh, Bandeira et al. manually selected ROIs for tem-
perature determination (Bandeira et al., 2012). In a
similar manner, Neves et al. estimated the centre of
the biceps brachii by exploiting the knowledge about
the muscle anatomy (Neves et al., 2014).
Several methods employ markers that were at-
tached to the skin. In this way, specific muscle tem-
peratures were determined by manually evaluating the
region between these markers in the recorded ther-
mal images. Fr
¨
ohlich et al., for example, bonded
corks to a human body in order to measure temper-
atures belonging to various regions (Fr
¨
ohlich et al.,
2014), while Neves et al. fixed tapes to the biceps
brachii. Further significant analyses and discussions
on thermographic investigations, their techniques and
influencing factors were presented in the works of
Fern
´
andez et al. and Ring and Ammer (Fern
´
andez-
Cuevas et al., 2015), (Ring and Ammer, 2015).
The evidence presented in this section suggest
that there is need to investigate automatic methods
for skin temperature evaluation. To date, automatic
techniques to measure muscle temperature cannot be
found in extant literature. Therefore, we propose such
an automatic method that employs skeleton extraction
algorithms to detect specific muscles. This enables an
automatic localisation and analysis of specific muscle
regions. Moreover, we demonstrated that after per-
forming biceps curls, the temperature of the surface
surrounding the biceps brachii increases, which sup-
ports the findings of Formenti et al.
2.2 Calibration
In order to combine skeletal data with thermal images,
the Kinect version 1.0 skeleton joint coordinates and
the images of the thermal camera had to be fused. For
this procedure, a calibration of both sensors was re-
quired to allow a mapping of the thermal image to the
Kinect RGB image, in which the skeleton joints are
represented in 2-D coordinates.
The combination of a thermal camera and an RGB
camera or a depth sensor, such as the Kinect, was
widely used for different applications. Thomanek
et al. implemented a pedestrian detection algorithm
based on both RGB images and thermal images
(Thomanek et al., 2011). On the one hand, pedestri-
ans could be detected in thermal images even at night,
when the detection on the RGB image failed. On the
other hand, the detection in thermal image failed on
warm days and under strong solar radiation. In these
cases, however, pedestrians could be detected in the
RGB image. The principle of compensating single
sensor weaknesses was extended by Geschwandtner
et al. (Gschwandtner et al., 2011). They utilised a
thermal camera, a laser range scanner and an RGB
camera in the field of autonomous driving. Other
applications of thermal imaging in combination with
other sensors are the maintenance of power equip-
ment (Nakagawa et al., 2014), the building sector (Vi-
das et al., 2013) and e-rehabilitation (Richter et al.,
2016). All these applications required a reliable cal-
ibration to fuse several optical systems to one com-
bined world coordinate system. For this purpose,
three aspects had to be considered: an adequate cal-
ibration target, whose pattern is detectable for every
employed sensor, as well as the intrinsic and the ex-
trinsic calibration.
In the visible light spectrum, a planar checker-
board is the commonly used calibration target. By
detecting the inner checkerboard corners, information
about distortions in the RGB images can be estimated.
However, this working scheme is not applicable for
VISAPP 2017 - International Conference on Computer Vision Theory and Applications
60
Figure 1: Sensor system with thermal camera and Kinect.
thermal images. Even if a checkerboard with appro-
priate material is illuminated by an infra-red heater,
the edges of the checkerboard appear smoothed and
cannot be accurately localised. To overcome this
problem, alternative calibration targets have been de-
veloped. Nakagawa et al. proposed two plastic plates
with inverse circular patterns (Nakagawa et al., 2014),
which could be plugged into each other. For the cal-
ibration of a thermal and an RGB sensor, one plate
was plugged onto the other and served as an isolating
layer when they were heated. The isolating layer was
then removed, so that only the heated circles were vis-
ible in the thermal image. At the same time, the circle
boundaries could be detected in the RGB image as
well. In comparison to that, Vidas et al. used a planar
mask with cut-out squares, which was held in front
of a backdrop with a different temperature (Vidas
et al., 2013). With this method, checkerboard corners
could more accurately detected than in case of the
commonly used checkerboard calibration described
above. Rangel et al. introduced a similar approach,
but instead of squares, they cut out asymmetrical cir-
cles to increase the accuracy (Rangel et al., 2014).
