Detection of Muscle Activation during Resistance Training Using Infrared Thermal Imaging
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sensors
Article
Detection of Muscle Activation during Resistance Training
Using Infrared Thermal Imaging
Haemin Jung 1,† , Jeongwung Seo 2,† , Kangwon Seo 2,† , Dohwi Kim 3 and Suhyun Park 1, *
1 Department of Electronic and Electrical Engineering, Ewha Womans University, Seoul 03760, Korea;
goalssla12@cau.ac.kr
2 School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Korea;
cyh4681@cau.ac.kr (J.S.); skangwon@naver.com (K.S.)
3 Thermoeye, Seoul 06979, Korea; oranss@thermoeye.co.kr
* Correspondence: suhyun.park@ewha.ac.kr
† These authors contributed to this work equally.
Abstract: Infrared thermal imaging has been widely used to show the correlation between thermal
characteristics of the body and muscle activation. This study aims to investigate a method using
thermal imaging to visualize and differentiate target muscles during resistance training. Thermal
images were acquired to monitor three target muscles (i.e., biceps brachii, triceps brachii, and deltoid
muscle) in the brachium while varying the training weight, duration, and order of training. The ac-
quired thermal images were segmented and converted to heat maps. By generating difference heat
maps from pairs of heat maps during training, the target muscles were clearly visualized, with an
average temperature difference of 0.86 ◦ C. It was observed that training order had no significant
effect on skin surface temperature. The difference heat maps were also used to train a convolutional
neural network (CNN) to show the feasibility of target muscle classification, with an accuracy of
92.3%. This study demonstrated that infrared thermal imaging could be effectively utilized to locate
Citation: Jung, H.; Seo, J.; Seo, K.;
and differentiate target muscle activation during resistance training.
Kim, D.; Park, S. Detection of Muscle
Activation during Resistance Training Keywords: infrared thermal imaging; muscle activation; skin temperature; deep learning; brachium
Using Infrared Thermal Imaging.
Sensors 2021, 21, 4505. https://
doi.org/10.3390/s21134505
1. Introduction
Academic Editor: Paola Saccomandi Resistance training exercises cause a subject’s muscles to contract against an external
resistance, which eventually strengthens and builds muscle endurance. Manipulating
Received: 4 June 2021
training variables (e.g., load weight, recovery period, and lifting speed) can produce a wide
Accepted: 26 June 2021
range of training stimuli [1]. Thus, various strategies have been explored to quantify the
Published: 30 June 2021
effects of resistance training. Marston et al. proposed a metric called exercise density and
investigated its relation to a physiological marker of internal training intensity along with
Publisher’s Note: MDPI stays neutral
traditional measures (e.g., number of repetitions and volume load) of external training
with regard to jurisdictional claims in
intensity [2]. Another study calculated mechanical work to quantify external resistance
published maps and institutional affil-
training volume during a resistance training session [3]; however, this process is time
iations.
consuming and requires specialized equipment, thereby limiting its practical application [4].
Although quantitative amounts of training can be measured, the direct effects of training
on the muscles are not considered. Surface electromyography (sEMG) has been widely
used to quantify muscle effort during resistance exercises via direct monitoring of muscle
Copyright: © 2021 by the authors.
activation [5]. By evaluating the zero-crossing levels of sEMG signals, assessments of a
Licensee MDPI, Basel, Switzerland.
subject’s fatigue endurance have been presented [6]. However, sEMG requires placement
This article is an open access article
of surface electrodes on the target muscles, which impede the subject’s movements during
distributed under the terms and
training. In addition, there are various factors that affect impedance measurements between
conditions of the Creative Commons
Attribution (CC BY) license (https://
muscles and sEMG electrodes, such as biological tissue composition, distance between
creativecommons.org/licenses/by/
muscle and electrodes, and size of the electrodes [7,8].
4.0/).
Sensors 2021, 21, 4505. https://doi.org/10.3390/s21134505 https://www.mdpi.com/journal/sensors
Sensors 2021, 21, 4505 2 of 12
During resistance training, heat is dissipated from muscle contraction owing to
metabolism and blood perfusion. Therefore, some studies have investigated the rela-
tionship between temperature changes and training [9–14]. A previous study showed
that prolonged exercise increased the amount of heat dissipated from muscle [9]. Several
works have utilized thermistors to investigate changes in the local temperature of muscles
after exercise [10]; however, these measurements were invasive, as the thermistors were
inserted into the subjects’ veins. Owing to its noninvasive and noncontact nature, infrared
thermal imaging has been widely used for various biomechanical applications [11–13].
Using infrared thermal imaging, several studies have investigated skin surface temperature
changes related to muscle activation [9,15–19]. It is known that human skeletal muscles
have a mechanical efficiency of 30–65%, and there is heat dissipation (i.e., temperature
change) related to muscle contraction [9]. Zagrodny et al. showed that there is a correlation
between sEMG and thermal characteristics of the skin surface [15]. Daud et al. also showed
a correlation between sEMG signals and skin temperature measured from thermal imag-
ing [16]. In [17], the authors observed that the temperature of the quadriceps before and
after training showed substantial agreement with changes in skin temperature following
physical exercise. Another study investigated localized heating of limbs in individuals of
different age groups [18]. Furthermore, temperature changes from high-intensity exercise
associated with muscle fatigue have also been investigated [18,19]. Therefore, previous
studies have proven that the change of skin surface temperature measured from thermal
imaging is highly correlated with the isolation of muscle activation. Although the cor-
relation between muscle activation and thermal characteristics has been demonstrated
in some of the previous studies, these were limited to a single region or muscle. Thus,
it was necessary to investigate a method to locate and differentiate muscle activation
during training.
