A Comprehensive Evaluation of Spine Kinematics, Kinetics, and Trunk Muscle Activities During Fatigue-Induced Repetitive Lifting

Abstract

Objective
Spine kinematics, kinetics, and trunk muscle activities were evaluated during different stages of a fatigue-induced symmetric lifting task over time.

Background
Due to neuromuscular adaptations, postural behaviors of workers during lifting tasks are affected by fatigue. Comprehensive aspects of these adaptations remain to be investigated.

Method
Eighteen volunteers repeatedly lifted a box until perceived exhaustion. Body center of mass (CoM), trunk and box kinematics, and feet center of pressure (CoP) were estimated by a motion capture system and force-plate. Electromyographic (EMG) signals of trunk/abdominal muscles were assessed using linear and nonlinear approaches. The L5-S1 compressive force (Fc) was predicted via a biomechanical model. A two-way multivariate analysis of variance (MANOVA) was performed to examine the effects of five blocks of lifting cycle (C1 to C5) and lifting trial (T1 to T5), as independent variables, on kinematic, kinetic, and EMG-related measures.

Results
Significant effects of lifting trial blocks were found for CoM and CoP shift in the anterior–posterior direction (respectively p < .001 and p = .014), trunk angle ( p = .004), vertical box displacement ( p < .001), and Fc ( p = .005). EMG parameters indicated muscular fatigue with the extent of changes being muscle-specific.

Conclusion
Results emphasized variations in most kinematics/kinetics, and EMG-based indices, which further provided insight into the lifting behavior adaptations under dynamic fatiguing conditions.

Application
Movement and muscle-related variables, to a large extent, determine the magnitude of spinal loading, which is associated with low back pain.

Full text

A Comprehensive Evaluation of Spine Kinematics, Kinetics,
and Trunk Muscle Activities During Fatigue- Induced
RepetitiveLifting
ZeinabKazemi, AdelMazloumi , Tehran University of Medical Sciences, Iran,
NavidArjmand, Sharif University of Technology, Iran, AhmadrezaKeihani,
ZanyarKarimi, Tehran University of Medical Sciences, Iran,
Mohamad SadeghGhasemi , Iran University of Medical Sciences, Iran, and
RaminKordi, Tehran University of Medical Sciences, Iran
Address correspondence to Adel Mazloumi, Department of
Occupational Health Engineering, School of Public Health,
Tehran University of Medical Sciences, P.O. BOX: 6446
Tehran 14155, Iran; e-mail: amazlomi@ tums. ac. ir
HUMAN FACTORS
Vol. 00, No. 0, Month XXXX, pp. 1-16
DOI: 10. 1177/ 0 018 7208 20983621
Article reuse guidelines: sagepub. com/ journals- permissions
Copyright © 2021, Human Factors and Ergonomics Society.
Objective: Spine kinematics, kinetics, and trunk muscle activi-
ties were evaluated during different stages of a fatigue- induced sym-
metric lifting task over time.
Background: Due to neuromuscular adaptations, pos-
tural behaviors of workers during lifting tasks are affected by
fatigue. Comprehensive aspects of these adaptations remain to be
investigated.
Method: Eighteen volunteers repeatedly lifted a box until
perceived exhaustion. Body center of mass (CoM), trunk and box
kinematics, and feet center of pressure (CoP) were estimated by a
motion capture system and force- plate. Electromyographic (EMG)
signals of trunk/abdominal muscles were assessed using linear and
nonlinear approaches. The L5- S1 compressive force (Fc) was pre-
dicted via a biomechanical model. A two- way multivariate analysis of
variance (MANOVA) was performed to examine the effects of five
blocks of lifting cycle (C1 to C5) and lifting trial (T1 to T5), as inde-
pendent variables, on kinematic, kinetic, and EMG- related measures.
Results: Significant effects of lifting trial blocks were found for
CoM and CoP shift in the anterior–posterior direction (respectively
p < .001 and p = .014), trunk angle (p = .004), vertical box dis-
placement (p < .001), and Fc (p = .005). EMG parameters indicated
muscular fatigue with the extent of changes being muscle- specific.
Conclusion: Results emphasized variations in most kinematics/
kinetics, and EMG- based indices, which further provided insight into
the lifting behavior adaptations under dynamic fatiguing conditions.
Application: Movement and muscle- related variables, to a
large extent, determine the magnitude of spinal loading, which is
associated with low back pain.
Keywords: biomechanical models- spine, manual
material handling, electromyography, nonlinear
dynamical systems, motor control
INTRODUCTION
Muscle fatigue induced by lifting activities
may adversely aect neuromuscular control of
spinal stability (Mehta et al., 2014; Srinivasan
& Mathiassen, 2012). Neuromuscular mecha-
nisms including reex response, active muscle
stiness, and muscle recruitment contribute to
the mechanical stability of the spine. During
physical activities, mechanical/chemical stimuli
induced by muscle contraction activate recep-
tors on group III/IV nerve endings on mus-
cles (Amann et al., 2015; Monjo et al., 2015).
The central reection of these aerents, their
inhibitory eects on moto- neurons as well as
the subsequent reduction in voluntary muscle
activation constitute a process that causes cen-
tral fatigue, thereby adversely aecting work-
ers’ performance (Amann et al., 2015; Sidhu
et al., 2014). Fatigue- induced reduction in
muscle stiness occurs as a result of a decrease
in co- contraction between agonist and antag-
onist muscles (Bonato et al., 2003; Granata
et al., 2004). Given the diminished stiness
of muscles, the compensatory recruitment of
antagonistic co- contraction is hence necessary
to maintain stability (Gardner- Morse et al.,
1995; Granata et al., 2004). It is also conrmed
that the recruitment of alternate agonistic and
antagonistic muscle groups is increased as a
result of fatigue in primary agonists (Sparto &
Parnianpour, 1998). These alterations together
may lead to increased spine compression forc-
es—a risk factor for micro- fracture of verte-
bral endplates (Brinckmann et al., 1988; Mehta
et al., 2014).

Month XXXX - Human Factors2
Furthermore, energy reservoir decreases
with muscular fatigue and thus enforcing the
central nervous system (CNS) to plan/execute
movements that aim to minimize energy expen-
diture while simultaneously maintaining task
performance (Bauer et al., 2017; Missenard
et al., 2008). In this respect, individuals may
adjust their working strategies to compensate
for fatigue during repetitive lifting (Mehta
et al., 2014). Optimal movement variations
are recognized as factors delaying the devel-
opment of fatigue during a prolonged activity
(Bartlett et al., 2007; Sedighi & Nussbaum,
2017; Srinivasan & Mathiassen, 2012) by dis-
tributing load across active- passive tissues,
thus maintaining performance (Bauer et al.,
2017; van Dieën et al., 2003). Depending on
the region and extent of fatigue, various behav-
ioral changes have been observed including
lifting style, switching between stoop and squat
postures (Bonato et al., 2003), and accelerated
forward bending (Bernardo et al., 2018; Chen,
2000).
Although a number of studies have attempted
to explore fatigue and its dependent postural
adaptations in lifting task, little research has
been conducted to investigate eects of fatigu-
ing conditions on simultaneous changes of spine
kinematics and muscle- related parameters. For
instance, postural kinematics and erector spinae
muscle activities were compared prior to, and
immediately following, a repetitive lifting task
by Dolan and Adams (1998) and Boocock et al.
(2015). In these studies and other similar studies
focused only on kinematic measures (Fischer
et al., 2015; Mehta et al., 2014), adaptations
during the course of lifting were neglected and
comparisons were limited to the initial and last
parts of the task. It is also worthwhile to note
that changes in the lifting strategy impact the
movement of the hand load and result in vary-
ing levels of compressive force (Fc) at the L5-
S1 disc (Chen, 2000). However, cycle- by- cycle
changes in the Fc at the L5- S1 disk have been
overlooked in the previous studies.
A key point, which is also neglected, is that
repetitive lifting is a complex dynamic task
(Chen, 2000) with nonlinear dynamical proper-
ties of biomechanical responses (Khalaf et al.,
2015). Recent research evidence supports the
fact that linear measures quantify the magnitude
of variation in a series of data irrespective of
their order in the distribution. In contrast, vari-
ations are more accurately drawn by nonlinear
measures where the temporal organization (or
structure) of distribution is targeted. Survey
of variability through measures for nonlinear
dynamics has thus attracted considerable atten-
tion because of the valuable information they
provide about human postural control system
(Cavanaugh et al., 2005; Stergiou & Decker,
2011). In this sense, healthy physiological
systems are often characterized by an irregu-
lar and complex type of variability (Donker
et al., 2007). With the development of fatigue,
a decreased complexity of muscle activity
has been reported (Cashaback et al., 2013).
However, how structure of muscular activity is
aected by fatigue under repetitive lifting task
remains to be investigated.
To address some of the mentioned limita-
tions and to gain deeper insights into the biome-
chanical properties of motor control strategies,
the present study aims to:
1. Measure cycle- by- cycle changes in body center of
mass (CoM) trajectory and a range of kinematic
data of trunk and hand load over a sagittally sym-
metric fatiguing repetitive lifting trial.
2. Quantify fatigue of trunk extensor and exor mus-
cles via assessment of their electromyographic
(EMG) activities. For this, both linear and non-
linear EMG parameters were used to understand
how fatigue development over a repetitive lifting
impacts magnitude and structure of muscle activ-
ity variability.
3. Evaluate the trend of center of pressure (CoP)
displacement and Fc at the L5- S1 disk during the
lifting cycle.
We sought to address the issue that continu-
ing lifting to exhaustion corresponds to a wide
range of alterations in trunk and hand load
kinematics and muscular activities, and, based
on these changes, we hypothesized that spinal
loads would be aected over time.
METHOD
Participants
Eighteen healthy male participants were
recruited from university students with a mean
(standard deviation, SD) age, stature, and body