They heated this pattern to enable the circle detection
in the thermal image. In contrast to the calibration of
RGB sensors, the characteristic of this kind of ther-
mal calibration targets is that the edges are blurred in
the thermal image. The advantage of circles in such
cases is a more stable detection: the centre of a cir-
cles can be detected more robustly than edges in such
cases. While all these approaches used passive cali-
bration targets, Ellmauthaler et al. suggested an LED
grid (Ellmauthaler et al., 2013), whereas Gschwandt-
ner et al. proposed a checkerboard with resistors as
active calibration targets (Gschwandtner et al., 2011).
An overview about different calibration targets was
presented by Rangel et al. (Rangel et al., 2014).
The intrinsic calibration in all presented ap-
proaches was always performed according to the
method of Zhang (Zhang, 2000), which assumed a
pinhole camera model. Sections 3.2 and 3.3 present a
detailed explanation of the intrinsic and extrinsic cal-
ibration procedures we applied in our study.
3 METHODS
This section presents the sensor system, the calibra-
tion steps and the procedure to measure the skin tem-
perature.
3.1 Sensor System
The sensor system comprises an RGB-D sensor, i. e.
a Kinect version 1.0, which additionally provides a
human skeleton stream, and a thermal camera, i. e. a
FLIR A35sc. The thermal camera has a thermal reso-
lution of 50 mK. The spatial resolution of the sensor
is 320×256 pixel. The rotation between both sensors
was as small as possible, so that both sensors share
approximately the same re-projection plane. The sen-
sor system is illustrated in Figure 1.
3.2 Intrinsic Calibration
The aim of the camera calibration is the mapping of
the thermal image onto the Kinect RGB image, in
which the skeleton joints are represented in 2-D im-
age coordinates.
For all calibration steps that are connected with
the thermal sensor, we used an aluminium plate with
cut-out circles, which is similar to Rangel et al.
(Rangel et al., 2014). In contrast to Rangel et al., the
circles on our target are symmetrically arranged, see
Figure 2. Similar to Vidas et al. (Vidas et al., 2013),
we used a TFT monitor as a backdrop that emits a
different temperature, as illustrated in Figure 2. By
using this calibration target, we could detect circles
in the thermal image according to the algorithm by
Suzuki and Abe (Suzuki and Abe, 1985).
The thermal camera was intrinsically calibrated
using the method proposed by Zhang (Zhang, 2000).
As a result, we obtained the camera matrix of the ther-
mal camera K
therm
. The camera matrix K is generally
defined as (Hartley and Zisserman, 2004):
K =
α
x
s p
x
0 α
y
p
y
0 0 1
(1)
This matrix contains the scale factors in x and y
direction α
x
and α
y
, the skew s and the x and y coor-
dinate p
x
and p
y
of the principal point.
In order to undistort the thermal image in ra-
dial and tangential direction, the method by Heikkila
(Heikkila and Silven, 1997) was applied. The RGB
images from the Kinect were not undistorted since
there were almost no distortions. However, the Kinect
was intrinsically calibrated to obtain the camera ma-
trix of the RGB sensor K
RGB
. For this calibration,
Skin Temperature Measurement based on Human Skeleton Extraction and Infra-red Thermography - An Application of Sensor Fusion
Methods in the Field of Physical Training
61
we applied the commonly used checkerboard target.
Both K
RGB
and K
therm
were needed for the extrinsic
calibration and the mapping, which are described in
the following sections.