This study aimed to investigate the use of infrared thermal imaging to assess the
isolation of muscle activation during resistance training. Based on previous studies, it was
assumed that the skin surface temperature measured from thermal imaging can be directly
related to muscle activation. Thermal images were acquired during resistance training
while varying the training conditions. The thermal images were then processed and
analyzed to visualize muscle activation. To evaluate the performance of the proposed
method, a deep-learning model was trained and tested on the processed thermal images
for classification of the target muscle activation. The advantage of this study is that the
activations of different muscles can be located and differentiated by utilizing the thermal
images acquired in a noninvasive and noncontact manner.
2. Materials and Methods
2.1. Experimental Setup
Figure 1 shows the experimental setup used to detect muscle activation. Thermal
images were acquired using a thermal camera (A65SC, FLIR systems Inc., Wilsonville,
OR, USA) with a resolution of 640 × 512 pixels and a thermal sensitivity of
Sensors 2021, 21, 4505 3 of 12
Sensors 2021, 21, 4505 3 of 12
and weight of the subjects were 26.2 ± 1.32 years, 175 ± 0.89 cm, and 67.4 ± 2.79 kg, respec-
respectively. The subjects
tively. The subjects exposed
exposed their brachium
their brachium to air
to air for 15 for
min15before
min before thermal
thermal imageimage
acqui-
acquisition.
sition.
Figure1.
Figure 1. Experimental
Experimental setup for detection
detection of
of muscle
muscle activation.
activation.Thermal
Thermalimages
imagesofofthe
thebrachium
brachium
were obtained using a thermal camera during the training sessions.
were obtained using a thermal camera during the training sessions.
2.2.
2.2. Experimental
Experimental Procedures
Procedures
The
The subjects
subjects performed
performed training
training movements
movements to activate
activate each
each of
of the
the target
target muscles.
muscles.
All
All training was performed
training was performedatatthe
therate
rateofof
1515 repetitions
repetitions perper minute
minute andandtimedtimed
withwith a
a met-
metronome to ensure periodicity of movements. The total number of sets
ronome to ensure periodicity of movements. The total number of sets per session, recov- per session,
recovery
ery time, time, and repetitions
and repetitions per
per set set varied
were were varied
betweenbetween experiments.
experiments. Three Three different
different exper-
experiments were conducted to investigate the effects of isolated resistance training
iments were conducted to investigate the effects of isolated resistance training on the skin- on
the skin-surface
surface temperature.
temperature. A summary
A summary of the
of the three three training
training conditions
conditions assessedassessed in thisis
in this study
study is shown
shown in Table 1. in Table 1.
Table1.1.Summary
Table Summaryofoftraining
trainingconditions
conditionsfor
forthe
theexperiments
experiments(#:
(#:Number).
Number).
Training
TrainingWeight
Weight Recovery
Recovery ## of
ofSets
Sets per # of # of # of #Repetitions
of Repetitions # of# of Target
Target
Experiment
Experiment [kg] Time [s] per Session Sessions per Set Subjects Muscle
[kg] Time [s] Session Sessions per Set Subjects Muscle
I.I.Weight
Weight 5 5and
and1010 300
300 1 set
1 set 1 1 15 15 1 1 Biceps
Biceps
II. Duration 10 92 3 sets 1 8 1 Biceps
II. Duration 10 92 3 sets 1 8 1 Biceps
III. Muscle 5 or 10 92 3 sets 3 12 5 All Three
III. Muscle 5 or 10 92 3 sets 3 12 5 All Three
To
To investigate
investigate the the effects
effectsofofweight
weightvariation
variation (experiment
(experiment I) on
I) on thethe subject’s
subject’s skinskin
sur-
surface
face temperature changes, the biceps was chosen as the target muscle. A single subject
temperature changes, the biceps was chosen as the target muscle. A single subject
performed
performedone oneset
setofoffull
fullarm-curl
arm-curlsessions,
sessions,where
whereeach
eachset
setincluded
included 1515
repetitions (i.e.,
repetitions 6060
(i.e., s
duration) to ensure that the training session fully stimulated the target muscle.
s duration) to ensure that the training session fully stimulated the target muscle. This was This was
followed by 5 min of recovery. Thermal videos of two sessions were acquired, while the
followed by 5 min of recovery. Thermal videos of two sessions were acquired, while the
training sessions were performed using 5 kg and 10 kg dumbbells separately. In addition,
training sessions were performed using 5 kg and 10 kg dumbbells separately. In addition,
the effects of consecutive sets of training (experiment II) on the subject’s skin-surface
the effects of consecutive sets of training (experiment II) on the subject’s skin-surface tem-
temperature were investigated for the biceps. The subject performed three sets of eight
perature were investigated for the biceps. The subject performed three sets of eight repe-
repetitions (i.e., 36 s duration including passive (no movement with weight) training for
titions (i.e., 36 s duration including passive (no movement with weight) training for 4 s).
4 s). Each set was followed by 92 s of recovery. To fully stimulate the target muscle, a 10 kg
Each set was followed by 92 s of recovery. To fully stimulate the target muscle, a 10 kg
dumbbell was used. Thermal video of the entire session was recorded. Lastly, muscle
dumbbell was used. Thermal video of the entire session was recorded. Lastly, muscle ac-
activation from different target muscles (experiment III) was investigated. To observe
tivation from different target muscles (experiment III) was investigated. To observe the
the effects of the order of training on different target muscles, three sessions with three
effects of the order of training on different target muscles, three sessions with three differ-
different training orders, corresponding to activation of the three different target muscles,
ent training orders, corresponding to activation of the three different target muscles, were
were employed. The selected training orders were order #1: arm curl–lateral raise–kickback;
Sensors 2021, 21, 4505 4 of 12
Sensors 2021, 21, 4505 4 of 12
employed. The selected training orders were order #1: arm curl–lateral raise–kickback;
order #2: lateral raise–kickback–arm curl; and order #3: kickback–arm curl–lateral raise;
hence,#2:the
order corresponding
lateral muscle activations
raise–kickback–arm were#3:
curl; and order biceps–deltoid–triceps;
kickback–arm curl–lateral deltoid–tri-
raise;
ceps–biceps; and triceps–biceps–deltoid. Each subject performed three sets
hence, the corresponding muscle activations were biceps–deltoid–triceps; deltoid–triceps– of 12 repeti-
tions (i.e.,
biceps; and48triceps–biceps–deltoid.
