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entropy
Article
Muscle Fatigue Analysis of the Deltoid during Three
Head-Related Static Isometric Contraction Tasks
Wenxiang Cui, Xiang Chen *, Shuai Cao and Xu Zhang
Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230026,
China; [email protected] (W.C.); [email protected] (S.C.); [email protected] (X.Z.)
* Correspondence: [email protected]; Tel.: +86-551-6360-1175
Academic Editors: Danilo P. Mandic, Andrzej Cichocki and Chung-Kang Peng
Received: 27 March 2017; Accepted: 9 May 2017; Published: 11 May 2017
Abstract: This study aimed to investigate the fatiguing characteristics of muscle-tendon units (MTUs)
within skeletal muscles during static isometric contraction tasks. The deltoid was selected as the
target muscle and three head-related static isometric contraction tasks were designed to activate
three heads of the deltoid in different modes. Nine male subjects participated in this study. Surface
electromyography (SEMG) signals were collected synchronously from the three heads of the deltoid.
The performances of five SEMG parameters, including root mean square (RMS), mean power
frequency (MPF), the first coefficient of autoregressive model (ARC1), sample entropy (SE) and
Higuchi’s fractal dimension (HFD), in quantification of fatigue, were evaluated in terms of sensitivity
to variability ratio (SVR) and consistency firstly. Then, the HFD parameter was selected as the fatigue
index for further muscle fatigue analysis. The experimental results demonstrated that the three
deltoid heads presented different activation modes during three head-related fatiguing contractions.
The fatiguing characteristics of the three heads were found to be task-dependent, and the heads kept
in a relatively high activation level were more prone to fatigue. In addition, the differences in fatiguing
rate between heads increased with the increase in load. The findings of this study can be helpful in
better understanding the underlying neuromuscular control strategies of the central nervous system
(CNS). Based on the results of this study, the CNS was thought to control the contraction of the
deltoid by taking the three heads as functional units, but a certain synergy among heads might also
exist to accomplish a contraction task.
Keywords: muscle fatigue; deltoid; SEMG; MTUs; static isometric contraction
1. Introduction
Muscle fatigue can be defined as a lack of ability to temporarily maintain a required or expected
force [1–3]. The mechanism of muscle fatigue generation is very complex as it relates to various
factors, such as the metabolites, muscle structures and the nervous system. Because muscle fatigue can
effectively reflect functional muscle changes, it has been widely explored in the fields of rehabilitation
medicine, kinesiology and biomechanics [4,5]. In the fields of rehabilitation medicine, muscle fatigue
can reflect changes of muscle attributes and functions caused by neuromuscular system diseases so
that it can be used as a disease diagnosis tool. For instance, Eken et al. found significant differences in
muscle fatigue among children with cerebral palsy, developing children and young healthy adults [6].
Villafañe et al. applied muscle fatigue to examine the validity and applicability of rehabilitation
exercises in patients with chronic non-specific low back pain [7]. In the fields of kinesiology or
biomechanics, muscle fatigue is regarded as a dominant factor affecting kinematic and mechanical
parameters [4,8]. For example, Becker et al. studied the changes of kinematic and kinetic parameters
caused by core muscle fatigue in header [9]. Corben et al. studied the muscle fatigue during fast-pitch
softball performance [10].
Entropy 2017, 19, 221; doi:10.3390/e19050221
www.mdpi.com/journal/entropy
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In the human anatomy, some skeletal muscles are composed of smaller structures called
muscle-tendon units (MTUs) or heads. For example, the biceps brachii, the deltoid and the extensor
digitorum communis have two, three and four MTUs, respectively [11–13]. Some researchers believe
that the central nervous system (CNS) controls motion tasks with different strategies for different
MTUs, rather than taking the muscle as a single unit [13,14]. Early studies about muscle fatigue just
considered muscles as single units and overlooked the dissimilarities among different regions of some
skeletal muscles. In recent studies, researchers started to notice that there existed region-specific
manifestations of fatigue within some skeletal muscles, which partly proved that muscles did not work
as a single unit. In 2011, Gallina et al. used a matrix of electrodes to measure surface electromyography
(SEMG) signals from the medial gastrocnemius muscle under the intermittent plantar flection fatigue
protocol. They selected root mean square (RMS) and mean power frequency (MPF) as indexes for the
quantification of muscle fatigue, and found that SEMG manifestation of fatigue was distributed locally
within the human medial gastrocnemius muscle [15]. Similarly, Watanabe et al. conducted a study to
detect region-specific manifestations of muscle fatigue within the rectus femoris muscle based on a
multi-channel SEMG grid in 2013. In their study, two motion tasks including isometric knee extension
and hip flexion were designed to induce muscle fatigue of the rectus femoris muscle, RMS and MPF
were chosen as indexes, and the manifestation of fatigue within the rectus femoris muscle was found
to be region-specific [16]. Watanabe et al. believed that the differences between the motor nerve
branch components and originations within the rectus femoris muscle, were the factors resulting in the
region-specific manifestation of fatiguing characteristics. Furthermore, in a study which used SEMG
to investigate the fatiguing characteristics of the triceps brachii, the three heads of the triceps brachii
also showed different fatiguing characteristics during a controlled forceful hand grip task with full
elbow extension [17]. To some extent, the research results related to the region-specific manifestation
of fatigue within some skeletal muscles all verified the assumption that the CNS controls a motion
task with different strategies for different MTUs. However, all these works just found region-specific
manifestation of fatigue within muscles during a targeted motion task whereas they did not explore
the fatiguing differences among the MTUs.
SEMG is the interferential tissue-filtered summation of all motor unit action potentials (MUAP,
the combination of muscle fiber action potentials generated by one motor unit). Therefore, SEMG
could be used to detect muscle force, neuromuscular diseases and muscle fatigue [18]. Specifically,
SEMG could reflect the changes of recruitment strategies of MUs and the biochemical environment in
muscle fibers due to muscle fatigue [3,19]. Various SEMG parameters such as RMS, MPF and the first
coefficients of autoregressive models (ARC1) were proved effective in muscle fatigue analyses [2,20].