Postural Behavior in rePetitive lifting 3
mass of 26.5 (2.9) years, 175.2 (6.1) cm, and
70.1 (7.3) kg, respectively. None of the par-
ticipants had experience in repetitive lifting
as a routine task. They did not have injuries,
disorders, or deformity, which may aect
their postural control or their ability to per-
form the activity. The study adhered to the
tenets of the Declaration of Helsinki and par-
ticipants completed a written consent form,
approved by Tehran University of Medical
Sciences Research Ethics Board (Approval No.
IR.TUMS.SPH.REC.1397.252).
Instrumentation and Data Acquisition
An 11- camera motion capture system (Vicon
Motion Systems Inc., Oxford, UK) set at a sam-
pling frequency of 120 Hz was used to record
3D kinematic data. Markers were bilaterally
attached to landmarks based on a standard marker
placement protocol to track the position and
movement of trunk (VICON- Documentation,
2020). In order to track the box trajectory, an
additional marker was attached to its top edge.
EMG activities of left and right erector spinae
longissimus (ESLR, ESLL), multidus (MFR,
MFL), latissimus dorsi (LDR, LDL), rectus
abdominis (RAR, RAL), and external oblique
(EOR, EOL) muscles were collected using a
wireless system at a sampling rate of 1200 Hz
(Myon 320, Switzerland). Bipolar surface elec-
trodes, with center- to- center distance of 2 cm,
were placed over the target muscles and paral-
lel to the muscle bers at: ~4 cm lateral to the
L3 spinous process for ESL, 1.5 cm lateral to
the L5 spinous process for MF, 2 cm distal to
the scapula inferior angle for LD, 3 cm above
the umbilicus and 3 cm lateral to midline for
RA, and 15 cm lateral to the umbilicus, in the
midpoint between the 12th rib and iliac crest,
for EO (Haddad & Mirka, 2013; Haddad, 2011;
Hardie et al., 2015). Ground reaction forces
data were recorded simultaneously using two
adjacent force platforms (Kistler Instrument
AG, Switzerland) at a sampling rate of 1200
Hz. Kinematics, EMG, and force- plate data
were automatically synchronized with Noraxon
hardware.
Experimental Procedures
Following a short warm- up stretching exer-
cises, including full exion/extension/lateral
bending/twisting, for 2–3 min (Ghesmaty
Sangachin & Cavuoto, 2016; Haddad & Mirka,
2013), subjects completed maximal volun-
tary contraction (MVC) for the assigned mus-
cles according to previous studies (Flint et al.,
2015; Nelson- Wong & Callaghan, 2010). Three
~5- s MVC tests were performed for each mus-
cle with a minimum of 30 s rest in between to
avoid fatigue. Before the repetitive lifting activ-
ity, EMG and motion data of participants were
recorded in a relaxed upright standing posture
for 3–5 s as a reference posture (i.e., static trial)
for the subsequent normalization purposes.
Subsequently, participants were instructed to
symmetrically lift/lower a two- handled box
(30.0 cm × 25.0 cm × 25.5 cm). For each par-
ticipant, box mass was adjusted equal to 10%
of his maximum lifting capacity determined by
a Chatillon force gage (model DFX; Ametek-
Chatillion, Largo, FL; Dolan & Adams, 1998).
Finally, participants were asked to stand at
a comfortable distance in front of the force-
plate. Given a verbal signal, they stood on the
force- plate with their feet placed symmetrically
shoulder- width apart and lifted a box from oor
to their waist height with a lifting/lowering pace
of ~10 lifts/min as controlled by a metronome
(Figure 1). Prior to the test, each participant was
asked to practice with an empty box and try to
maintain the metronome rate in order to provide
relatively constant cycles’ durations (~3 s). All
tests were performed in the morning. The whole
cycle time was divided into lifting and lower-
ing phases. The participant continued the lift-
ing/lowering activity until he failed to continue
the task and verbally reported the highest level
of exhaustion. In order to ensure that subjects
have reached their maximum fatigue level, par-
ticipants were asked to rate subjective percep-
tion of their fatigue by the end of each minute
during the course of the lifting task using a 10-
point Borg rating of perceived exertion (RPE)
scale (Ahmad & Kim, 2018; Bonato et al.,
2003; Fischer et al., 2015; Ghesmaty Sangachin
& Cavuoto, 2016). As the task was highly
demanding, subjects’ heart rate was recorded

Month XXXX - Human Factors4
by a wristwatch monitoring device (Beurer, PM
70, Germany) so that subjects did not to exceed
their maximum heart rate, calculated for each
subject according to a formula developed else-
where (Tanaka et al., 2001). Besides, before
performing the task, we explained the proce-
dure to our subjects and asked them to stop
the task at any time they felt uncomfortable.
Subjects were not instructed on any lifting tech-
nique (i.e., free- style lifting with no instruction
on knee/lumbar postures).
Data Analysis
Since postural adaptations as well as mus-
cular contraction properties are dependent on
the task type (lifting or lowering; Davis, 1996;
Mehta et al., 2014), only data related to lifting
phase of cycles were extracted for characteriz-
ing the parameters of interest across the trial.
According to data obtained by the motion cap-
ture system, each lifting cycle was considered as
the time instant that trunk was in maximum for-
ward exion angle in the sagittal plane through
the time that the subject placed the box on the
shelf (i.e., minimum sagittal trunk exion angle
just before becoming completely upright for the
rest phase). To extract the kinematics, EMG,
and kinetics parameters, all signals were pro-
cessed oine as follows.
Kinematics. The motion time series were
low- pass smoothed at 7 Hz using a 4th- order
digital Butterworth lter. The kinematic met-
rics selected as representative of fatigue- related
changes in our experiment were position of
body center of mass in anterior–posterior
direction (CoMA/P), vertical position of body
center of mass (CoMVertical), average trunk
exion angle in the sagittal plane (ATrunk), and
average trunk angular velocity (VTrunk). The
box kinematic variables were average vertical
travel distance of the box (XBox- Vertical), aver-
age box horizontal displacement from L5- S1
(XBox- Horizontal), and average box vertical dis-
placement velocity (VBox). Plug- in Gait Model
along with standard anthropometric measures
were applied (Davis et al., 1991) to generate
time- series data of trunk angular displacements
and velocities within the sagittal plane. The
3D trajectory of CoM for each participant was
determined using the kinematic centroid method
(Nexus, Vicon Motion Systems, Oxford, UK;
Baniasad et al., 2019). Box horizontal distance
from the L5- S1 was measured using the coordi-
nates of markers placed on hands and overlying
skin at the L5- S1 joint (Dolan & Adams, 1993).
EMG signal processing. All raw EMG sig-
nals were band- pass ltered using an 8th- order
Butterworth with a cut- o frequency of 20–450
Figure 1. A schematic of two- handed symmetric lifting setup. A box positioned not more
than 15 cm in front of the ankles, lifted from near oor level up to a shelf positioned at the
subject’s waist level. Each participant completed lifting/lowering cycles until exhaustion.