s duration), followed byEach92 s subject
of recovery for eachthree
performed session.
setsEach
of 12subject chose
repetitions
either
(i.e., 48 the 5 kg or 10
s duration), kg dumbbell
followed by 92 saccording
of recoveryto for
their
eachability to withstand
session. the chose
Each subject full training
either
session.
the 5 kg orEach training
10 kg session
dumbbell was repeated
according to theirfor bothto
ability the left and right
withstand arms,
the full and asession.
training total 70
sessions
Each weresession
training selected.
was repeated for both the left and right arms, and a total 70 sessions
were selected.
2.3. Data Processing
2.3. Data Processing
All images were aligned so that the biceps were located on the left sides of the images
All images
(Figure 2a). Then,werethealigned
brachium so that
areathe
wasbiceps were located
manually croppedon the left2b).
(Figure sides of the
After imagesa
applying
(Figure 2a). Then, the brachium area was manually cropped (Figure
moving average filter, Otsu’s method [20] for global thresholding was applied to segment2b). After applying a
moving average filter, Otsu’s method [20] for global thresholding was
the brachium areas (Figure 2c). Further segmentation was manually performed, if neces- applied to segment
the brachium
sary. areasof(Figure
The region interest 2c). Further
(ROI) wassegmentation was manually
then evenly divided into 20performed,
horizontalifand necessary.
60 ver-
The
ticalregion of (Figure
sections interest2d).(ROI) was map
A heat thenwasevenly dividedbyinto
generated 20 horizontal
calculating the meanandtemperature
60 vertical
sections
from each (Figure
divided2d).region
A heatinmap the was
ROIgenerated
(Figure 2e)byandcalculating
displayed theusing
meana temperature
color map (Figurefrom
each
2f). divided region in the ROI (Figure 2e) and displayed using a color map (Figure 2f).
To
Todetect
detect muscle
muscle activation,
activation, the the thermal
thermalimages
imagesatatthe thebeginning
beginningand and
endend of of
eacheachset
set were chosen from each training set. Thus, a total of six images
were chosen from each training set. Thus, a total of six images were used from the three were used from the
three
sets insets in a single
a single session.session.
Using Using the thermal
the thermal images,images, heatwere
heat maps maps were generated
generated and denoted and
denoted as (S1),
as (S1), (S2), and(S2), and (E1),
(S3) and (S3) and
(E2),(E1),
and (E2), andthe
(E3) for (E3) for the beginning
beginning and end ofand eachendset,ofrespec-
each
set, respectively. Thus, a total of 420 heat maps were generated
tively. Thus, a total of 420 heat maps were generated out of the 70 sessions. out of the 70 sessions.
Figure
Figure2. Visualization of
2. Visualization ofthe
theprocess
processofofanalysis
analysisofof a single
a single image:
image: (a) original
(a) original thermal
thermal image,
image, (b)
(b) cropped image around the brachium, (c) image after segmentation, (d) 20 × 60 divided
cropped image around the brachium, (c) image after segmentation, (d) 20 × 60 divided regions, (e) regions,
heat
(e) map
heat mapwith three
with selected
three regions
selected for for
regions the the
target muscles,
target and and
muscles, (f) colorized heat heat
(f) colorized map map
of theoftar-
the
get muscles.
target muscles.
To
Toanalyze
analyzethe
thetemperature
temperaturechangeschangesfromfromthe
thetarget
targetmuscle
muscleregions,
regions,each
eachheat
heatmap
map
was
was divided
divided into three
three regions,
regions,as asshown
shownininFigure
Figure
2e.2e. Based
Based on positions
on the the positions ofmus-
of the the
muscles, it was
cles, it was assumed
assumed thatthat
thethe upper-halfregion
upper-half regionwas
wasthe
the deltoid,
deltoid, the lower-left
lower-leftquadrant
quadrant
was
wasthethebiceps,
biceps,and
andthe
thelower-right
lower-rightquadrant
quadrantwas wasthe
thetriceps.
triceps. The
Themean
meanvalue
valueofofeach
each
region
regionrepresents
representsthe
thetemperature
temperatureof ofthe
thecorresponding
correspondingmuscle.
muscle.Statistical
Statisticalanalyses
analyseswere
were
performed
performedusingusingthe
theKruskal–Wallis
Kruskal–Wallistest testto
toexamine
examinethe
thetemperature
temperaturedifference
differencefor
foreach
each
target muscle between the three training
target muscle between the three training orders. orders.Sensors 2021, 21, 4505 5 of 12
2.4. Classification of Target Muscle Activation
The ResNet-18 [21] network was used to classify target muscle activation using the
difference heat maps (i.e., temperature differences) from the proposed method. To train the
network, a total of 134 difference heat maps were generated from the differences between
the heat map pairs, which are (S1-E3) and (S1-S3). The target classification was divided
into four classes, namely biceps, triceps, deltoid, and vague. The “vague” class implies
that no valid muscle activation was observed. The test set was 10% of randomly selected
data, thus resulting in 121 training data and 13 test data. The summary of the dataset is
shown in Table 2. For augmentation of the training data, the heat maps were processed
by rotation from −1◦ to 1◦ in steps of 1◦ , Gaussian noise addition with a mean of 0.07,
scaling by 0.9 and 1.1 times, and resizing to a resolution of 244 × 244 pixels, resulting in
2178 training images. The network model was initialized using the pretrained weights with
the ImageNet dataset [22] for ResNet-18 provided in MATLAB (Mathworks Inc., Natick,
MA, USA). Then, the model was trained by finetuning with a learning rate of 1 × 10−4
and batch size of 16 for 50 epochs. The Adam optimizer and cross-entropy loss were used
for training.