However, the effectiveness in quantification of muscle fatigue is different in these SEMG parameters,
since various factors such as muscle force, would affect SEMG signals [21]. For instance, Kim et al.
investigated the performance of ARC1 in depicting trunk muscle fatigue under 15%, 30%, 45%, 60%
and 75% of the maximal voluntary contraction (MVC) force levels. Other four parameters including
RMS, zero crossing rate (ZCR), MPF and MDF were compared with ARC1 in terms of sensitivity
and reliability. Their research results revealed that the activation levels of muscle had impact on
the performance of SEMG features in depicting muscle fatigue. ARC1 was found to be the most
sensitive parameter at 15–45% MVC and ZCR was found to be the most sensitive parameter at 60–75%
MVC [20]. Because different MTUs may be at different activation levels in a given task, the robustness
and effectiveness of SEMG parameters for individual MTU fatigue analysis should be ensured in
relevant studies.
In order to further reveal the neuromuscular control mechanism of the CNS, this study conducted
an investigation on the fatiguing characteristics of the three deltoid heads during three head-related
static isometric contraction tasks based on SEMG technique. Specifically, the performance in depicting
fatigue of five SEMG parameters were evaluated based on the sensitivity to variability ratio (SVR)
and consistency firstly. Then, the best one was selected for further MTU-related fatigue analysis. This
study put efforts into extending the muscle fatigue analysis from the whole muscle to the MTU level,
Entropy 2017, 19, 221
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3 of 14
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related fatigue analysis. This study put efforts into extending the muscle fatigue analysis from the
whole muscle to the MTU level, and the research achievements are meaningful for the training
and the research achievements are meaningful for the training guidance, the rehabilitation action
guidance, the rehabilitation action design, and the establishment of biomechanical model in the fields
design, and the establishment of biomechanical model in the fields of fitness, rehabilitation medicine
of fitness, rehabilitation medicine and biomechanics.
and biomechanics.
2. Materials
and Methods
Methods
2.
Materials and
2.1. Subjects
and wrist
wrist pathology
pathology or complaints
complaints
Nine male subjects without history of shoulder, elbow, and
volunteered to participate
in
this
study
(age:
23.2
±
3.1
years
old,
stature:
175.7
±
3.0
cm,
mass:
±
participate in this study (age: 23.2 ± 3.1 years old, stature: 175.7 ± 3.0 cm, 65.2
mass:
4.0 kg).
theAllparticipants
werewere
informed
of the
experiment
procedure
65.2
± 4.0All
kg).
the participants
informed
of the
experiment
procedureand
andsigned
signedan
an informed
consent approved by the Ethics Review Committee of Anhui Medical University (No. PJ 2014-08-04).
2.2. Head-Related Static Isometric Contraction Tasks
Tasks
The deltoid, which forms the rounded contour of the shoulder,
shoulder, was selected as
as the
the target
target muscle.
muscle.
The
deltoid
is
made
up
of
three
distinct
sets
of
muscle
fibers
forming
three
MTUs
known
as
the
anterior,
The deltoid is made up of three distinct sets of muscle fibers forming three MTUs known as the
lateral,
posterior
head [11].
The[11].
unique
of the deltoid
ensuresensures
its capability
to move
anterior,and
lateral,
and posterior
head
The anatomy
unique anatomy
of the deltoid
its capability
to
the
arm
different
directions.
The anterior
head,head,
lateral
headhead
andand
posterior
head
areare
in charge
of
move
theinarm
in different
directions.
The anterior
lateral
posterior
head
in charge
shoulder
flexion
(raising
thethe
armarm
forward),
shoulder
abduction
(lifting
the arm
out to
and
of shoulder
flexion
(raising
forward),
shoulder
abduction
(lifting
the arm
outthe
toside),
the side),
shoulder
extension
(moving
the arm
respectively
[11,22].[11,22].
In thisIn
study,
three head-related
and shoulder
extension
(moving
thebackward),
arm backward),
respectively
this study,
three headstatic
isometric
contraction
tasks were tasks
designed
to activate
deltoid
In
related
static isometric
contraction
were
designedthetothree
activate
theheads
threepreferentially.
deltoid heads
order
to standardize
the to
experimental
a guiding device
with
a height device
adjustable
horizontal
preferentially.
In order
standardizeprotocol,
the experimental
protocol,
a guiding
with
a height
pole
was used
as an auxiliary
(Figure
1).
adjustable
horizontal
pole wasdevice
used as
an auxiliary
device (Figure 1).
Figure 1.
1. Three
Three head-related
head-related static isometric contraction tasks: (a)
(a) Task
Task 1; (b) Task
Figure
Task 2;
2; (c)
(c) Task
Task 3.
3.
Task 1:
1: This
This task
task was
was designed
designed to
to preferentially
preferentially activate
activate the
the anterior
anterior deltoid.
Task
deltoid. As
As shown
shown in
in Figure
Figure 1a,
1a,
subjects
firstly
sat
erect
with
the
right
arm
straight
forward
and
holding
a
dumbbell
in
the
hand,
and
subjects firstly sat erect with the right arm straight forward and holding a dumbbell in the hand, and
then kept
kept their
their arm
arm and
and shoulder
shoulder at
at the
the same
same height
height as
as guided
guided by
by aa horizontal
horizontal pole.
pole. Subjects
Subjects were
were
then
asked
to
maintain
this
posture
until
they
failed
to
maintain
it,
which
was
considered
to
be
in
the
state
asked to maintain this posture until they failed to maintain it, which was considered to be in the state
of
exhaustion.
of exhaustion.
Task 2:
2: This
This task
task was
was designed
designed to
to preferentially
preferentially activate
activate the
the lateral
lateral deltoid.
Task
deltoid. As
As shown
shown in
in Figure
Figure 1b,
1b,
subjects
sat
erect
with
the
right
arm
straight
outward
and
the
holding
a
dumbbell,
and
kept
their arm
arm
subjects sat erect with the right arm straight outward and the holding a dumbbell, and kept their
and shoulder
shoulder at
at the
the same
same height
height as
as guided
guided by
by aa horizontal
horizontal pole
pole until
until the
the state
state of
of exhaustion.
exhaustion.
and
Task 3:
was
designed
to preferentially
activate
the posterior
deltoid.deltoid.