Postural Behavior in rePetitive lifting 5
Hz (Arjmand et al., 2010. Chen et al., 2016;
Gagnon et al., 2011; Samadi & Arjmand, 2018).
A 50- Hz notch lter was also applied to eliminate
the power line noise. All EMG recordings were
visually inspected for any noise spike (Trask et al.,
2010). Root mean square (RMS) values, as indic-
ative of signal amplitude, were calculated for the
static trial (RMSStatic) as well as each lifting cycle
(RMSCycle). From the three MVC eorts of each
muscle, the mean value of their RMS was con-
sidered RMSMVC. Subsequently, the normalized
RMS for each lifting cycle was computed by nor-
malizing muscle- specic RMSCycle with respect
to the corresponding RMSMVC after subtracting
RMSStatic (Yin et al., 2019). Fast Fourier transfor-
mation was employed to develop a power spec-
trum of the EMG signals, from which the median
frequency (MF) was computed, as the most com-
monly used parameter for fatigue assessment
(Boocock et al., 2015; Mawston et al., 2007).
RMS and MF are recognized as linear indi-
ces, characterizing time- frequency aspects of
EMG signals (Wang et al., 2018). Therefore, an
entropy- based method to determine the degree
of complexity was also employed to provide
deeper insight into the muscular mechanisms
in fatigue adaptation condition. Entropy is a
measure of complexity and randomness of
dynamic systems, expressing the rate of infor-
mation production (Zhang & Zhou, 2012).
Among various entropy methods, Sample
Entropy (SampEn) has been widely utilized
in physiological time- series analysis (Zhang
& Zhou, 2012). In order to calculate SampEn,
time series of length N is rst embedded in a
delayed m- dimensional space. SampEn is then
the negative natural logarithm of the condi-
tional probability that two similar sequences
of m points in a tolerance of r remain similar at
the next point (m + 1; Richman & Moorman,
2000). Self- matches are not included in calcu-
lating probability (Delgado- Bonal & Marshak,
2019). We chose m = 2 and r = .2 × SD of the
time series (equals to approximately 3 s; Lake
et al., 2002; Zhang & Zhou, 2012). Smaller
sample entropy values are associated with
greater regularity (Donker et al., 2007). In the
present study, a decrease in sample entropy
(i.e., more regular EMG signals) was inter-
preted as fatigue.
Kinetics. Force- plate data were low- pass
ltered at 10 Hz, using a dual- pass 4th- order
Butterworth lter. Ground reaction force data
were used to calculate the net center of pres-
sure, in the anterior–posterior (CoPA/P) and
medial–lateral (CoPM/L) directions (Fuller
et al., 2011). Furthermore, Fc at the L5- S1
disc (Fc in N) was calculated using the revised
Hand- Calculation Back Compressive Force
(HCBCF) model (Merryweather et al., 2009).
Inputs of this model are horizontal moment arm
of the hand- load to L5- S1, magnitude of the
hand load, trunk sagittal exion angle from the
upright position, body weight, and body height
(Merryweather et al., 2009).
Statistical Analyses
In order to plot uctuations in the kinematic
and kinetic variables, total duration of each
lifting cycle was divided into ve distinct time
blocks (0%–20%, 20%–40%, 40%–60%, 60%–
80%, 80%–100% of the cycle; called C1 to C5).
Since participants performed dierent numbers
of lifting cycles, the total number of lifting cycles
was also categorized into ve blocks (T1 to T5
each containing 20% of the number of cycles),
comprising 25 blocks for each participant (ve
blocks for lifting cycle duration × ve blocks for
lifting numbers). For muscular parameters, the
variables of interest were calculated as represen-
tative of each cycle (i.e., building only ve blocks
over lifting trial; T1 to T5). Data for each block
were expressed as the mean ± SD. For body CoM
and CoP displacements, time- series data were
detrended so that the changes could be readily
observed and the average of changes about a zero
mean over the 25 blocks were calculated.
A two- way multivariate analysis of variance
(MANOVA) was then used to test the eects of
cycle block (C1–C5), lifting trial blocks (T1–
T5), and C × T interactions on kinematic and
kinetic variables. Moreover, MANOVA analy-
sis was conducted to test the eects of lifting
trial blocks (T1–T5) on the dependent electro-
myographic variables. When appropriate, pair-
wise comparisons using Bonferroni post hoc
tests were included. An α level of .05 was set
to determine the signicance level for the sta-
tistical tests.

Month XXXX - Human Factors6
RESULTS
Kinematics and Kinetics
Using Wilks’ Lambda, MANOVA showed
a signicant main eect for cycle block (V =
.05, F40, 1002.91= 30.09, p < .001) and lifting
trial block (V = .63, F40, 1002.91= 3.22, p < .001).
However, the analysis revealed no signi-
cant eect for cycle block × lifting task block
(V = .58, F160, 2273.63= 0.94, p = .681; Table 1).
The univariate tests manifested signicant
dierences between cycle blocks (C1–C5) for
all body and hand load kinematic variables as
well as model predicted Fc (p < .001, Table 2).
According to post hoc analysis, in the middle
of the cycle (C3), CoMA/P signicantly dif-
fered with those of C2 and C4 (p < .001). With
respect to CoM in vertical direction, this vari-
able had signicantly larger values during C1–
C3 than C4 (C1: p < .001, C2: p = .004, C3:
p = .001) and C5 (C1: p < .001, C2: p = .009,
C3: p = .001; Figure 2A). Toward the end of the
lifting cycle, trunk angle decreased monotoni-
cally from C1 to C5 (p < .001). Regarding trunk
velocity, an increase was observed from C1 to
C4 and a decrease from C4 to C5. The dier-
ences between all blocks were statistically sig-
nicant, except between C2 and C5, as well as
C3 and C4 (Figure 2B). Box vertical displace-
ment increased from C1 to C5 with signicant
dierences between all blocks (p < .001). From
C1 to C4, subjects signicantly decreased the
box horizontal distance from body. Moreover,
for C1 to C3, the box velocity increased and
then for the remaining part, it decreased. For
this variable, the dierences between all cycle
blocks were signicant, except between C1
and C5, as well as, C3 and C4 (Figure 2C).
As the subjects extended their trunk in sagittal
plane, Fc values at the L5- S1 disk signicantly
decreased (p < .001), except from C1 to C2 (p =
.093; Figure 3).
Considering lifting trial blocks (T), the
MANOVA analyses conrmed signicant main
eects for CoMA/P (p < .001), ATrunk (p = .004),
XBox- Vertical (p < .001), CoPA/P (p = .014), and Fc
(p = .005; Table 2). A follow- up analysis revealed
signicantly larger RMS of CoM displacement
along the A/P direction in T3 and T4 as compared
to T1 (T3: 28.5%, p = .003; T4: 29.3%, p = .002);
and in T5 as compared to T1 and T2 (T1: 43.7%,
p < .001; 31.1%, p < .001). Generally, mean
trunk sagittal exion angle decreased across the
ve blocks of the lifting trial. More specically,
trunk angle in T5 showed a signicant reduction
compared to T1 (14.4%, p = .048) and T2 (15.8%,
p = .008). Considering the box- related kinematic
variables, XBox- Vertical in T4 and T5 was larger
than those in T1 (T4: 9.7%, p = .009; T5: 12.8%,
p < .001), T2 (T4: 13.3%, p < .001; T5: 16.5%, p
< .001) and T3 (T4: 9.4%, p = .013; T5: 12.5%,
TABLE 1: Results of the MANOVA for Kinematic and Kinetic Variables
Effect Tests Value F Df Error Df Sig. ɳ2
C Pillai’s Trace 1.497 15.965 40.000 1068.000 <0.001 0.374
Wilks’ Lambda 0.050 30.089 40.000 1002.913 <0.001** 0.526
Hotelling’s Trace 9.286 60.937 40.000 1050.000 <0.001 0.699
Roy’s Largest Root 8.299 221.593 10.000 267.000 <0.001 0.892
T Pillai’s Trace 0.400 2.970 40.000 1068.000 <0.001 0.100
Wilks’ Lambda 0.632 3.220 40.000 1002.913 <0.001** 0.108
Hotelling’s Trace 0.530 3.481 40.000 1050.000 <0.001 0.117
Roy’s Largest Root 0.419 11.184 10.000 267.000 <0.001 0.295
C × T Pillai’s Trace 0.503 0.903 160.000 2730.000 0.799 0.050
Wilks’ Lambda 0.578 0.943 160.000 2273.633 0.681 0.053
Hotelling’s Trace 0.601 0.985 160.000 2622.000 0.537 0.057
Roy’s Largest Root 0.281 4.794 16.000 273.000 <0.001 0.219
Note. MANOVA = multivariate analysis of variance; C = cycle blocks; T = lifting trial blocks. *p < .05; **p < .01.