Table 2. Summary of the dataset used for training and test for classification.
Dataset Biceps Triceps Deltoid Vague Total
Train 33 19 31 38 121
Test 4 3 2 4 13
3. Results
3.1. Effects of Weight, Duration, and Muscle Activation for Training
Figure 3 shows the changes in skin surface temperatures from the biceps with vary-
ing weights (experiment I (Figure 3a) and in consecutive sets of training (experiment II
(Figure 3b). For the measurement with a 5 kg dumbbell (dotted line in Figure 3a), the initial
biceps skin temperature was measured as 36.07 ◦ C. After one minute of training, the biceps
skin temperature was still 36.07 ◦ C. After five minutes of recovery, the skin temperature
reached a maximum of 36.34 ◦ C. While the average rate of increase of the skin surface
temperature on the biceps during training was negligible, the skin surface temperature
during recovery was 0.05 ◦ C/min. With a 10 kg dumbbell (solid line in Figure 3a), the skin
surface temperature was 35.98 ◦ C initially and 36.11 ◦ C at the end of training. After five
minutes of recovery, the temperature reached up to 37.24 ◦ C. The average elevation rate
was 0.13 ◦ C/min during exercise and 0.47 ◦ C/min during 3 min of recovery. It was there-
fore observed that the increase in the skin surface temperature occurred during recovery.
Moreover, the skin temperature reached a plateau and decreased after enough recovery
time (3 min in this study). The difference in the average elevation rate during 3 min of
recovery with the 5 kg and 10 kg weights was 0.46 ◦ C/min. Figure 3b shows skin surface
temperature changes during three consecutive sets of training with recovery times between
each set. The temperature gradually rose during rest, which matches with the results
from a single set of training in Figure 3a. Further, it was observed that the skin surface
heated up as more sets of training were performed. The overall average elevation rate
was 0.52 ◦ C/min. In each recovery period, the average elevation rates of the skin surface
temperatures were 0.51, 0.42, and 0.59 ◦ C/min, as shown in Figure 3b. The mean skin
surface temperatures during exercise were 32.90, 34.00, and 35.07 ◦ C for each training set.
The mean skin surface temperatures during recovery were 33.42, 34.65, and 35.64 ◦ C after
each training set.Sensors 2021, 21, 4505 6 of 12
Sensors 2021, 21, 4505 6 of 12
Sensors 2021, 21, 4505 6 of 12
(a) Exp. I. Weight Variation (b) Exp. II. Consecutive Set
38 (a) Exp. I. Weight Variation 36.5 (b) Exp. II. Consecutive Set
38 10kg 36.5 0.59℃/min
10kg
5kg 36 0.59℃/min
5kg 36
37.5 35.5
37.5 35.5 0.42℃/min
0.47℃/min 35 0.42℃/min
0.47℃/min 35
37
37 34.5
34.5
34 0.51℃/min
36.5 34 0.51℃/min
36.5 0.05℃/min
0.05℃/min 33.5
33.5
33
36 33
36 A R 32.5 A R A R A R
A R 32.5 A R A R A R
0 60 120 180 240 300 360 0 36 128 164 256 292 360
0 60 120 180 240 300 360 0 36 128 164 256 292 360
Time
Time (s)
(s) Time(s)
Time (s)
Figure
Figure3.3.Change
Changein inskin
skinsurface temperature
surfacetemperature from
temperaturefrom biceps
bicepsinin
frombiceps in (a) aa session
session of aa single
single set with
with 5kg kgand
and1010kg
kgdumbbells
dumbbells
Change in skin surface (a)(a)
a session of of
a single set set
with 5 kg5 and 10 kg dumbbells and
and
and in (b) a session of three consecutive sets with a 10 kg dumbbell. (“A” and “R” denote active training and recovery
in (b)ina (b) a session
session of three
of three consecutive
consecutive setsawith
sets with 10 kga dumbbell.
10 kg dumbbell.
(“A” and(“A”
“R”and “R” active
denote denotetraining
active training and recovery
and recovery periods,
periods,
periods,respectively.).
respectively.).
respectively.).
Figure
Figure44 shows
Figure shows examples
examples of the heat
of the heat maps
maps for
for the
the three
threedifferent
differentmuscle
muscleactivations
activations
(i.e., (a)
(i.e., (a) biceps,
(a)biceps, (b)
biceps,(b) triceps,
(b)triceps, and
triceps,and
and(c) deltoid)
(c)(c)
deltoid) from
from
deltoid) experiment
experiment
from experiment III. Column
III. Column
III. Column (i) shows
(i) shows theheat
the
(i) shows heat
the
maps
maps of
heat mapsof the beginning
theofbeginning
the beginningof
of each
of training
each training set (S1-S3),
set
each training (S1-S3), column
column
set (S1–S3), (ii)shows
(ii)
column shows theheat
the
(ii) shows heat maps
themaps
heat ofofthe
mapsthe
end
end of
of each
each training
training set
set (E1-E3)
(E1-E3) from
from a
a single
single session,
session, and
and column
column (iii)
(iii)
of the end of each training set (E1–E3) from a single session, and column (iii) shows the shows
shows the
the difference
difference
heat
heat maps
mapsfrom
difference the
the (S1-E2),
frommaps
heat (S1-E2),
from the(S1-S3),
(S1-S3),
(S1–E2), and
and (S1-E3)and
(S1-E3)
(S1–S3), pairs.
pairs. Thedifference
The
(S1–E3) difference
pairs. Theheat heatmaps
mapsare
difference areshown
heat shown
maps
in
in Figure
areFigure 4a–c
shown4a–c (iii).