As shown
inshown
Figure 1c,
Task
3: This
Thistask
task
was
designed
to preferentially
activate
the posterior
As
in
the
difference
between
Task
3
and
Task
2
was
that
in
Task
2
subjects
were
asked
to
sit
erect,
in
Figure 1c, the difference between Task 3 and Task 2 was that in Task 2 subjects were asked to sitbut
erect,
Taskin3Task
subjects
were asked
to sit with
chest
touching
their their
knee knee
first, first,
and then
keepkeep
theirtheir
arm arm
and
but
3 subjects
were asked
to sit the
with
the chest
touching
and then
back
at
the
same
height
as
guided
by
the
horizontal
pole
until
they
could
not
maintain
it.
and back at the same height as guided by the horizontal pole until they could not maintain it.
In most
most muscle
muscle fatigue
fatigue studies,
studies, two
two methods
methods are
are usually
usually used
used to
to keep
keep the
the muscles
muscles at
at aa different
different
In
activation level
level during
during one
one specific
specific task.
task. One
One method
method suggested
suggested subjects
subjects maintain
maintain contraction
contraction with
with aa
activation
certain
percentage
of
the
MVC.
The
other
method
allows
using
different
loads
to
activate
the
target
certain percentage of the MVC. The other method allows using different loads to activate the target
muscle
[23,24].
In
this
study,
the
object
of
fatigue
analysis
was
the
MUTs
instead
of
the
whole
muscle.
muscle [23,24]. In this study, the object of fatigue analysis was the MUTs instead of the whole muscle.
However, the measurement of MVC for MTUs was almost impossible since it was difficult to activate
Entropy 2017, 19, 221
4 of 15
Entropy 2017, 19, 221
4 of 14
However, the measurement of MVC for MTUs was almost impossible since it was difficult to activate
one specific
specificMTU
MTU
independently.
Hence,
step-increasing
weretoused
to activate
at
one
independently.
Hence,
step-increasing
loadsloads
were used
activate
MTUs atMTUs
different
different
activation
levels.
To
determine
the
weights
of
the
loads,
the
MVCs
of
the
whole
deltoid,
activation levels. To determine the weights of the loads, the MVCs of the whole deltoid, were measured
were measured
three
tasks before
for each
subject
the formal
experiments.
Finally,
under
three tasksunder
for each
subject
the
formalbefore
experiments.
Finally,
three loads
of 1, three
2 andloads
3 kg,
of 1, 2 and 3 kg,
respectively,
which could cover
approximately
coverlevel
the activation
level20%
ranging
from
respectively,
which
could approximately
the activation
ranging from
to 60%
of20%
the
to
60%
of
the
MVC,
were
used.
MVC, were used.
2.3.
2.3. Data Collection
During
During the
the experiment,
experiment, the
the rough
rough region
region of
of the
the deltoid
deltoid and
and a reference
reference point
point called
called the acromion
acromion
were
palpation.
Then,
thethe
skin
areaarea
of the
deltoid
was was
shaved
and cleaned
with
werefirstly
firstlydetected
detectedthrough
through
palpation.
Then,
skin
of the
deltoid
shaved
and cleaned
alcohol.
Three Three
bipolarbipolar
electrodes
were placed
the three
deltoid
by heads
elastic by
adhesive
with alcohol.
electrodes
were on
placed
on the
threeheads
deltoid
elasticbandages
adhesive
in
order toin
reduce
detailed
of these electrodes
are shownare
inshown
Figure in
2. Figure
“Ch1”
bandages
order artefacts.
to reduce The
artefacts.
Theplacements
detailed placements
of these electrodes
2. “Ch1”
was
about
onewidth
fingerdistal
widthand
distal
and anterior
the acromion
and directed
the
was
placed
atplaced
about at
one
finger
anterior
to the to
acromion
and directed
in theinline
line between
the acromion
thumb.
“Ch2”
was
placed
the
greatestbulge
bulgeofofthe
themuscle
musclefrom
from the
the
between
the acromion
andand
thumb.
“Ch2”
was
placed
in in
the
greatest
acromion to the
the lateral
lateral epicondyle of the elbow and in the line between the acromion and the hand.
acromion
“Ch3”was
wasplaced
placedat
atthe
the area
areaabout
about two
two finger
finger breaths
breaths behind
behind the
the angle
angle of
of the
the acromion
acromion and
and oriented
oriented
“Ch3”
in the
the line
line between
between the acromion
acromion and
and the little finger.
finger. The
The placements
placements of
of SEMG
SEMG sensors
sensors on
on the
the deltoid
deltoid
in
followed the
the recommendation
recommendation of the
the SENIAM
SENIAM project
project [25,26].
[25,26]. The reference electrode
electrode was
was placed
placed at
at
followed
the proximal
proximal head
head of
of the
the elbow
elbow on
on the
the same
same arm.
arm. Electrode
Electrode locations
locations were
were marked
marked for
for each
each subject
subject in
in
the
order to
to ensure
ensure the
the consistency
consistency of
of sensor
sensor placements
placements during
during the
the following
following two
two days.
days. A
A home-made
home-made
order
16-channel SEMG
SEMG acquisition
acquisition system
system (actually,
(actually, only
only three
three channels
channels were
were used) was used in this
16-channel
this study.
study.
Each SEMG
SEMG sensor
sensor has
has two
two parallel
parallelAg
Agbars
barswith
withaa10
10mm
mm×× 1 mm physical dimension
dimension and
and aa 10-mm
10-mm
Each
center-to-center spacing
spacing respectively,
respectively,as
aspresented
presentedin
inFigure
Figure2.2.An
Anamplifier
amplifierwith
withaa total
total gain
gain of
of 37
37 dB
dB
center-to-center
and aa band-pass
band-pass filter with
with bandwidth between 20 and 500 Hz were used for analog SEMG signals.
and
Then the
the analog
analog SEMG
SEMG signals
signalswere
weresampled
sampledat
at1000-Hz
1000-Hzsampling
samplingrate
rateusing
usingaa24-bit
24-bitA/D
A/D converter
converter
Then
(ADS 1299,
1299, Texas
Texas Instruments,
Instruments, Inc.,
Inc., Dallas,
Dallas, TX,
TX, USA).
USA). All
All data
data were
were stored
stored on
on computer
computer disks
disks for
for
(ADS
further off-line
off-line analysis.
analysis.
further
Figure 2. Placement of EMG sensors on the deltoid. Here “Ch” denotes the channel of EMG
EMG sensor.
sensor.