TABLE 2: MANOVA Results (Tests of Between Subjects Effects) for Kinematic and Kinetic Variables
Source Dependent variables Type III sum of squares Df Mean square FSig. ɳ2
CKinematic variables
CoMA/P 1835.112 4 458.778 6.758 <.001** 0.090
CoMVertical 45146.706 4 11286.676 10.442 <.001** 0.133
ATrunk 148148.073 4 37037.018 251.515 <.001** 0.787
VTrunk 30258.991 4 7564.748 41.044 <.001** 0.376
XBox- Vertical 27.175 4 6.794 492.807 <.001** 0.878
XBox- Horizontal 4.400 4 1.100 151.007 <.001** 0.689
VBox 4.669 4 1.167 47.548 <.001** 0.411
Kinetic variables
CoPA/P 188.869 4 47.217 0.347 0.846 0.005
CoPM/L 6.802 4 1.701 0.080 0.988 0.001
Fc 119340863.289 4 29835215.822 174.403 <.001** 0.719
TKinematic variables
CoMA/P 2651.636 4 662.909 9.764 <.001** 0.125
CoMVertical 1749.827 4 437.457 0.405 0.805 0.006
ATrunk 2301.074 4 575.268 3.907 0.004** 0.054
VTrunk 1549.007 4 387.252 2.101 0.081 0.030
XBox- Vertical 0.629 4 0.157 11.399 <.001** 0.143
XBox- Horizontal 0.048 4 0.012 1.631 0.167 0.023
VBox 0.033 4 0.008 0.334 0.855 0.005
Kinetic variables
CoPA/P 1747.121 4 436.780 3.207 0.014* 0.045
CoPM/L 56.572 4 14.143 0.668 0.615 0.010
Fc 2612577.160 4 653144.290 3.818 0.005** 0.053
Note. ATrunk = average trunk flexion angle in the sagittal plane; C = cycle blocks; CoMA/P = position of body center of mass in anterior–posterior direction; CoMVertical =
vertical position of body center of mass; CoPA/P = net center of pressure in the anterior–posterior direction; CoPM/L = net center of pressure in the medial–lateral direction;
T = lifting trial blocks; XBox- Horizontal = average box horizontal displacement from L5- S1; XBox- Vertical = average vertical travel distance of the box; VBox = average box vertical
displacement velocity; VTrunk = average trunk angular velocity. *p < .05; **p < .01.
7

Month XXXX - Human Factors8
p < .001). From the rst block (T1) to the second
(T2), the mean CoPA/P displacement increased,
and for the next blocks (T3), it decreased.
Subsequently, from T3 to T4 an increase was
observed, and then, for the last 20%, it decreased.
However, a further analysis showed a signicant
increase only in T2 (13.8%, p = .033) and T4
(14.5%, p = .021) as compared to T1. Finally, the
model predicted Fc at the L5- S1 disk in T5 was
signicantly lower than that in T2 (9%, p = .009).
Muscle Activity
MANOVAs on the EMG measures revealed
a signicant eect of lifting trial blocks for
SampEn (Wilks’ Lambda = .362, F40, 191.45 =
1.472, p = .046) and MPF (Wilks’ Lambda =
.219, F40, 126.988 = 1.566, p = .032; Table 3).
The MANOVA conrmed a signicant lift-
ing trial main eect on SampEn values of all
trunk muscles as well as MFR and MFL (p <
Figure 2. Mean (SD) values of body and box- related kinematics including (A) body center of mass in anterior–
posterior (CoMA/P) and vertical direction (CoMVertical); (B) trunk angular velocity (Vtrunk) and trunk exion
angle (ATrunk); (C) box velocity in the vertical direction (Vbox), box horizontal and vertical displacements
(XBox- Horizontal and XBox- Vertical). Each variable was separately calculated for ve blocks of lifting cycle (C1–C5)
and ve blocks of lifting duration (T1–T5).

Postural Behavior in rePetitive lifting 9
.05). For these muscles, fatigue led to a reduc-
tion of SampEn in T5 relative to T1 by 5.3%–
14.8% (Table 4). Further analyses revealed
no signicant pairwise dierences for ESLL,
MFR, MFL, LDL, and RAL. However, sample
entropy of ESLR signicantly reduced in T5 as
compared to T1 (p = .025) and T2 (p = .026).
Moreover, SampEn values of LDR and RAR in
T5 were signicantly lower than those in T1 (p
= .034 and p = .033, respectively).
MF was signicantly aected by lifting
blocks for ESLL (p = .014), MFR (p = .021),
MFL (p < .001), LDR (p = .001), and RAL (p
= .039; Table 4, Figure 4). Fatigue led to a sig-
nicant reduction of median frequency in T5
relative to T1 in ESLL (p = .009), MFR (p =
.024), MFL (p < .001), and LDR (p = .001). No
pairwise signicant dierences were found for
abdominal muscles.
DISCUSSION
This study aimed to understand role of the
CNS in dealing with repetitive lifting- induced
fatigue. A detailed analysis of kinematics of
trunk and hand load, trunk muscular activities,
and kinetic parameters were conducted via a set
of metrics during the lifting cycles. Regardless
of the dened lifting trial blocks, similar behav-
iors were found during blocks of lifting cycles.
For instance, CoMA/P, CoMVertical, and VBox
were maximum in the middle of the cycle (C3).
Moreover, toward near the end of the lifting
cycle (from C1 to C4), trunk extension velocity
Figure 3. Changes in center of pressure in anterior–posterior (CoPA/P) and medial–lateral (CoPM/L) directions
and the L5- S1 compressive force (Fc) for ve blocks of lifting cycle (C1–C5) over ve distinct parts of lifting
task time (T1–T5).