(iii). 4a–c (iii).
in Figure
Figure
Figure4.4.
Figure 4.Heat
Heatmaps
Heat mapsfrom
maps frommuscle
from muscleactivation
muscle activationofof
activation of(a)
(a)biceps,
(a) biceps,
biceps, (b)
(b) triceps,
triceps,
(b) and
triceps, and
(c)(c)
and deltoid.
deltoid.
(c) Column
Column
deltoid. Column(i): (i):
(i):heat
heat maps
maps
heat ofofthe
of the
maps thebegin-
beginning
begin-
ning
ning of each
of each
of each training
training
training set (S1-S3),
set (S1-S3),
set (S1–S3), column column
column (ii): heat
(ii):maps
(ii): heat maps
heat maps of the
of the
of the end end
of end of
eachof each training
each training
training set (E1-E3)
set (E1-E3)
set (E1–E3) from a single
from session,
from a single session,
a single and
session, and
and
column
column
column (iii): difference
(iii): difference heat maps
heatfrom
maps from (S1-E3),
from (S1-E3), (S1-S2), and (S1-S3) pairs.
(iii): difference heat maps (S1–E3), (S1–S2),(S1-S2), and (S1-S3)
and (S1–S3) pairs. pairs.
Difference
Difference heat
Difference heat maps
heat maps were generated
maps were generated from
generated from various
from various heat
heatmap
variousheat mappairs,
map pairs,including
pairs, including(S1-E3),
including (S1–E3),
(S1-E3),
(S1-S3), (S1-E2), (S1-S2), and (S1-E1). Table 3 shows the numbers of pairs that producethe
(S1-S3),
(S1–S3), (S1-E2),
(S1–E2), (S1-S2),
(S1–S2),and
and (S1-E1).
(S1–E1).Table 3
Table shows
3 the
shows numbers
the numbersof pairs
of that
pairs produce
that producethe
highest
highest temperature
the highest
temperature differences
temperature
differences among
differences
among the
the various
among heat
the various
various map
heatheat pairs
mapmap from
pairs
pairs from each
from session.
eacheach The
session.
session. The
average difference
The average for S1-E3
difference of the entire
for S1-E3 collected data data
was 0.86 °C, ◦ C,the
and highest tem-
average difference for S1-E3 of theofentire
the entire collected
collected data was was
0.86 0.86
°C, and and the highest
the highest tem-
perature
temperaturedifference was
difference 0.97 °C. ◦ The higher the temperature difference, the clearer the
perature difference waswas0.970.97
°C. C. The
The higherthe
higher thetemperature
temperature difference,
difference, thethe clearer
clearerthe
theSensors 2021, 21, 4505 7 of 12
Sensors 2021, 21, 4505 7 of 12
differentiation of the selected muscles. It was observed that 85% of the highest tempera-
ture difference was acquired from the heat map pairs of the first and third sets of each
session.
differentiation of the selected muscles. It was observed that 85% of the highest temperature
difference was acquired from the heat map pairs of the first and third sets of each session.
Table 3. Counts of different periods showing the highest temperature differences from heat map
pairs.
Table 3. Counts of different periods showing the highest temperature differences from heat map pairs.
Heat Map Pairs Count Percentage
Heat Map Pairs Count Percentage
(S1-E3) 36 51%
(S1-E3)
(S1-S3) 36
24 51%
34%
(S1-S3) 24 34%
(S1-E2) 4 5%
(S1-E2) 4 5%
(S1-S2)
(S1-S2) 22 3%
3%
(S1-E1)
(S1-E1) 44 5%
5%
Figure 55shows
Figure showsvarious
variousheat
heatmapmapexamples
examplesgenerated
generatedfrom
fromthetheexperiments
experimentsin inthis
this
study. Figure 5a is the control case where the subjects performed training with
study. Figure 5a is the control case where the subjects performed training with no weights no weights
(i.e., passive
(i.e., passive training);
training); nono local
local heating
heating was
wasobserved
observedininthis
thiscase.
case. Figure
Figure 5b–d
5b–d clearly
clearly
demonstrates local heating of the target muscles with proper training
demonstrates local heating of the target muscles with proper training for each targetfor each target mus-
cle activation.
muscle Figure
activation. 5f shows
Figure an example
5f shows an exampleof unexpected muscle
of unexpected activation
muscle from from
activation over-
weighted training, where the subject swung the elbow excessively while
over-weighted training, where the subject swung the elbow excessively while targeting targeting the bi-
ceps. Additionally, in the case of Figure 5e, the subject performed an inaccurate
the biceps. Additionally, in the case of Figure 5e, the subject performed an inaccurate kick-back
session. The
kick-back resulting
session. images show
The resulting imagesthat the temperature
show increased
that the temperature throughout
increased the bra-
throughout
chium, especially at the posterior deltoid (i.e., upper-right area).
the brachium, especially at the posterior deltoid (i.e., upper-right area).
Figure5.5.Heat
Figure Heatmap
mapexamples
examplesfrom
from(a)
(a)control
controlfrom
frompassive
passivetraining, (b–d)
training, proper
(b–d) training
proper forfor
training biceps,
bi-
triceps, and deltoids, respectively, (e) over-weighted training, and (f) incorrect posture of training.
ceps, triceps, and deltoids, respectively, (e) over-weighted training, and (f) incorrect posture of
training.