The whole
whole experiment
experiment was
was completed
completed in
in three
three days
days in
in case
case of
of fatigue
fatigue accumulation.
accumulation. On
On the
the first
first
The
day,subjects
subjectswere
wereasked
askedto
tosuccessively
successivelyperform
perform three
threehead-related
head-relatedtasks
tasksat
at 11 kg
kg load.
load. Between
Between each
each
day,
task,
a
30-min
rest
was
required
to
reduce
muscle
fatigue
accumulation.
In
the
following
two
days,
task, a 30-min rest was required to reduce muscle fatigue accumulation. In the following two days,
subjects repeated
repeateda asimilar
similar
experimental
process
2 kg3 and
3 kg respectively.
loads, respectively.
All
subjects
experimental
process
withwith
the 2the
kg and
kg loads,
All subjects
subjects
weretotrained
to three
do these
three
tasks correctly
the experiments.
were
trained
do these
tasks
correctly
before thebefore
experiments.
Entropy 2017, 19, 221
5 of 15
2.4. Signal Preprocessing
Raw SEMG signals were filtered firstly (4-th order Butterworth digital high-pass filter with a
20 Hz cutoff) to remove the noise which mainly consists of low frequency components. Then, filtered
SEMG signals were divided into 2 s long epochs (non-overlap) using a moving window.
2.5. SEMG Parameters
(1)
Root mean square (RMS)
Parameter RMS, which could be used for the measurement of SEMG amplitude and has been
verified to increase with fatigue, is defined as in Equation (1), where T is the length of SEMG signals [2].
In this study, the average of RMS (MRMS) is calculated using Equation (2) to estimate the activities of
three heads, where M indicates the number of epochs:
s
RMS =
1
T
Z T
MRMS =
(2)
0
1
M
SEMG (t)2 dt,
(1)
M
∑ RMSi ,
(2)
i =1
Mean power frequency (MPF)
During the fatiguing process, the power spectral density was found to move toward low
frequencies and this phenomenon could be described indirectly by the MPF parameter [2].
The definition of MPF is presented in Equation (3), where PSD is the power spectral density of
SEMG signals and f s is the sampling frequency:
R
MPF =
(3)
f s /2
f
0
R f s /2
0
∗ PSD ( f )d f
PSD ( f )d f
,
(3)
The first coefficient of the autoregressive model (ARC1)
The AR model could be mathematically expressed using Equation (4), where e(n) stands for
smooth white noise process and p is the order of model:
p
SEMG (n) = − ∑ ai × SEMG (n − i ) + e(n),
(4)
i =1
Many studies have demonstrated that the first coefficient of the AR model (a1 ) decreases during
the fatiguing process [4,20].
(4)
Sample entropy (SE)
The entropy is introduced in the field of information theory as a non-linear measurement of the
complexity of signals. During isometric fatiguing contraction, the entropy of SEMG was found to
decrease with time [27]. SE was derived from approximate entropy and the calculation procedure
was described below. In order to compute SE, a delayed m-dimensional vectors from a given signal
S = [s(1), s(2), . . ., s(n)] is constructed via Equation (5):
−1
s( p) = [s( p + k)]m
k =0 , p = 1, 2, . . . , n − m + 1,
(5)
B m + 1 (r )
SE = − ln
,
B m (r )
(6)
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6 of 15
The sample entropy is then computed using Equation (6), where Bm (r ) stands for the probability
that two sequences match for m points. Bm (r ) was calculated by counting the average number of
vector pairs, for which the distance is lower than the tolerance r [28]. We set m = 2 and r = 0.2 × SD
in this study, where SD refers to the standard deviation of the original signal [28].
(5)
Higuchi’s fractal dimension (HFD)
In mathematics, a fractal dimension is a statistical index which is used for measuring the
space-filling capacity of a pattern. SEMG signals have self-similarity properties, which means that if
SEMG signals are split into parts, each of these parts is a reduced-size copy of original signal [2,29].
A previous study demonstrated that HFD parameters of SEMG signals decreased with time during
fatiguing contractions [30]. A brief introduction of Higuchi’s algorithm for fractal dimension calculation
is provided below [31]:
Step 1: construct k new signals from a given SEMG signal S = [s(1), s(2), . . ., s( N )] according
k stands for
to Equation (7), where m and k are integers indicating the initial and interval time and Sm
newly constructed signals:
N − m
k
Sm
= s ( m ), s ( m + k ), s ( m + 2 ∗ k ), . . . , s m +
∗k
, m = 1, 2, . . . , k,
k
(7)
k according to Equation (8) and compute the average of
Step 2: calculate the length Lm (k) of Sm
Lm (k) over m called L(k):
(
Lm (k ) =
b( N −m)/kc
∑ i =1
)
|s(m + i ∗ k) − s(m + (i − 1) ∗ k)| ∗ ( N − 1)
/k,
b( N − m) / kc ∗ k
(8)
Step 3: plot L(k ) against k (ranging from 1 to k max , we set k max = 16 in this study) on a double
logarithmic scale and calculate the slope of this line as the HFD parameter.
2.6. The Performance of Fatigue Indexes in Quantification of Fatigue
Numerous studies have manifested that SEMG variables were linearly correlated with time
during static isometric fatiguing contraction tasks [17,20,30]. To relate the changes in power loss
caused by muscle fatigue to the changes in SEMG variables, the linear regression technique was
highly recommended in the studies of González-Izal et al. [32,33]. In this study, RMS, MPF, ARC1, SE
and HFD were firstly calculated for each SEMG epoch. Then all parameters were normalized to the
value obtained at the first epoch. After that, the linear regression technique was applied to depict the
variation trend for these parameters during fatiguing process.