Month XXXX - Human Factors10
and box vertical displacement increased, while
box horizontal distance from body decreased.
Despite dierent numbers of lifting cycles per-
formed by the participants to perceived exhaus-
tion, similar time- dependent changes were found
over the repetitive lifting task—adaptive behav-
iors that appears to compensate for fatigue. Our
hypothesis that continuing lifting to exhaustion
corresponds to a wide range of variations in trunk
movements and muscular activities was there-
fore conrmed. Repetitive lifting signicantly
aected CoMA/P, ATrunk, XBox- Vertical, regardless of
the cycle phase. Mean body CoM displacement
along the A/P direction increased over the fatigu-
ing lifting task, most likely to provide stability
(Rajachandrakumar et al., 2018). However, par-
ticipants maintained relatively consistent CoM in
vertical direction even in the presence of fatigue.
Moreover, mean trunk exion angle reduced
over time, showing that subjects adopted a more
erected posture to place the box on the shelf. This
result contradicts with the previous studies that
have reported an increase in trunk exion angle
over cyclic lifting (Bonato et al., 2003; Boocock
et al., 2015; van Dieën et al., 1998). However,
these works either did not calculate exion angle
adjusting the dierent stages of lifting cycle or
they only reported the peak exion angle. With
respect to vertical displacement of the box, an
increase was observed at the end of the lifting
trial. A possible reason might be the subjects’
attempt to reach the standing posture to compen-
sate for the fatigue.
As expected, repetitive lifting signicantly
aected EMG activity. It is well documented
that shifts toward lower frequency reect mus-
cle fatigue (Bonato et al., 2003; Boocock et al.,
2015). The signicant decreasing trend of MF
in a similar manner in ESLL, MFR, MFL, LDR,
and RAL veried that the experimental proce-
dure successfully induced fatigue. Boocock et al.
(2015) compared the dierence in the median fre-
quency intercepts of erector spinae pre- and post-
repetitive lifting and found a decrease of about
12% in the young participants’ group (Boocock
et al., 2015). In the study by Bonato et al. (2003),
a frequency- based feature (instantaneous median
frequency [IMDF]) signicantly decreased in
the paravertebral back muscles during the cyclic
lifting task (Bonato et al., 2003). In our data, the
RAL was the only abdominal muscle to be sig-
nicantly aected by lifting trial blocks. Fatigue
TABLE 3: Results of the MANOVA Analysis for Electromyography- Related Variables (Normalized RMS,
Sample Entropy, and Median Frequency)
Effect Variable Tests Value F Df Error Df Sig. ɳ2
T Normalized
RMS
Pillai’s Trace 0.211 0.383 40.00 276.00 1.000 0.053
Wilks’ Lambda 0.791 0.401 40.00 252.12 1.000 0.057
Hotelling’s Trace 0.262 0.422 40.00 258.00 0.999 0.061
Roy’s Largest
Root
0.253 1.743 10.00 69.00 0.089 0.202
Sample
entropy
Pillai’s Trace 0.723 1.170 40.000 212.000 0.239 0.181
Wilks’ Lambda 0.362 1.472 40.000 191.450 0.046*0.224
Hotelling’s Trace 1.538 1.865 40.000 194.000 0.003 0.278
Roy’s Largest
Root
1.387 7.350 10.000 53.000 0.000 0.581
Median
frequency
Pillai’s Trace 0.938 1.102 40.000 144.000 0.332 0.234
Wilks’ Lambda 0.219 1.566 40.000 126.988 0.032* 0.316
Hotelling’s Trace 2.903 2.286 40.000 126.000 0.000 0.421
Roy’s Largest
Root
2.674 9.625 10.000 36.000 0.000 0.728
Note. MANOVA = multivariate analysis of variance; RMS = root mean square. *p < .05 ; **p < .01

Postural Behavior in rePetitive lifting 11
in the extensor muscles may adversely aect their
ability to maintain spinal stability (Sparto et al.,
1997). Hence, our results support the previous
ndings that abdominal activities must increase
to improve trunk stiness and stabilize the spine
(Granata & Orishimo, 2001). Interestingly, a high
rate of reduction in the MF was observed for the
LDR. Previous studies mainly neglected the sub-
stantial function of the LDR/LDL in lifting. Great
muscle fatigue in the LD may be due to the par-
ticipants trying to adjust the shoulder position to
ensure that body CoM remains over the base of
support during trial.
Considering the RMS, no signicant changes
were found with the progress of task eort, a
nding that contradicts with those of some pre-
vious reports (Boocock et al., 2015; Lotz et al.,
2009). Meanwhile, it is known that amplitude
manifestations of fatigue rapidly recover
(Bonato et al., 2003); thus, it is likely that the
rest between lifting and lowering aects our
ability to detect muscle fatigue by this index.
A complexity- based feature (SampEn) was
also investigated for fatigue detection, which
to the best of our knowledge, has not previ-
ously been explored in EMG signals during
dynamic lifting tasks. This entropy- based
method was used due to its low sensitivity to
noise, which makes it a useful tool for analyz-
ing nonstationary signals (Cashaback et al.,
2013; Richman & Moorman, 2000; Zhang &
Zhou, 2012). Mean SampEn during the nal
block was lower than those during the rst
block. This concurs with the current body of
literature that suggests SampEn declines with
the progress of muscle fatigue, which can be
TABLE 4: MANOVA Results (Tests of Between Subjects Effects) for Electromyography- Related Variables
EMG measure
Dependent
variable Type III sum of squares Df Mean square FSig. ɳ2
Sample entropy ESLR 1.351 4 0.338 4.531 0.003** 0.266
ESLL 1.357 4 0.339 3.706 0.010* 0.229
MFR 1.210 4 0.302 3.360 0.016 0.212
MFL 1.446 4 0.361 3.241 0.019* 0.206
LDR 0.577 4 0.144 3.488 0.014* 0.218
LDL 0.565 4 0.141 3.015 0.026* 0.194
RAR 0.301 4 0.075 4.012 0.007** 0.243
RAL 0.231 4 0.058 3.383 0.016* 0.213
EOR 0.590 4 0.147 1.685 0.168 0.119
EOL 0.276 4 0.069 0.685 0.606 0.052
Median frequency ESLR 410.440 4 102.610 1.670 0.175 0.137
ESLL 613.530 4 153.383 3.530 0.014* 0.252
MFR 700.672 4 175.168 3.257 0.021* 0.237
MFL 1146.427 4 286.607 6.508 0.000** 0.383
LDR 2411.580 4 602.895 5.638 0.001** 0.349
LDL 410.462 4 102.616 1.530 0.211 0.127
RAR 7051.934 4 1762.983 0.873 0.488 0.077
RAL 19265.360 4 4816.340 2.787 0.039* 0.210
EOR 15456.762 4 3864.190 1.785 0.150 0.145
EOL 11125.492 4 2781.373 1.640 0.182 0.135
Note. EOL = left external oblique; EOR = right external oblique; ESLL = left erector spinae longissimus; ESLR = right
erector spinae longissimus; LDL = left latissimus dorsi; LDR = right latissimus dorsi; MFL = left multifidus; MFR = right
multifidus; RAL = left rectus abdominis; RAR = right rectus abdominis; MANOVA = multivariate analysis of variance.
*p < .05; **p < .01.