3.3 Extrinsic Calibration
The calibration setup with the circle grid and the TFT
monitor that has been introduced in Section 3.2 was
used for the extrinsic calibration. Figure 2 shows the
view of both RGB and thermal sensor. We eluded
large rotations between the calibration target and the
sensors, so that elliptical shapes and consequent false
detections of the used circle detection algorithm could
be avoided. The centre points of the circles that were
detected in both RGB and undistorted thermal image
and their 3-D correspondences were used for the ex-
trinsic calibration that finally results in the rotation
matrix R and the translation vector t, see (Hartley and
Zisserman, 2004). R and t are given with respect to
the world coordinate system, which is located at the
RGB sensor of the Kinect. In other words, R and t de-
scribe the rotation and translation of the thermal sen-
sor with respect to the RGB sensor.
Figure 2: Calibration target in front of a TFT monitor, cap-
tured by the thermal camera (left) and the Kinect RGB cam-
era (right).
In the next step, the undistorted thermal image
could be mapped onto the RGB image by using the
obtained camera matrices K
RGB
and K
therm
, the rota-
tion matrix R and the translation vector t. This proce-
dure is described in the following section.
3.4 Mapping
The mapping of the undistorted thermal image onto
the RGB image comprised the back- projection of the
undistorted thermal image to a plane in the world with
a defined distance µ from the sensor using K
therm
, and
the forward projection of the resulting world points to
the RGB image using K
RGB
.
The back projection can be generally described as
(Hartley and Zisserman, 2004):
X(µ) =
M
1
(µx p
4
)
1
, (2)
Figure 3: Thermal data was mapped to the RGB image.
The mapping result is shown in the present alpha-blended
image.
whereas X(µ) denotes the back-projected world
point at the distance µ. In our application, µ was set
to a value of 3 metres. M is the first 3×3 sub-matrix
of the projection matrix P. p
4
is the fourth column of
P, which is generally defined as follows:
P = K
R|t
=
M|p
4
(3)
When we back-projected the thermal image to the
world, K
therm
was used to calculate the projection ma-
trix P. Since the world coordinate system was located
at the RGB sensor, R and t that have been calcu-
lated during the extrinsic calibration were used for
this back-projection step. The back-projected points
from the thermal image X were then projected onto
the RGB image according to Equation 4, which de-
scribes the general forward projection. At this point,
P was calculated using K
RGB
as well as a unit matrix
for R and a zero vector for t.
x = PX (4)
The resulting coordinates x in the RGB image
were afterwards bi-linearly interpolated. The forward
projection was realised by means of a look-up-table.
The result of this mapping process is exemplary illus-
trated in Figure 3.
3.5 Skeleton Extraction
We localised the biceps brachii muscle by means of
the 2-D position of skeletal joints in the mapped im-
ages. For this purpose, we employed the skeleton ex-
traction algorithm that is provided by the Kinect ver-
sion 1.0 (Shotton et al., 2013). This algorithm calcu-
lates depth features from depth images and uses these
features to train a randomised decision forest. With
the help of this classifier, both 3-D and 2-D positions
of overall 20 joints can be predicted. In our approach,
we used only the 2-D elbow and the shoulder joints
for the biceps brachii localisation on both left and
right upper arm.
VISAPP 2017 - International Conference on Computer Vision Theory and Applications
62
3.6 Skin Temperature Measurement
The biceps brachii temperature measurement using
shoulder and elbow joints is illustrated in Figure 4.
We discovered that the original shoulder joints 5
and 9 were not suitable to accurately localise the bi-
ceps brachii. Therefore, their original locations were
shifted by a parametrisable distance along the con-
necting, dashed line between the left and right shoul-
der in outward direction.
5
6
9
10
5'
9'
Figure 4: Left: Skin temperature measurement principle
using shifted shoulder joints 5’ and 9’ and elbow joints 6
and 10. Joint indices are according to the Kinect indexing.
Right: Thermal image with localised biceps brachii (black
line).