The performance of five SEMG parameters in depicting muscle fatiguing characteristics was
evaluated via SVR and consistency. In detail, when the variation trend of a SEMG parameter with time
was represented by a linear fitting curve, SVR could be defined as in Equation (9) [34]:
max Î − min Î
SVR = q
2 ,
N
1
I
−
Î
∑
n
N n =1 n
(9)
In Equation (9), I represents the true value of the parameter, Î is the value of fitting curve at the
corresponding point. The numerator describes the sensitivity of Î and the denominator stands for the
variability of I. High SVR value means high sensitivity and low variability of the SEMG parameter in
depicting muscle fatigue. When a parameter shows a relatively fixed variation trend (decreasing or
increasing) due to fatigue during all fatiguing processes, it can be considered to have good consistency
in depicting fatiguing characteristics. In this study, the slopes of the linear fitting curves of SEMG
parameters were calculated to represent their variation trend during a fatigue process. For a SEMG
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parameter, the combination of three tasks and three loads formed nine fatiguing processes, and all
cases were divided into two classes according to the positive or negative characteristics of the slopes of
the corresponding linear fitting curves. Consistency parameter was defined as Equation (10) in which
Num p and Numn stand for the number of cases that linear regression slope was positive and negative
respectively. The consistency parameter ranges from 0 to 1, and a good fatigue-depicting index should
has high consistency:
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Num p
Numn + Num p , if Num p > Numn
consistency =
(10)
Numn consistency was selected,to depict MTUs’ fatigue
The SEMG parameter with high SVR and
good
Numn + Num p , if Num p ≤ Numn
(
characteristics in further head-related muscle fatigue analysis, and the linear regression slope of that
The SEMG
parameterto
with
SVR and
consistency
was selected to depict MTUs’ fatigue
parameter
was considered
be ahigh
descriptor
of good
fatiguing
rate [17].
characteristics in further head-related muscle fatigue analysis, and the linear regression slope of that
2.7.
Statistical
Analysis
parameter
was
considered to be a descriptor of fatiguing rate [17].
order toAnalysis
investigate possible between-task and between-load differences in terms of activity
2.7. In
Statistical
and fatiguing rate of one head we ran a two-way repeated measures ANOVA for the dependent
In order
to investigate
possible
and
between-load
differences
terms of activity
and
variables
MRMS
and the slopes
ofbetween-task
fitting curves.
The
design included
two in
independent
with-in
fatiguing
rate ofeach
one consisting
head we ran
two-way
measures
ANOVA
for “Task
the dependent
subjects
factors,
of athree
levelsrepeated
(Task: “Task
1”, “Task
2” and
3”; Load:variables
“1 kg”,
MRMS
and
the
slopes
of
fitting
curves.
The
design
included
two
independent
with-in
subjects
“2 kg” and “3 kg”). Additionally, in order to examine the difference in activity among heads afactors,
oneeachrepeated
consistingmeasures
of three levels
(Task:for
“Task
“Task 2” and
“Task 3”;
Load:was
“1 kg”,
“2 kg”The
and design
“3 kg”).
way
ANOVA
the 1”,
dependent
variables
MRMS
applied.
Additionally,
in order to examine
the difference
in each
activity
among heads
a one-way
repeated
measures
included
one independent
within-subjects
factor,
consisting
of three
levels (Head:
“Anterior”,
ANOVA
for
the
dependent
variables
MRMS
was
applied.
The
design
included
one
independent
“Lateral” and “Posterior”). The level of statistical significance was set to p < 0.05 for all analyses. All
within-subjects
factor,
consisting
of three
levelssoftware
(Head: “Anterior”,
“Lateral”
andChicago,
“Posterior”).
statistical
analyses
wereeach
carried
out using
the SPSS
(version 20.0,
SPSS Inc.,
IL,
The level of statistical significance was set to p < 0.05 for all analyses. All statistical analyses were
USA).
carried out using the SPSS software (version 20.0, SPSS Inc., Chicago, IL, USA).
3. Results
3. Results
3.1. Activation Level Analysis of the Three Deltoid Heads during Three Head-Related Tasks
3.1. Activation Level Analysis of the Three Deltoid Heads during Three Head-Related Tasks
As mentioned above, the MRMS parameter of SEMG signals was used to estimate muscle
As mentioned above, the MRMS parameter of SEMG signals was used to estimate muscle
activation levels in this study. Figure 3 shows MRMS values (mean and standard deviation for all
activation levels in this study. Figure 3 shows MRMS values (mean and standard deviation for
nine subjects) of the three heads, during three head-related tasks with different loads.
all nine subjects) of the three heads, during three head-related tasks with different loads.
Figure3.3.MRMS
MRMSofofthe
thethree
threedeltoid
deltoidheads
headsduring
duringthree
threehead-related
head-relatedtasks
taskswith
withdifferent
differentloads.
loads.The
The
Figure
boxes
and
whiskers
show
the
mean
and
standard
deviation
respectively
for
all
nine
subjects.
boxes and whiskers show the mean and standard deviation respectively for all nine subjects.
The
Thestatistical
statisticalresults
resultsininTable
Table11indicate
indicatethat
thatfactors
factorsincluding
includingtask
taskand
andload
loadhad
hadsignificant
significant
effects
ofof
thethe
three
deltoid
heads.
ForFor
Task
1, there
werewere
significant
differences
in thein
effectson
onthe
theactivities
activities
three
deltoid
heads.
Task
1, there
significant
differences
MRMS values among the three heads under all loads (p values, 1 kg: 0.002, 2 kg: 0.001 and 3 kg:
0.000). The anterior head obtained the highest MRMS values, followed by the lateral head. For Task
2, the anterior head and the lateral head gave similar MRMS values which were much higher than
that of the posterior head (p values, 1 kg: 0.092, 2 kg: 0.045 and 3 kg: 0.059). For Task 3, there were
also significant differences in the MRMS values among the three heads under all loads (p values, 1
kg: 0.000, 2 kg: 0.000 and 3 kg: 0.000). The posterior head gave the highest MRMS values, followed
Entropy 2017, 19, 221
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the MRMS values among the three heads under all loads (p values, 1 kg: 0.002, 2 kg: 0.001 and 3 kg:
0.000). The anterior head obtained the highest MRMS values, followed by the lateral head. For Task 2,
the anterior head and the lateral head gave similar MRMS values which were much higher than that
of the posterior head (p values, 1 kg: 0.092, 2 kg: 0.045 and 3 kg: 0.059). For Task 3, there were also
significant differences in the MRMS values among the three heads under all loads (p values, 1 kg:
0.000, 2 kg: 0.000 and 3 kg: 0.000). The posterior head gave the highest MRMS values, followed by the
lateral head. The results mentioned above demonstrate that the anterior head was mainly activated
during Task 1, the posterior head was mainly activated during Task 3, and the anterior head and the
lateral head were activated almost equally in Task 2. Although there were significant differences in
Entropy 2017, 19, 221
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the activation levels of the three deltoid heads in Task 1 and Task 3, it was inaccurate to conclude
that
the anterior
posteriorand
heads
were activated
preferentially
in Task 1 andinTask
to conclude
that and
the anterior
posterior
heads were
activated preferentially
Task3,1respectively.