Month XXXX - Human Factors12
regarded as more regular and less complex
responses (Hong et al., 2016; Zhang & Zhou,
2012). Indeed, this nonlinear technique could
dierentiate fatigue in successive blocks
of lifting task. When assessing the eect of
lifting trial blocks, MF values signicantly
decreased for ESLL, MFR, MFL, LDR, and
RAL. However, no signicant eects of lift-
ing trial blocks were found for ESLR, LDL,
and RAR, while the SampEn values of these
muscles were signicantly aected. These
are already well- established linear and non-
linear techniques proven to be eective in
various tasks. However, they have yet to be
implemented in analyzing EMG signals under
highly dynamic conditions. As this is the rst
study of these measures of fatigue during free
dynamic lifting task, no data are available in
the literature for the sake of comparison.
Considering kinetic variables, CoPA/P
showed a uctuating pattern but with a gen-
eral increasing trend over time. This uctua-
tion was signicantly aected by the lifting
trial blocks (T), which may be due to the
subjects’ attempt to maintain a uniform bal-
ance and control body sway over lifting task.
Finally, a main eect of both C and T was
displayed for Fc. In this sense, Fc decreased
over the lifting cycle (C), that is, from the
time instant that the subject pick up the load
to the time that they placed the load on the
shelf. Moreover, the mean model predicated
Fc decreased over the blocks of lifting (T),
which is due to the contribution of only two
kinematic parameters (i.e., load horizontal
distance and trunk angle) in calculating Fc.
Subjects’ adaptive behavior in terms of keep-
ing load closer and exing trunk to smaller
angles is assumed to be adopted to counteract
the fatigue. Meanwhile, according to Granata
and Marras (1995), spinal loads may be under-
estimated if muscle coactivation is neglected.
The model used in the present study or any
model that fails to account for muscles coact-
ivations (such as optimization- driven models)
would predict this decreasing trend since the
Figure 4. Changes in linear and nonlinear muscle- related parameters over ve blocks of lifting trial (T1–T5)
for right and left erector spinae longissimus (ESLR and ESLL), multidus (MFR and MFL), latissimus dorsi
(LDR and LDL), rectus abdominis (RAR and RAL), and external oblique (EOR and EOL). RMS = root mean
square.

Postural Behavior in rePetitive lifting 13
two mentioned parameters decrease by time
as the subjects get exhausted. Considering
the activation of trunk extensors/exors in
the present study, we are not sure if the spinal
loads would actually decrease under such con-
tradicting conditions (i.e., increased muscle
activation and decreased external moments/
trunk angle). In order to have an accurate
estimation of compressive load, future stud-
ies are hence suggested to use EMG- assisted
models, since they are capable of predicting
muscle activation/coactivation by including
muscle fatigue, muscle length, rate of length-
ening/shortening as well as passive tissues
behavior. This would allow for more repre-
sentative estimations of Fc experienced by
individuals.
LIMITATIONS
A limitation of the present study, beyond
the convenience gender- limited sampling,
is the conducting of measurements in a lab-
oratory setting, which makes it difficult to
generalize results to real workstation condi-
tions. Moreover, lifting is usually accompa-
nied with other activities in an asymmetric
manner. In other words, we cannot infer that
the obtained adaptations, resulting from
fatigue in our study, remain identical when
performing lifting activities in real work set-
tings. Hence, the effects of fatigue during
combined manual material handling tasks
should be further explored. Moreover, the
participants were young and healthy with no
history of low back pain, which restricts gen-
eralizability of the results. Furthermore, all
obtained changes were based on acute effects
of fatigue, and whether fatigue in the long
term (e.g., an entire working day or week)
results in similar adaptations requires further
investigations. Finally, the predicted L5- S1
compression loads could be inaccurate as
muscle co- contractions, which contribute
to spinal loads, were ignored in the single-
equivalent muscle model used here. Future
work needs to use EMG- driven models that
may provide more accurate assessments of
spinal loading.
CONCLUSION
The current study quantied biomechani-
cal variables on a cycle- by- cycle basis, which
enabled tracking of postural and muscular
behavior over time. Overall, the ndings sup-
port that alteration in kinematic variables, along
with reduction in the frequency and complex-
ity responses of EMG signals, are indicators
of CNS strategy to control lifting behavior
under dynamic fatiguing condition. A more in-
depth understanding of CNS adaptations may
be useful for providing recommendations for
workstation design, exoskeleton intervention,
or workers training in order to manage muscu-
loskeletal disorders. Further, using nonlinear
dynamical measures to analyze EMG signals
helps quantify fatigue- related changes, which
are dierent from or even contrary to signal
amplitude and frequency outcomes. Due to the
complex nature of EMG signals, it is critical not
to draw conclusions merely by characterizing
time- frequency content.
ACKNOWLEDGMENTS
This work was a joint project, No. 39697,
between School of Public Health and Sports
Medicine Research Center, Neuroscience
Institute, Tehran University of Medical
Sciences. The authors would like to thank
Djavad Mowafaghian Research Center for
Intelligent and all participants for their volun-
tary contribution. We also thank Mr. Hossein
Mahdavi for his assistance in data collection.
KEY POINTS
●Individuals adjust their lifting strategies as a
compensatory behavior to fatigue during repeti-
tive tasks.
●The current study characterized postural behavior
in terms of alterations in the spine kinematics,
kinetics, and trunk muscle activities in ve phases
of symmetric lifting cycles over time.
●Body CoM in anterior–posterior and vertical
directions and box velocity were maximum in the
middle of the lifting cycle.
●CoM and CoP shifts in anterior–posterior axis,
trunk angle, box vertical displacement, and Fc at
the L5- S1 disk were aected by repetitive lifting-
induced fatigue.