3.2. Statistical Analysis
Figure 6 shows
3.2. Statistical Analysisthe overall average of temperature differences between the regions
of target and nontarget muscles during the exercise session. Overall, the heat map pair
Figure 6 shows the overall average of temperature differences between the regions
(S1)-(E3) showed the most significant temperature difference, as analyzed from Table 2.
of target and nontarget muscles during the exercise session. Overall, the heat map pair
The results in Figure 6 match well with those in Figure 3, where the surface temperature
(S1)-(E3) showed the most significant temperature difference, as analyzed from Table 2.
of the target muscle gradually increased during a session with three training and rest
The results in Figure 6 match well with those in Figure 3, where the surface temperature
periods. In the case of the biceps (Figure 6a), the temperature difference was higher when
of the target muscle gradually increased during a session with three training and rest pe-
compared with the deltoid (dotted line) than with the triceps (solid line). For the triceps
riods.
case In the6b),
(Figure case ofoverall
the the biceps (Figure 6a),
temperature the temperature
difference was lower difference
compared to wasthehigher when
other cases.
compared with the deltoid (dotted line) than with the triceps (solid line). For the triceps
For the deltoid case (Figure 6c), the trend with the triceps (solid line) was similar to that of
casebiceps
the (Figure 6b), line).
(dotted the overall temperature difference was lower compared to the other
cases. For the deltoid case (Figure 6c), the trend with the triceps (solid line) was similar to
that of the biceps (dotted line).Sensors
Sensors 2021,
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21,4505
4505 888of
of 12
of 12
(a)
(a)Biceps
Biceps (b)
(b)Triceps
Triceps (c)
(c)Deltoid
Deltoid
Bi
Bi--Tri
Tri Tri
Tri--Bi
Bi Delt
Delt--Bi
Bi
0.8
0.8 Bi
Bi--Delt
Delt 0.8
0.8 Tri
Tri--Delt
Delt 0.8
0.8 Delt
Delt--Tri
Tri
0.6
0.6 0.6
0.6 0.6
0.6
0.4
0.4 0.4
0.4 0.4
0.4
0.2
0.2 0.2
0.2 0.2
0.2
00 00 00
Figure
Figure6.
Figure 6.The
6. Theaverage
The averagetemperature
average temperaturedifference
temperature differencebetween
difference betweentarget
between targetmuscles
target musclesand
muscles andnon-target
and non-targetmuscles
non-target muscleswhen
muscles whentargeting
when targeting(a)
targeting (a)biceps,
(a) biceps,
biceps,
(b)
(b)triceps,
triceps,and
and(c)
(c)deltoid.
deltoid.Error
Errorbars
barsdenote
denotestandard
standarddeviation.
deviation.
(b) triceps, and (c) deltoid. Error bars denote standard deviation.
Figure
Figure 77 shows
shows the
the median
median and and variance
variance values
values ofof the
the temperature
temperature differences
differences forfor
each target
each target muscle
targetmuscle from
musclefrom thethe
from the three
threethree different
different
different training
training
training orders.
orders.orders. The
The Kruskal–Wallis
Kruskal–Wallis
The Kruskal–Wallis test
test showedtest
showed
showed that
that no
no significant
that no significant significant
differencesdifferences
(p-value =(p-value
differences (p-value == 0.57,
0.57, 0.62, 0.62,
0.57,0.40
and forand
0.62, and 0.40 for
for biceps,
0.40triceps,
biceps, and triceps,
biceps, triceps,
deltoid,
and
and deltoid,
deltoid, respectively)
respectively) respectively) were
were found
were found among found
the among
among
three the three
three different
the training
different orders. training
different training orders.
orders.
The results The
The re-
demonstrate re-
sults
sults demonstrate
demonstrate
that the that
thatthe
order of muscle order
orderof
theactivationofmuscle
muscle activation
activationhad
had insignificant had insignificant
insignificant
effects effects
effectson
on the temperature onthe
thetem-
tem-
changes
perature
perature changes
changesduring
during exercise. duringexercise.
exercise.
Temperature Difference
Temperature [℃]
Difference [℃]
Figure 7. Temperature differences for target muscles (a) biceps, (b) triceps, and (c) deltoid from three different training
Figure 7.7. Temperature
Figure (order
Temperature differences
differences for
for target
target muscles (a)
(a) biceps,
biceps, (b)
(b) triceps, and (c)
(c) deltoid from
from three
three different
different training
orders #1: biceps–deltoid–triceps, ordermuscles triceps,
#2: deltoid–triceps–biceps, andandorder deltoid
#3: triceps–biceps–deltoid). training
orders
orders(order
(order#1:
#1:biceps–deltoid–triceps,
biceps–deltoid–triceps,order
order#2:
#2:deltoid–triceps–biceps,
deltoid–triceps–biceps,and andorder
order#3:
#3:triceps–biceps–deltoid).
triceps–biceps–deltoid).
3.3. ClassificationofofMuscle
3.3. Muscle Activation
3.3.Classification
Classification of MuscleActivation
Activation
The
The training accuracy and loss function of the network training are shown ininFigure 8.
Thetraining
trainingaccuracy
accuracyandandloss
lossfunction
functionof ofthe
thenetwork
networktraining
trainingare
areshown
shown inFigure
Figure
After 50 epochs, 100% training accuracy and 92.3% test accuracy were achieved. Figure 9
8.