and Task 3,
More
precisely,
Task
1,
Task
2
and
Task
3
should
be
considered
as
the
anterior-lateral-head-related
task,
respectively. More precisely, Task 1, Task 2 and Task 3 should be considered as the anterior-lateralthe
anterior-lateral-posterior-head-related
task
and
the
lateral-posterior-head-related
task
respectively.
head-related task, the anterior-lateral-posterior-head-related task and the lateral-posterior-headMoreover,
although
three heads
showed
different
activation
levels in
three head-related
motion
related task
respectively.
Moreover,
although
three
heads showed
different
activation levels
in tasks,
three
all
of
them
exhibited
an
increase
in
activation
level
with
the
rising
in
loads
no
matter
which
motion
head-related motion tasks, all of them exhibited an increase in activation level with the rising in
loads
task
was conducted.
no matter
which motion task was conducted.
Table 1. Results of two-way repeated measure ANOVA for MRMS values in the three deltoid heads.
Table 1. Results of two-way repeated measure ANOVA for MRMS values in the three deltoid heads.
Factors
Factors
Significance(p
(pValues)
Values)
Significance
Anterior
Lateral
Posterior
Anterior
Lateral
Posterior
0.001
*
0.007
*
0.000
*
0.001 *
0.007 *
0.000
*
0.000
*
0.000
*
0.000
*
0.000 *
0.000 *
0.000 *
Task Task
Load Load
* Statistically
correlation
< 0.05.
* Statistically significant
significant correlation
at pat<p0.05.
3.2. Performance
Performance of
of Five
Five SEMG
SEMG Parameters
Parameters in
in Quantification
QuantificationofofFatigue
Fatigue
3.2.
The SVR
SVR indexes
indexes (Mean
(Mean ±
± SD
for nine
nine subjects)
subjects) of
of five
five SEMG
SEMG parameters
parameters in
in the
the three
three deltoid
deltoid
The
SD for
heads
during
three
head-related
tasks
with
three
loads
are
shown
in
Figure
4.
Taking
all
subjects,
heads during three head-related tasks with three loads are shown in Figure 4. Taking all subjects, tasks
tasksloads
and into
loads
into account,
the proportional
distribution
of the
two corresponding
classes, corresponding
to the
and
account,
the proportional
distribution
of the two
classes,
to the positive
positive
or
negative
characteristics
of
the
linear
regression
slopes,
is
demonstrated
in
Figure
5.
or negative characteristics of the linear regression slopes, is demonstrated in Figure 5.
Figure
Figure4.4. SVR
SVR indexes
indexes of
of five
five SEMG
SEMG parameters
parameters for
for the
the anterior
anterior head
head (a),
(a), the
the lateral
lateral head
head (b)
(b) and
and the
the
posterior
during
three
isometric
contraction
tasks with
loads. The
boxes
andboxes
whiskers
posteriorhead
head(c)(c)
during
three
isometric
contraction
tasksdifferent
with different
loads.
The
and
show
the mean
standard
for values
all ninefor
subjects.
whiskers
show and
the mean
anddeviation
standard values
deviation
all nine subjects.
Figure 5. Proportional distribution of two classes, corresponding to the positive or negative
Figure 4. SVR indexes of five SEMG parameters for the anterior head (a), the lateral head (b) and the
head (c) during three isometric contraction tasks with different loads. The boxes and9 of 15
Entropyposterior
2017, 19, 221
whiskers show the mean and standard deviation values for all nine subjects.
Figure 5. Proportional distribution of two classes, corresponding to the positive or negative
Figure 5. Proportional distribution of two classes, corresponding to the positive or negative
characteristics of linear regression slopes for five SEMG parameters. (a) In the anterior head; (b) In
characteristics of linear regression slopes for five SEMG parameters. (a) In the anterior head; (b) In the
the lateral head; (c) In the posterior head.
lateral head; (c) In the posterior head.
On the one hand, although there were large individual differences, HFD gave the highest SVR
Onwhile
the one
hand,
there
were
large
differences,
HFD gave
theall
highest
SVR
values
RMS
gavealthough
the lowest
SVR
values
in individual
all three heads
of the deltoid
during
nine cases.
values
theHDF
lowest
values in allobtained
three heads
of consistency
the deltoid during
all nine
cases.
On thewhile
otherRMS
hand,gave
MPF,
andSVR
SE parameters
good
in all three
heads.
In
On
the
other
hand,
MPF,
HDF
and
SE
parameters
obtained
good
consistency
in
all
three
heads.
In
almost 97% cases, the linear regression slopes of these parameters maintained negative. RMS and
almost 97% cases, the linear regression slopes of these parameters maintained negative. RMS and
ARC1 parameters showed bad consistency since only in 79% cases, the linear regression slopes of these
parameters maintained positive. Taking both SVR and consistency into consideration, HFD which
obtained a high SVR (Anterior: 497.77 ± 397.76, Lateral: 668.28 ± 405.90, Posterior: 520.03 ± 460.86)
and high consistency (Anterior: 0.96, Lateral: 1.00, Posterior: 0.99) was selected as a fatigue index for
further head-related muscle fatigue analysis. Specifically, the value of the linear regression slope of
HFD was adopted to depict the fatiguing rate of the three deltoid heads.
3.3. HFD-Based Fatigue Analysis of the Three Deltoid Heads
Taking one subject as an example, Figure 6 reports the normalized HFD values and the linear
fitting of HFD against number of epochs in the three deltoid heads during three head-related tasks
with different loads. From Figure 6, HFD was found to be linearly correlated with muscle fatigue, and
the fatigue-related decreases were observed in the three deltoid heads in all cases. For all subjects,
the linear regression slopes of HFD under different conditions are presented in Figure 7, and Table 2
gives the statistical results of two-way repeated measure ANOVA (factors: task and load).