Month XXXX - Human Factors14
●Linear and nonlinear analysis of EMG data
demonstrated trunk and abdominal muscle
fatigue in response to the repetitive lifting task.
ORCID iDs
Adel Mazloumi https:// orcid. org/ 0000- 0003-
4877- 7962
Mohamad Sadegh Ghasemi https:// orcid. org/
0000- 0002- 9382- 0029
SUPPLEMENTAL MATERIAL
The online supplemental material is avail-
able with the manuscript on the HF website.
REFERENCES
Ahmad, I., & Kim, J.-Y. (2018). Assessment of whole body and local
muscle fatigue using electromyography and a perceived exertion
scale for squat lifting. International Journal of Environmental
Research and Public Health, 15, 784. https:// doi. org/ 10. 3390/
ijerph15040784
Amann, M., Sidhu, S. K., Weavil, J. C., Mangum, T. S., &
Venturelli, M. (2015). Autonomic responses to exercise: Group
III/IV muscle aerents and fatigue. Autonomic Neuroscience,
188, 19–23. https:// doi. org/ 10. 1016/ j. autneu. 2014. 10. 018
Arjmand, N., Gagnon, D., Plamondon, A., Shirazi-
Adl, A., & Larivière, C. (2010). A comparative study of two
trunk biomechanical models under symmetric and asymmetric
loadings. Journal of Biomechanics, 43, 485–491. https:// doi. org/
10. 1016/ j. jbiomech. 2009. 09. 032
Baniasad, M., Farahmand, F., Arazpour, M., & Zohoor, H. (2019).
Kinematic and electromyography analysis of paraplegic gait with
the assistance of mechanical orthosis and walker. The Journal
of Spinal Cord Medicine, 42, 1–8. https:// doi. org/ 10. 1080/
10790268. 2019. 1585705
Bartlett, R., Wheat, J., & Robins, M. (2007). Is movement variability
important for sports biomechanists? Sports Biomechanics, 6,
224–243. https:// doi. org/ 10. 1080/ 14763140701322994
Bauer, C. M., Rast, F. M., Ernst, M. J., Meichtry, A., Kool, J.,
Rissanen, S. M., Suni, J. H., & Kankaanpää, M. (2017). The eect of
muscle fatigue and low back pain on lumbar movement variability
and complexity. Journal of Electromyography and Kinesiology, 33,
94–102. https:// doi. org/ 10. 1016/ j. jelekin. 2017. 02. 003
Bernardo, F., Martins, R., & Guedes, J. (2018). Muscle fatigue
assessment in manual handling of loads using motion analysis and
accelerometers: A short review [Symposium]. Paper presented at
the Occupational Safety and Hygiene VI: Proceedings of the 6th
International Symposium on Occupation Safety and Hygiene
(SHO 2018), Guimaraes, Portugal.
Bonato, P., Ebenbichler, G. R., Roy, S. H., Lehr, S., Posch, M.,
Kollmitzer, J., & Della Croce, U. (2003). Muscle fatigue and
fatigue- related biomechanical changes during a cyclic lifting
task. Spine, 28, 1810–1820. https:// doi. org/ 10. 1097/ 01. BRS.
0000087500. 70575. 45
Boocock, M. G., Mawston, G. A., & Taylor, S. (2015). Age- related
dierences do aect postural kinematics and joint kinetics during
repetitive lifting. Clinical Biomechanics, 30, 136–143. https://
doi. org/ 10. 1016/ j. clinbiomech. 2014. 12. 010
Brinckmann, P., Biggemann, M., & Hilweg, D. (1988). Fatigue
fracture of human lumbar vertebrae. Clinical Biomechanics, 3
(Suppl 1), i–0. https:// doi. org/ 10. 1016/ S0268- 0033( 88) 80001-9
Cashaback, J. G. A., Clu, T., & Potvin, J. R. (2013). Muscle fatigue
and contraction intensity modulates the complexity of surface
electromyography. Journal of Electromyography and Kinesiology,
23, 78–83. https:// doi. org/ 10. 1016/ j. jelekin. 2012. 08. 004
Cavanaugh, J. T., Guskiewicz, K. M., & Stergiou, N. (2005).
A nonlinear dynamic approach for evaluating postural control.
Sports Medicine, 35, 935–950. https:// doi. org/ 10. 2165/
00007256- 200535110- 00002
Chen, X., Xie, P., Liu, H., Song, Y., & Du, Y. (2016). Local band
spectral entropy based on wavelet packet applied to surface
EMG signals analysis. Entropy, 18, 41. https:// doi. org/ 10. 3390/
e18020041
Chen, Y.-L. (2000). Changes in lifting dynamics after localized arm
fatigue. International Journal of Industrial Ergonomics, 25, 611–
619. https:// doi. org/ 10. 1016/ S0169- 8141( 99) 00048-7
Davis, K. G. (1996). The effect of lifting vs. lowering on spinal
loading [Conference session]. Paper presented at the Proceedings
of the Human Factors and Ergonomics Society Annual Meeting
Davis, R. B., Ounpuu, S., Tyburski, D., & Gage, J. R. (1991). A gait
analysis data collection and reduction technique.
Delgado- Bonal, A., & Marshak, A. (2019). Approximate entropy
and sample entropy: A comprehensive tutorial. Entropy, 21, 541.
https:// doi. org/ 10. 3390/ e21060541
Dolan, P., & Adams, M. (1993). PCSA. Journal of Biomechanics,
26, 513–522.
Dolan, P., & Adams, M. A. (1998). Repetitive lifting tasks fatigue
the back muscles and increase the bending moment acting on the
lumbar spine. Journal of Biomechanics, 31, 713–721. https:// doi.
org/ 10. 1016/ S0021- 9290( 98) 00086-4
Donker, S. F., Roerdink, M., Greven, A. J., & Beek, P. J. (2007).
Regularity of center- of- pressure trajectories depends on the
amount of attention invested in postural control. Experimental
Brain Research, 181, 1–11. https:// doi. org/ 10. 1007/ s00221- 007-
0905-4
Fischer, S. L., Greene, H. P., Hampton, R. H., Cochran, M. G., &
Albert, W. J. (2015). Gender- based dierences in trunk and
shoulder biomechanical changes caused by prolonged repetitive
symmetrical lifting. IIE Transactions on Occupational
Ergonomics and Human Factors, 3, 165–176. https:// doi. org/ 10.
1080/ 21577323. 2015. 1034382
Flint, J., Linneman, T., Pederson, R., & Storstad, M. (2015). EMG
analysis of latissimus dorsi, erector spinae and middle trapezius
muscle activity during spinal rotation: A pilot study. University
of North Dakota.
Fuller, J. R., Fung, J., & Côté, J. N. (2011). Time- dependent
adaptations to posture and movement characteristics during
the development of repetitive reaching induced fatigue.
Experimental Brain Research, 2 11, 133–143. https:// doi. org/ 10.
1007/ s00221- 011- 2661-8
Gagnon, D., Arjmand, N., Plamondon, A., Shirazi- Adl, A., &
Larivière, C. (2011). An improved multi- joint EMG- assisted
optimization approach to estimate joint and muscle forces
in a musculoskeletal model of the lumbar spine. Journal of
Biomechanics, 44, 1521–1529. https:// doi. org/ 10. 1016/ j.
jbiomech. 2011. 03. 002
Gardner- Morse, M., Stokes, I. A., & Laible, J. P. (1995). Role of
muscles in lumbar spine stability in maximum extension eorts.
Journal of Orthopaedic Research, 13, 802–808. https:// doi. org/
10. 1002/ jor. 1100130521
Ghesmaty Sangachin, M., & Cavuoto, L. A. (2016). Obesity- related
changes in prolonged repetitive lifting performance. Applied
Ergonomics, 56, 19–26. https:// doi. org/ 10. 1016/ j. apergo. 2016.
03. 002
Granata, K. P., & Marras, W. S. (1995). An EMG- assisted model
of trunk loading during free- dynamic lifting. Journal of
Biomechanics, 28, 1309–1317. https:// doi. org/ 10. 1016/ 0021-
9290( 95) 00003-Z
Granata, K. P., & Orishimo, K. F. (2001). Response of trunk
muscle coactivation to changes in spinal stability. Journal of
Biomechanics, 34, 1117–1123. https:// doi. org/ 10. 1016/ S0021-
9290( 01) 00081-1
Granata, K. P., Slota, G. P., & Wilson, S. E. (2004). Inuence of
fatigue in neuromuscular control of spinal stability. Human
Factors: The Journal of the Human Factors and Ergonomics
Society, 46, 81–91. https:// doi. org/ 10. 1518/ hfes. 46. 1. 81. 30391