8.After
After5050epochs,
epochs,100%
100%training
trainingaccuracy
accuracyandand92.3%
92.3%test
testaccuracy
accuracywerewereachieved.
achieved.Figure
Figure
shows the prediction probabilities for the classification of difference heat maps for various
99shows
showsthe theprediction
predictionprobabilities
probabilitiesforforthe
theclassification
classificationofofdifference
differenceheat
heatmaps
mapsforforvari-
vari-
muscle activations. Each target muscle activation showed the highest prediction probability
ous
ousmuscle
muscleactivations.
activations.Each
Eachtarget
targetmuscle
muscleactivation
activationshowed
showedthe thehighest
highestprediction
predictionprob-
prob-
from the correct class (biceps: 99.99% (Figure 9a), deltoid: 98.09% (Figure 9b), triceps:
ability
ability from
from the
the correct
correct class
class (biceps:
(biceps: 99.99%
99.99% (Figure
(Figure 9a),
9a), deltoid:
deltoid: 98.09%
98.09% (Figure
(Figure 9b),
9b), tri-
tri-
99.98% (Figure 9c), vague: 99.84% (Figure 9d). Although insufficient training and test
ceps:
ceps: 99.98%
99.98% (Figure
(Figure 9c),
9c), vague:
vague: 99.84%
99.84% (Figure
(Figure 9d).
9d). Although
Although insufficient
insufficient training
training and
and
datasets were utilized, the results clearly demonstrate that classification of muscle activation
test
test datasets
datasets were
were utilized,
utilized, the
the results
results clearly
clearly demonstrate
demonstrate that that classification
classification of
of muscle
muscle
can be easily achieved from the trained network using the difference heat maps.
activation
activationcan canbe
beeasily
easilyachieved
achievedfromfromthe
thetrained
trainednetwork
networkusing
usingthe
thedifference
differenceheat
heatmaps.
maps.Sensors
Sensors 2021,21,
Sensors2021,
2021, 21,4505
21, 4505
4505 999of
of 12
of12
12
Figure8.8.Summary
Figure Summaryofofnetwork
networktraining:
training:(a)
(a)training
trainingaccuracy
accuracyand
and(b)
(b)loss
lossfunction.
function.
Figure 9. Classification of muscle activation. (a–d): test heat maps of three different muscle activations
Figure9.9.Classification
Figure Classificationofofmuscle
muscleactivation.
activation.(a–d):
(a–d):test
testheat
heatmaps
mapsofofthree
threedifferent
differentmuscle
muscleactiva-
activa-
and vague
tions case with
andvague
vague casepredicted probabilities
withpredicted
predicted from the
probabilities fromnetwork, respectively.
thenetwork,
network, respectively.
tions and case with probabilities from the respectively.
4. Discussion
4.4.Discussion
Discussion
This study demonstrated the correlation between isolated resistance training of three
separate Thismuscles
This studydemonstrated
study demonstrated
of the brachium thecorrelation
the correlation
and between
between
temperatures isolated
isolated
of resistancetraining
resistance
their corresponding training ofofthree
locations three
on
separate
separate muscles
muscles of
of the
the brachium
brachium and
and temperatures
temperatures ofof
the skin surface. Three muscles of the brachium (i.e., biceps, triceps, and deltoid) were their
their corresponding
corresponding locations
locations on
on
the
the skin
skin surface.
surface. Three
Three muscles
muscles of
of the
the brachium
brachium (i.e.,
(i.e.,
selected owing to their easy isolation for resistance training movements and the anatomy biceps,
biceps, triceps,
triceps, and
and deltoid)
deltoid) were
were
selected
selected
of owingdistinguishable
owing
the muscles tototheir
theireasy
easyisolation
isolation
underfor for resistance
resistance
thermal training
training
imaging. movements
Themovements
main findings andof
and the
the anatomy
anatomy
this study
of
of the
arethe muscles
as muscles distinguishable
follows: distinguishable
(1) weight variation under
underduring thermal
thermaltraining imaging.
imaging. The
The main
affects main findings
findings
the change in of of this study
thissurface
skin study
areas
are asfollows:
follows:
temperature; (1)
(1)
(2) weightvariation
weight
consecutive variation
training during
during trainingheating
training
sets increase affectsthe
affects thethe
of change
change
skin in inskin
skinsurface
surface; surface
and (3)tem- tem-
the
perature;
perature;
order (2)consecutive
(2)
of trainingconsecutive trainingsets
training
has an insignificant sets increase
increase
effect heatingof
heating
on isolating ofthe
theskin
muscle skin surface;and
surface;
activation. and(3) (3)thetheor-or-
derofof
der training
Ittraining
was hasan
has
observed aninsignificant
insignificant
that temperature effectdifferences
effect onisolating
on isolating muscle
inmuscle activation.
activation.
the thermal images were highest
mostly ItItwas
wasobserved
betweenobserved
the start that
thatand temperature
end of training
temperature differences
sessions
differences inin(Table
thethermal
the thermal images
2). Thisimages
does not were
were match highest
well
highest
mostly
with
mostly between
results
between from the
the startand
previous
start and endofoftraining
studies,
end training
which sessions(Table
considered
sessions (Table
training 2).This
2). Thisdoes
with does
highnot not matchwell
intensity
match well
and
withresults
duration
with results fromprevious
leading
from previous
to studies,
perspiration
studies, andwhich
which muscleconsidered training
fatiguetraining
considered [23–25].withwith highstudy,
In high
this intensity
intensity theandand
effectsdu-
du-
ration
from
ration leadingtotoperspiration
perspiration
leading perspiration
and muscle and andmuscle
fatiguemuscle fatigue
werefatigue
minimized [23–25].