In this study, load factor was found to have a significant effect on the fatiguing characteristics for
all three heads of the deltoid (Table 2, p < 0.05). From Figure 7, we can observe that, with the increase
of load, the fatiguing rates of three heads all increased. These results revealed that the heads were
more prone to fatigue in the case of high load. Meanwhile, the differences of fatiguing rate among
heads were also found to increase with the increase in load during all tasks.
Table 2. Results of two-way repeated measure ANOVA for linear regression slopes of HFD in the three
deltoid heads.
Factors
Task
Load
Significance (p Values)
Anterior
Lateral
Posterior
0.782
0.004 *
0.031 *
0.000 *
0.000 *
0.000 *
* Statistically significant correlation at p < 0.05.
In addition, the three deltoid heads were found to show different fatiguing rates in the same task.
Combining Figures 3 and 7, we can find that preferentially activated heads were usually more prone to
fatigue. In Task 1, the anterior head and the lateral head showed similar fatiguing rates which were
higher than that in the posterior head. In Task 2, the lateral head showed the highest fatiguing rate
3.3. HFD-Based Fatigue Analysis of the Three Deltoid Heads
Taking one subject as an example, Figure 6 reports the normalized HFD values and the linear
fitting of HFD against number of epochs in the three deltoid heads during three head-related tasks
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2017, 19, 221
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with different
loads. From Figure 6, HFD was found to be linearly correlated with muscle fatigue,
and the fatigue-related decreases were observed in the three deltoid heads in all cases. For all subjects,
the linear regression slopes of HFD under different conditions are presented in Figure 7, and Table 2
followed by the anterior head. In Task 3, the posterior head showed the highest fatiguing rate followed
gives the statistical results of two-way repeated measure ANOVA (factors: task and load).
by the lateral head.
Figure 6. The linear fit of HFD against time (number of epochs) in the three heads of the deltoid under
Figure 6. The linear fit of HFD against time (number of epochs) in the three heads of the deltoid under
nine conditions for one subject. (a) Task1 and load = 1 kg; (b) Task1 and load = 2 kg; (c) Task1 and
nine conditions for one subject. (a) Task1 and load = 1 kg; (b) Task1 and load = 2 kg; (c) Task1 and load
load = 3 kg; (d) Task2 and load = 1 kg; (e) Task2 and load = 2 kg; (f) Task2 and load = 3 kg; (g) Task3
= 3 kg; (d) Task2 and load = 1 kg; (e) Task2 and load = 2 kg; (f) Task2 and load = 3 kg; (g) Task3 and
and
load
=221
1 kg; (h) Task3 and load = 2 kg; (i) Task3 and load = 3 kg.
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2017,
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load
= 119,kg;
(h) Task3 and load = 2 kg; (i) Task3 and load = 3 kg.
Table 2. Results of two-way repeated measure ANOVA for linear regression slopes of HFD in the
three deltoid heads.
Factors
Task
Load
Significance (p Values)
Anterior
Lateral
Posterior
0.782
0.031 *
0.000 *
0.004 *
0.000 *
0.000 *
* Statistically significant correlation at p < 0.05.
Figure 7. Boxplot of linear regression slopes of HFD for all subjects under different conditions. Box
Figure 7. Boxplot of linear regression slopes of HFD for all subjects under different conditions. Box
plots show the results for all subjects, the middle line in each box plot represents the median value,
plots show the results for all subjects, the middle line in each box plot represents the median value, and
and the whisker indicates the range. The bottom and top limits of each box reveal the interquartile
the whisker indicates the range. The bottom and top limits of each box reveal the interquartile range
range
andplus
blacksigns
plusdenote
signs denote
outliers.
and
black
outliers.
In this study, load factor was found to have a significant effect on the fatiguing characteristics
for all three heads of the deltoid (Table 2, p < 0.05). From Figure 7, we can observe that, with the
increase of load, the fatiguing rates of three heads all increased. These results revealed that the heads
were more prone to fatigue in the case of high load. Meanwhile, the differences of fatiguing rate
among heads were also found to increase with the increase in load during all tasks.
In addition, the three deltoid heads were found to show different fatiguing rates in the same
task. Combining Figures 3 and 7, we can find that preferentially activated heads were usually more
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4. Discussion
Previous studies have found that there existed region-specific manifestation of fatigue within
some skeletal muscles. The differences in control strategies of the CNS for different regions were
considered as a major factor resulting in the region-specific manifestation of fatigue. However, all these
studies did not relate the substructures within skeletal muscles (such as MTU) with the region-specific
manifestation of fatigue. In this study, the deltoid was targeted as the object, and the fatiguing
characteristics of the three heads during three head-related static isometric contraction tasks were
investigated by means of SEMG technique. The experimental results of this study can help us better
understand the underlying neuromuscular control strategies of the CNS.
4.1. The Performance in Quantification of Fatigue for Different SEMG Parameters
In relevant researches, the variation in SEMG parameters was considered to be related to the
changes of MU recruitment patterns and biochemical environment of muscle, which is caused by
fatigue [2,4,18]. In this study, the performance of five SEMG parameters (RMS, MPF, ARC1, HFD
and SE) in depicting fatiguing characteristics of heads was evaluated, under different tasks and loads.
Being consistent with previous studies [20,27,30], RMS and ARC1 (normalized) parameters showed
increasing trends with fatigue in most cases. MPF, HFD and SE parameters showed decreasing trends
with fatigue almost in all cases. In order to find an effective parameter to depict muscle fatigue
linearly during isometric contraction protocol, SVR and consistency were defined as criteria for the
assessment of the performance in quantification of muscle fatigue. Results indicated that HFD showed
the maximal SVR index and consistency for three head-related tasks with different loads. Arjunan et al.
found that increase in synchronization (IIS) index of SEMG, had the highest correlation with muscle
fatigue, compared with other SEMG parameters such as RMS, MPF, and waveform length (WL) [21].
This means that parameters which could describe the MUs synchronization well would be suitable
for muscle fatigue assessment. Exactly, HFD was proved to be one of those parameters [35–37]. In
addition, Troiano et al. found that the fractal dimension of SEMG was not affected by force level during
non-fatiguing contractions but decreased against time during fatiguing contractions [30]. Therefore,
HFD is a promising index in depicting fatigue of the three deltoid heads during head-related isometric
contraction tasks.