Postural Behavior in rePetitive lifting 15
Haddad, O. (2011). Development and validation of a fatigue-
modied, EMG- assisted biomechanical model of the lumbar
region. Iowa State University.
Haddad, O., & Mirka, G. A. (2013). Trunk muscle fatigue and
its implications in EMG- assisted biomechanical modeling.
International Journal of Industrial Ergonomics, 43, 425–429.
https:// doi. org/ 10. 1016/ j. ergon. 2013. 08. 004
Hardie, R., Haskew, R., Harris, J., & Hughes, G. (2015). The
eects of bag style on muscle activity of the trapezius, erector
spinae and latissimus dorsi during walking in female university
students. Journal of Human Kinetics, 45, 39–47. https:// doi. org/
10. 1515/ hukin- 2015- 0005
Hong, T., Zhang, X., Ma, H., Chen, Y., & Chen, X. (2016). Fatiguing
eects on the multi- scale entropy of surface electromyography in
children with cerebral palsy. Entropy, 18, 177. https:// doi. org/ 10.
3390/ e18050177
Khalaf, T., Karwowski, W., & Sapkota, N. (2015). A nonlinear
dynamics of trunk kinematics during manual lifting tasks. Work,
51, 423–437. https:// doi. org/ 10. 3233/ WOR- 141880
Lake, D. E., Richman, J. S., Grin, M. P., & Moorman, J. R. (2002).
Sample entropy analysis of neonatal heart rate variability.
American Journal of Physiology- Regulatory, Integrative and
Comparative Physiology, 283, R789–R797. https:// doi. org/ 10.
1152/ ajpregu. 00069. 2002
Lotz, C. A., Agnew, M. J., Godwin, A. A., & Stevenson, J. M. (2009).
The eect of an on- body personal lift assist device (PLAD) on
fatigue during a repetitive lifting task. Journal of Electromyography
and Kinesiology, 19, 331–340. https:// doi. org/ 10. 1016/ j. jelekin.
2007. 08. 006
Mawston, G. A., McNair, P. J., & Boocock, M. G. (2007). The
eects of prior warning and lifting- induced fatigue on trunk
muscle and postural responses to sudden loading during manual
handling. Ergonomics, 50, 2157–2170. https:// doi. org/ 10. 1080/
00140130701510139
Mehta, J. P., Lavender, S. A., & Jagacinski, R. J. (2014). Physiological
and biomechanical responses to a prolonged repetitive
asymmetric lifting activity. Ergonomics, 57, 575–588. https://
doi. org/ 10. 1080/ 00140139. 2014. 887788
Merryweather, A. S., Loertscher, M. C., & Bloswick, D. S. (2009).
A revised back compressive force estimation model for
ergonomic evaluation of lifting tasks. Work, 34, 263–272. https://
doi. org/ 10. 3233/ WOR- 2009- 0924
Missenard, O., Mottet, D., & Perrey, S. (2008). The role of
cocontraction in the impairment of movement accuracy with
fatigue. Experimental Brain Research, 185, 151–156. https:// doi.
org/ 10. 1007/ s00221- 007- 1264-x
Monjo, F., Terrier, R., & Forestier, N. (2015). Muscle fatigue as an
investigative tool in motor control: A review with new insights
on internal models and posture- movement coordination. Human
Movement Science, 44, 225–233. https:// doi. org/ 10. 1016/ j.
humov. 2015. 09. 006
Nelson- Wong, E., & Callaghan, J. P. (2010). Repeatability of clinical,
biomechanical, and motor control proles in people with and
without standing- induced low back pain. Rehabilitation Research
and Practice, 2010, 1–9. https:// doi. org/ 10. 1155/ 2010/ 289278
Rajachandrakumar, R., Mann, J., Schinkel- Ivy, A., & Manseld, A.
(2018). Exploring the relationship between stability and variability
of the centre of mass and centre of pressure. Gait & Posture, 63,
254–259. https:// doi. org/ 10. 1016/ j. gaitpost. 2018. 05. 008
Richman, J. S., & Moorman, J. R. (2000). Physiological time-
series analysis using approximate entropy and sample entropy.
American Journal of Physiology- Heart and Circulatory
Physiology, 278, H2039–H2049. https:// doi. org/ 10. 1152/
ajpheart. 2000. 278. 6. H2039
Samadi, S., & Arjmand, N. (2018). A novel stability- based EMG-
assisted optimization method for the spine. Medical Engineering
& Physics, 58, 13–22. https:// doi. org/ 10. 1016/ j. medengphy.
2018. 04. 019
Sedighi, A., & Nussbaum, M. A. (2017). Temporal changes in motor
variability during prolonged lifting/lowering and the inuence of
work experience. Journal of Electromyography and Kinesiology,
37, 61–67. https:// doi. org/ 10. 1016/ j. jelekin. 2017. 09. 001
Sidhu, S. K., Weavil, J. C., Venturelli, M., Garten, R. S.,
Rossman, M. J., Richardson, R. S., Gmelch, B. S., Morgan, D. E.,
& Amann, M. (2014). Spinal μ-opioid receptor- sensitive lower
limb muscle aerents determine corticospinal responsiveness
and promote central fatigue in upper limb muscle. The Journal
of Physiology, 592, 5011–5024. https:// doi. org/ 10. 1113/ jphysiol.
2014. 275438
Sparto, P. J., & Parnianpour, M. (1998). Estimation of trunk
muscle forces and spinal loads during fatiguing repetitive
trunk exertions. Spine, 23, 2563–2573. https:// doi. org/ 10. 1097/
00007632- 199812010- 00011
Sparto, P. J., Parnianpour, M., Reinsel, T. E., & Simon, S. (1997). The
eect of fatigue on multijoint kinematics and load sharing during
a repetitive lifting test. Spine, 22, 2647–2654. https:// doi. org/ 10.
1097/ 00007632- 199711150- 00013
Srinivasan, D., & Mathiassen, S. E. (2012). Motor variability in
occupational health and performance. Clinical Biomechanics,
27, 979–993. https:// doi. org/ 10. 1016/ j. clinbiomech. 2012. 08. 007
Stergiou, N., & Decker, L. M. (2011). Human movement variability,
nonlinear dynamics, and pathology: Is there a connection?
Human Movement Science, 30, 869–888. https:// doi. org/ 10. 1016/
j. humov. 2011. 06. 002
Tanaka, H., Monahan, K. D., & Seals, D. R. (2001). Age- predicted
maximal heart rate revisited. Journal of the American College of
Cardiology, 37, 153–156. https:// doi. org/ 10. 1016/ S0735- 1097( 00)
01054-8
Trask, C., Teschke, K., Morrison, J., Johnson, P., Village, J., &
Koehoorn, M. (2010). EMG estimated mean, peak, and cumulative
spinal compression of workers in ve heavy industries.
International Journal of Industrial Ergonomics, 40, 448–454.
https:// doi. org/ 10. 1016/ j. ergon. 2010. 02. 006
van Dieën, J. H., Selen, L. P. J., & Cholewicki, J. (2003). Trunk
muscle activation in low- back pain patients, an analysis of the
literature. Journal of Electromyography and Kinesiology, 13,
333–351. https:// doi. org/ 10. 1016/ S1050- 6411( 03) 00041-5
van Dieën, J. H., van der Burg, P., Raaijmakers, T. A., &
Toussaint, H. M. (1998). Eects of repetitive lifting on kinematics:
Inadequate anticipatory control or adaptive changes? Journal
of Motor Behavior, 30, 20–32. https:// doi. org/ 10. 1080/
00222899809601319
VICON- Documentation. (2020). Upper body modeling with plug- in
gait. 1 July 2020, from. https:// docs. vicon. com/ display/ Nexus26/
Upper+ body+ modeling+ with+ Plug- in+ Gait# Uppe rbod ymod elin
gwit hPlug- inGait- MarkerPlacementUpper
Wang, L., Wang, Y., Ma, A., Ma, G., Ye, Y., Li, R., & Lu, T. (2018).
A comparative study of EMG indices in muscle fatigue evaluation
based on grey relational analysis during all- out cycling exercise.
BioMed Research International, 2018, 1–8. https:// doi. org/ 10.
1155/ 2018/ 9341215
Yin, P., Yang, L., Wang, C., & Qu, S. (2019). Eects of wearable
power assist device on low back fatigue during repetitive lifting
tasks. Clinical Biomechanics, 70, 59–65. https:// doi. org/ 10. 1016/
j. clinbiomech. 2019. 07. 023
Zhang, X., & Zhou, P. (2012). Sample entropy analysis of surface
EMG for improved muscle activity onset detection against
spurious background spikes. Journal of Electromyography and
Kinesiology, 22, 901–907. https:// doi. org/ 10. 1016/ j. jelekin. 2012.
06. 005
Zeinab Kazemi is currently a PhD candidate in ergo-
nomics, in the Department of Occupational Health
Engineering, Tehran University of Medical Sciences,
Iran. Her research interest areas are occupational ergo-
nomics and biomechanics.
Adel Mazloumi is a professor of ergonomics at
Tehran University of Medical Sciences. He earned
his PhD in occupational safety and ergonomics
from University of Occupational and Environmental
Health (UOEH), Japan. He is interested in phys-
ical ergonomics, ergonomics in design, and
macroeconomics.

Month XXXX - Human Factors16
Navid Arjmand earned his PhD in biomechanical
engineering from École Polytechnique de Montréal,
Canada. He is currently an associate professor at
the Department of Mechanical Engineering at Sharif
University of Technology, Tehran, Iran.
Ahmadreza Keihani is a PhD candidate in biomedical
engineering at Tehran University of Medical Sciences,
Iran. His research interest areas are chaos and nonlin-
ear dynamics, biological data science, brain computer
interface, and neuroimaging.
Zanyar Karimi is a PhD candidate in ergonomics at
Tehran University of Medical Sciences. He received
his master’s degree in ergonomics from Urmia
University of Medical Sciences in 2015.
Mohamad Sadegh Ghasemi is an associate professor
at the Department of Basic Rehabilitation Sciences &
Department of Ergonomics, Iran University of Medical
Sciences, Iran. He received his PhD in clinical biome-
chanics from Glasgow Caledonian University, UK, in
1998.
Ramin Kordi is a professor at the Sport Medicine
Research Center, Neuroscience Institute, Tehran
University of Medical Sciences. He received his PhD
in sports medicine from University of Nottingham,
UK, in 2006.
Date received: July 17, 2020
Date accepted: December 1, 2020