In addition to this modification, we only used a
parametrisable percentage of the pixels lying on the
line connecting 5’ and 6 or 9’ and 10 respectively,
staring symmetrically from the centre of this line. In
this study, the percentage was set to 60 %, which re-
sults in a number of R and L temperature values for
the right and left biceps brachii respectively. Finally,
the skin temperature on the biceps brachii of the right
arm ϑ
r
(t) and the left arm ϑ
l
(t) were calculated by
determining the mean of the R and L temperature val-
ues of the pixels located on the black lines. These
pixel values are denoted as temp
r
(t) and temp
l
(t) re-
spectively, see Equation 5. t corresponds to the mea-
surement point.
ϑ
r
(t) =
1
R
·
R
r=1
temp
r
(t) (5a)
ϑ
l
(t) =
1
L
·
L
l=1
temp
l
(t) (5b)
4 EVALUATION
METHODOLOGY
In our experiments, we measured the biceps brachii
temperature of the left and the right arm for four per-
sons (P = 4) while they performed three sets of biceps
curls with their right arm. The weight of the dumb-
bell was 7 kg. The distance from the camera was ap-
proximately 3 meters. The measurement schedule is
presented in Table 1. At the beginning, each person
had a time of 15 minutes for acclimatisation. The first
measurement was taken directly after this acclimati-
sation phase (t = 1). Then every person had to per-
form as many biceps curls as possible for every set.
Directly after each set, a measurement was performed
(t = 2, 3, 4). The pause between two consecutive sets
was 1 min. After the last set had been performed,
nine further measurements were taken, whereas the
time difference given in Table 1 is relative to the mea-
surement with index t=4. The measurements for both
arms were taken synchronously.
For evaluation, the measurements were equidis-
tantly displayed, which means that the distance does
not correspond to the time that elapsed between the
measurement points.
We used three ways to generate temperature pro-
files. The first profile exemplarily displays the abso-
lute temperature measured for the right arm ϑ
r
(t) and
left arm ϑ
l
(t) of one of the probands, see Figure 5.
For the second profile, see Figure 6, we aimed at vi-
sualising the averaged relative changes ϑ
r,rel
(t) and
ϑ
l,rel
(t) with respect to the starting temperature t
0
of
each arm, see Equations 6. The starting temperature
corresponds to the measurement time t = 1, i. e. after
acclimatisation. This type of representation was re-
quired for the evaluation of different probands’ data
to eliminate the influence of different environmental
temperatures for the different probands.
ϑ
r,rel
(t) =
1
P
·
P
p=1
(ϑ
r
(t) ϑ
r
(t
0
)) (6a)
ϑ
l,rel
(t) =
1
P
·
P
p=1
(ϑ
l
(t) ϑ
l
(t
0
)) (6b)
The third profile illustrates the averaged difference
between the right and the left arm for each measure-
Table 1: Measurement schedule.
Measurement Point t Description
1 After acclimatisation
2 After first set
3 After second set
4 After third set
5 After 1 min
6 After 2 min
7 After 3 min
8 After 4 min
9 After 5 min
10 After 10 min
11 After 15 min
12 After 20 min
13 After 25 min
Skin Temperature Measurement based on Human Skeleton Extraction and Infra-red Thermography - An Application of Sensor Fusion
Methods in the Field of Physical Training
63
ment time and is defined as
ϑ
diff
(t) =
1
P
·
P
p=1
(ϑ
r
(t) ϑ
l
(t)) . (7)
This profile type allows to eliminate environmen-
tal changes during a proband’s training session and is
visualised in Figure 7.
5 RESULTS AND DISCUSSION
In this section, the obtained temperature profiles that
were described above are presented. The aim of these
analyses is to investigate the difference between the
temperature of the right biceps, which was active, and
the left biceps, which was passive. We discuss every
profile and explain occurring effects.