[23–25].using Inthis
In thisstudy,
recoverystudy, time thebetween
the effectsfrom
effects from
the
training
perspiration
perspiration sets.and
Overall,
and muscle
muscle there werewere
fatigue
fatigue relatively
were minimized
minimized subtleusing
temperature
using recovery
recovery changes
timebetween
time duringthe
between training
the train-
train-
compared
ing sets. to that
Overall, during
there recovery
were time,
relatively as shown
subtle in Figures
temperature
ing sets. Overall, there were relatively subtle temperature changes during training com- 3 and
changes 4. This
during can be explained
training com-
by
paredthe process
paredtotothat thatduringof human
duringrecovery metabolism.
recoverytime, time,as asshown When muscles
shownininFiguresFigures33andproduce
and4.4.This energy
Thiscan canbe by movement,
beexplained
explainedby by
PCr
the hydrolysis
theprocess
process is initially
ofofhuman
human dominant
metabolism.
metabolism. (~10 muscles
When
When s), followed
muscles by glycolysis
produce
produce energyby
energy (~50
by s). Subsequently,
movement,
movement, PCrhy-
PCr hy-
oxidation
drolysisisiswill
drolysis dominate
initially
initially dominantenergy
dominant (~10 production
(~10 s),followed
s), followed [9].byAsglycolysis
by heat production
glycolysis (~50s).
(~50 s).occurs mainly during
Subsequently,
Subsequently, oxida-
oxida-
the
tionoxidation
tion willdominate
will process,
dominate exercise
energy
energy durations[9].
production
production longer
[9]. As than
Asheat 60 s are expected
heatproduction
production occurs
occurs tomainly
contribute
mainly to heat
during
during the
the
production.
oxidation In this
process, study,
exercise the training
durations time
longer for each
than 60 set
s
oxidation process, exercise durations longer than 60 s are expected to contribute to heat was
are up
expectedto 60 tos, so the
contribute oxidation
to heat
process
production.
production. wasIn not
In thisconsidered.
this study,the
study, As
thetraining it is time
training known
timefor forthat
each
each only 2%
wasof
setwas
set upheat
up toto60 produced
60 s,s,sosothe from
theoxidation
oxidation the
muscle
processwas
process is instantly
wasnot dissipated
notconsidered.
considered.As through
Asititisisknown the
knownthat skin while
thatonly
only2%the rest
2%ofofheat is accumulated
heatproduced
producedfrom in
fromthe the muscle
themuscle
muscleSensors 2021, 21, 4505 10 of 12
and slowly perfused through skin, inducing vasodilation near the activated muscle [10],
this heat transfer causes delays in temperature change on the skin surface. Thus, it is
expected that the temperature rise during the recovery time would be more noticeable than
that during training, as observed in this study.
Overall, the results in Figure 6 show that the biceps region exhibited the most signifi-
cant rise in temperature. This can be explained by the presence of major veins (i.e., cephalic
vein) in the biceps region. The heat accumulated during training was dissipated through
the veins. In contrast, in other regions with no major veins, heat dissipation was relatively
low (Figure 6b,c). Owing to the thickest skinfold with no major veins, the heat dissipation
from the triceps region was the lowest among the three muscles [26], which was also ob-
served in this study (Figure 6b). This is presumably because the deltoid muscles function as
synergist muscles during motion. Although the overall temperature increased, there were
several temperature decreases for the deltoid (Figure 6c). During the lateral raise motion to
activate the deltoid muscle, the subjects swung their arms away from the body until their
arms became parallel with the floor. This movement may cause cooling via respiration.
As shown in Figure 5a–f, accurate training postures result in localized heating of
the skin directly above the target muscles. Thus, accurate training postures for isolated
resistance training can be clearly visualized using thermal imaging. Utilizing the difference
heat maps generated in this study, a convolutional neural network (ResNet-18 in this study)
was trained and tested for classification of muscle activation. Although there were not
sufficient data to discuss the accuracy or performance of the network, the test results of
the network clearly demonstrate the feasibility of the proposed approach to differentiate
muscle activation. The proposed technique can be further utilized in physical therapy,
which should be performed with proper muscle activation.
In this study, the number of subjects was limited, and the number of variations was
small. There are various conditions of the subject, such as body mass composition, muscle
mass, training skill, color and thickness of skin, thickness of skinfold, and age, that may
affect the result of this study. Thus, further investigations by performing experiments
on various subject groups are necessary [24,27]. In order to achieve repeatability of the
measurements, it is necessary to minimize changes in distance and angle between the
camera and the subject. Thus, motion tracking of the target area could be implemented.
Although the proposed approach is a noninvasive and noncontact method, the surface of
the skin needs to be exposed during the measurement. As only two absolute loads (5 and
10 kg) were utilized, which can induce different relative intensities of work depending
on the subject’s muscle strength, it is also necessary to consider the absolute intensity of
work correlated to muscle activation. Further investigation on validating the accuracy and
efficacy of the isolated muscle training sessions with the proposed approach will be our
future work.
5. Conclusions
The activation of three different brachium muscles during resistance training was
detected using infrared thermal imaging and analyzed using heat maps. The proposed
approach in this study demonstrated that the differences in heat maps could be used to
effectively differentiate the target muscles. Further investigations are necessary to validate
this approach on subject groups with various conditions and to expand it to other target
muscles. Future studies will also seek to improve the accuracy and efficacy of the proposed
approach for muscle isolation.
Author Contributions: Conceptualization, H.J., J.S., K.S., and S.P.; methodology, H.J., J.S., and K.S.;
software, H.J., J.S., K.S., and D.K.; validation, S.P.; resources, S.P. and D.K.; writing—original draft
preparation, H.J., J.S., and K.S.; writing—review and editing, S.P.; supervision, S.P. All authors have
read and agreed to the published version of the manuscript.Sensors 2021, 21, 4505 11 of 12
Funding: This research was supported by the Basic Science Research Program through the National
Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT under grant number
NRF-2020R1A2C1011889.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are available upon request from the
corresponding author.
Conflicts of Interest: The authors declare no conflict of interest.
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