4.2. Possible Control Strategies of the CNS to Control the Three Deltoid Heads during Contraction Task
In 2015, Franke et al. conducted a study to analyze the activities of the anterior, lateral and
posterior deltoid during single and multi-joint exercises including the inclined lat pull-down, reverse
peck deck and seated row [38]. They found that the activation of the anterior portion of deltoid muscle
maintained in similar level in the three exercises, while the lateral head presented greater activation
during the reverse peck deck and the seated row compared to the inclined lat pull-down, and the
posterior head showed greater activation during the reverse peck deck compared to the other two
exercises. In our study, we tried to design three head-related motion tasks to activate the three deltoids
heads relatively independently. The experimental results revealed that the anterior head, the lateral
head and posterior head were activated in different modes in the three head-related tasks. Despite the
exercises in the study of Franke et al. were different from the tasks defined in this study, both of the
studies proved that the deltoid presented region-specific manifestation of activation in different motion
tasks and the heads might be the functional unit of the deltoid. Additionally, all three deltoid heads
showed a sign of fatigue no matter which motion task was conducted in this study. Moreover, with
the rising of loads, the activation levels and the fatiguing rates of all three heads exhibited increasing
trends. These experimental results suggested that, although the CNS might control the contraction of
the deltoid by taking the three heads as functional units, certain synergies between heads might exist
when muscle accomplishes a contraction task [11,39].
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4.3. Possible Reasons for the Differences of the Fatiguing Characteristics among the Three Deltoid Heads
Previous studies have found that the region-specific manifestation of muscle fatigue existed
in some skeletal muscles, for example, the retus femoris muscle and the medial gastrocnemius
muscle [15,16]. Later, Ali et al. reported the difference of fatigue characteristics existed in the three
heads of triceps brachii during a static task, in which the activation level was maintained at 80%
MVC [17]. Consistent with the above studies, this study also found the region-specific manifestations
of fatigue between the three deltoid heads during three head-related static isometric contraction tasks.
From the perspective of muscle sub-structures, the fatiguing characteristics of the three deltoid heads
were found to be task-dependent, and the factor of load was found to have significant effects on the
fatiguing rate of all three heads. Specifically, the heads were more prone to fatigue in the case of high
load, and the differences in fatiguing rate between heads became larger with the increase in load in
each task.
In fact, muscle fatigue can be divided into central and peripheral fatigue according to its origin [36].
“Central fatigue” is related to a decline of motor drive, MUs synchronization, and the variations
in control strategy of the CNS. “Peripheral fatigue” can be related to biochemical changes of the
environment locally. From the perspective of “peripheral fatigue”, a higher activation level would
lead to a faster accumulation of metabolites like lactates. Increased concentration of the lactates is
responsible for fatigue via changes in intracellular pH, which would lower the muscle fiber conduction
velocity (CV) [4]. In this study, heads which maintained in higher activation level would consume
more nutrients and oxygen, and produce more lactates, compared with heads maintained in low
activation level. Consequently, a higher concentration of lactates contributes to a higher fatiguing
rate in these preferentially activated heads. In addition, with the increase of loads, the difference of
activities among heads was gradually widening, as shown in Figure 3. Therefore, the differences in
fatiguing rate between heads became larger with the increase in load.
From the perspective of “central fatigue”, the dissimilarities in muscle fiber type and the
motor recruitment pattern might be another main reason. Numerous studies have proved that MU
synchronization is a major factor which contributes to muscle fatigue [21,40,41]. MUs are made up
of two types of fibers, including Type I fibers (slow MU) and Type II fibers (fast MU). Slow MUs are
recruited first but have stronger fatigue resistance compared with fast MUs [42,43]. In the case of low
level load, slow MUs were mainly recruited in all heads so that MUs synchronization in all heads was
not obvious. Consequently, all three heads presented similar low fatiguing rates. However, in the
case of high level load, the preferentially activated heads started to recruit fast MUs but heads in low
activation level still mainly recruited slow MUs [43]. In this condition, the difference in terms of MUs
synchronization between the preferentially activated head and the others began to appear. As a result,
the difference of fatiguing rate between heads became larger with the increase of loads.
4.4. Limitations
In this study, the fatiguing characteristics of MTUs within the deltoid during static isometric
contraction tasks were investigated via SEMG. Although some interesting results were obtained, some
potential issues were noted. Firstly, due to the difficulties in the measurement of the MVCs for MTUs,
a compromised approach namely step-increasing loads was used to activate MTUs. This approach
inevitably causes individual differences between subjects because different subjects had different load
acceptance. The other issue was the bias caused by electrode misalignment. Although we have placed
the sensors in strict accordance with the guidance of the SENIAM project, the differences in muscle
shape and fat layer thickness of subjects inevitably cause some bias.
5. Conclusions
In this paper, the performance of five SEMG parameters in depicting the fatiguing characteristics
of MTUs was evaluated firstly. Then, fatigue analysis was conducted on the three deltoid heads during
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three head-related static isometric contraction tasks. The experimental results demonstrated that
the anterior head, the lateral head and posterior head were activated in different modes in the three
head-related tasks. The fatiguing characteristics of the three heads were found to be task-dependent,
and the heads at high activation level were more prone to fatigue. In addition, the fatigue differences
among heads increased with the increase in load. The findings of this study could help us better
understand the underlying neuromuscular control strategies of the CNS, and are meaningful for
training guidance, rehabilitation motion design, and establishment of biomechanical model in the
fields of fitness, rehabilitation medicine, biomechanics, kinesiology and neuroscience.
Acknowledgments: We are grateful to all the subjects for their participation in this study. This work was
supported by the National Nature Science Foundation of China (NSFC) under Grant 61431017 and 61671417.
Author Contributions: Wenxiang Cui performed the experiments, analyzed the data, interpreted the results and
wrote the first draft of the manuscript. Xiang Chen provided instructions on all stages of the study including
experimental design, data analysis, interpretation, and substantial revision of the manuscript. Shuai Cao put
efforts on data acquisition system. Xu Zhang provided guidance on data analysis and interpretation. All authors
have read and approved the final version of the manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
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