Absolute Temperatures of a Single Person. Fig-
ure 5 compares the absolute temperatures of the bi-
ceps of the right and the biceps of the left arm. From
this figure, we can see that the temperature of the right
biceps increases more than the temperature of the left
biceps. What is interesting is that the maximal tem-
perature of the active biceps was reached only several
minutes after the training. These results are consistent
with the findings of Formenti et al. (Formenti et al.,
2013).
1 2 3 4
5 6
7 8 9 1011 1213
28
29
30
31
Measurement Point t
ϑ
r
(t) and ϑ
l
(t) in
C
Absolute Temperature
left
right
Figure 5: Absolute temperatures over time for one proband.
A possible explanation for this behaviour is that
the cardiovascular system reacts to the physical stress
by providing more blood to the active muscle. The
blood has core body temperature, which results in the
warming of the muscle. The adjacent tissue warms
up as well due to thermal conduction. This described
process needs time and results in the delay that is vis-
ible in the graph.
Temperatures Relative to the Starting Tempera-
ture of all Persons. To compare the temperature be-
haviour of the right and left biceps for more than one
proband, we examined the mean relative temperature,
see Figure 6. This type of evaluation allows the de-
termination of temperature changes with respect to a
start time. Taking the average of all probands, we ob-
tained results that are comparable to the previous pro-
file with one proband. Moreover, it can be seen that
the temperature of the right arm increases by approx-
imately 1.5
C on average. The temperature of the
left arm decreases slightly at the beginning. An ex-
planation for this is that the blood flow might even be
reduced in the passive arm.
1 2 3 4
5 6
7 8 9 1011 1213
1
0
1
2
Measurement Point t
ϑ
r,rel
(t) and ϑ
l,rel
(t) in
C
Relative Temperature
left
right
Figure 6: Mean relative temperature with respect to start
temperature over time for all probands.
Temperature Difference Between Arms of all Per-
sons. In order to eliminate environmental influences
during one training session, we evaluated the mean
difference between the temperatures of the right and
the left arm. By doing this, eventual temperature
changes in the training room would not influence the
measurement, because this change would influence
both arms. Moreover, by employing this evaluation
method, we can determine the temperature difference
compared to the corresponding limb, i. e. the left arm,
which is passive. Figure 7 provides the according
temperature profile.
The graph illustrates that, after the first set, the
right arm has a higher temperature as the left arm.
Another finding to emerge from this graph is that, av-
eraged over all probands, the right arm is more than
1
C warmer than the left arm at the maximum.
VISAPP 2017 - International Conference on Computer Vision Theory and Applications
64
1 2 3 4
5 6
7 8 9 1011 1213
0
0.5
1
Measurement Point t
ϑ
diff
(t) in
C
Difference Temperature
Figure 7: Mean temperature difference between right and
left arm over time for all probands.
6 CONCLUSIONS AND FUTURE
WORK
In this study, we presented a method to automati-
cally determine and evaluate skin temperatures. This
method is based on a sensor fusion of a thermal cam-
era and the Kinect. In order to fuse both sensor data,
we introduced a novel calibration procedure and de-
signed a special calibration target. The obtained re-
sults provide further support for the hypothesis that
the skin temperature increases during and after the
training. Moreover, we evaluated relative temperature
measurements, i. e. differences between active and
passive muscles, instead of absolute measurements.
This allows the elimination of environmental changes
in a training session.
Further research could investigate the influence of
subcutaneous fat tissue and clothing on the thermal
conduction. Moreover, future research might explore
skin temperature profiles of other muscles as well.
Another aspect in our future work will be the detec-
tion of skeleton joints directly on the thermal image,
which could considerably simplify the sensor system.
Continued efforts are needed to transfer our ap-
proach to other applications fields, such as medical
diagnostics, which can profit from automatic temper-
ature measurements.
ACKNOWLEDGEMENTS
This project is funded by the European Social Fund
(ESF). We furthermore would like to express our
thanks to all the persons who contributed to this
project during the recordings.
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