Lower Limb Muscle Co-Activation Maps in Single and Team Lifting at Different Risk Levels

Abstract

The central nervous system uses muscle co-activation for body coordination, effector movement control, and joint stabilization. However, co-activation increases compression and shear stresses on the joints. Lifting activity is one of the leading causes of work-related musculoskeletal problems worldwide, and it has been shown that when the risk level rises, lifting enhances trunk muscle co-activation at the L5/S1 level. This study aims to investigate the co-activation of lower limb muscles during liftings at various risk levels and lifting types (one-person and vs. two-person team lifting), to understand how the central nervous system governs lower limb rigidity during these tasks. The surface electromyographic signal of thirteen healthy volunteers (seven males and six females, age range: 29–48 years) was obtained over the trunk and right lower limb muscles while lifting in the sagittal plane. Then co-activation was computed according to different approaches: global, full leg, flexor, extensor, and rostro-caudal. The statistical analysis revealed a significant increase in the risk level and a decrease in the two-person on the mean and/or maximum of the co-activation in almost all the approaches. Overall, our findings imply that the central nervous system streamlines the motor regulation of lifting by increasing or reducing whole-limb rigidity within a distinct global, extensor, and rostro-caudal co-activation scheme, depending on the risk level/lifting type.

Full text

Citation: Chini, G.; Varrecchia, T.;
Serrao, M.; Ranavolo, A. Lower Limb
Muscle Co-Activation Maps in Single
and Team Lifting at Different Risk
Levels. Appl. Sci. 2024,14, 4635.
https://doi.org/10.3390/
app14114635
Academic Editor: Gongbing Shan
Received: 10 April 2024
Revised: 23 May 2024
Accepted: 24 May 2024
Published: 28 May 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
applied
sciences
Article
Lower Limb Muscle Co-Activation Maps in Single and Team
Lifting at Different Risk Levels
Giorgia Chini 1, Tiwana Varrecchia 1,*, Mariano Serrao 2and Alberto Ranavolo 1
1
Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Via Fontana
Candida, 1, Monte Porzio Catone, 00078 Rome, Italy; g.chini@inail.it (G.C.); a.ranavolo@inail.it (A.R.)
2Department of Medical and Surgical Sciences and Biotechnologies, Sapienza University of Rome,
Polo Pontino, Via Franco Faggiana 1668, 04100 Latina, Italy; mariano.serrao@uniroma1.it
*Correspondence: t.varrecchia@inail.it
Abstract: The central nervous system uses muscle co-activation for body coordination, effector
movement control, and joint stabilization. However, co-activation increases compression and shear
stresses on the joints. Lifting activity is one of the leading causes of work-related musculoskeletal
problems worldwide, and it has been shown that when the risk level rises, lifting enhances trunk
muscle co-activation at the L5/S1 level. This study aims to investigate the co-activation of lower limb
muscles during liftings at various risk levels and lifting types (one-person and vs. two-person team
lifting), to understand how the central nervous system governs lower limb rigidity during these tasks.
The surface electromyographic signal of thirteen healthy volunteers (seven males and six females,
age range: 29–48 years) was obtained over the trunk and right lower limb muscles while lifting in the
sagittal plane. Then co-activation was computed according to different approaches: global, full leg,
flexor, extensor, and rostro-caudal. The statistical analysis revealed a significant increase in the risk
level and a decrease in the two-person on the mean and/or maximum of the co-activation in almost
all the approaches. Overall, our findings imply that the central nervous system streamlines the motor
regulation of lifting by increasing or reducing whole-limb rigidity within a distinct global, extensor,
and rostro-caudal co-activation scheme, depending on the risk level/lifting type.
Keywords: two-person team lifting; bipolar sEMG; co-contraction; manual material handling; lower
limb; spinal output
1. Introduction
Lifting is one of the primary causes of work-related musculoskeletal diseases globally,
affecting a considerable number of industrial workers and manual material handlers [
1
–
4
].
To prevent work-related musculoskeletal diseases, it is crucial to adopt effective er-
gonomic interventions designed on an accurate and precise estimate of the biomechanical
risk level also by using approaches based on wearable sensor networks and specific algo-
rithms and indexes [
5
]. These approaches allow us to estimate the risk levels during the
execution, among the other manual material handling activities, of lifting tasks performed
in the team by more than one person or performed with the aid of exoskeletons and col-
laborative robots [
5
–
9
]. The latter would not be assessable with methods listed within the
international ergonomic standards [10–12].
Since during lifting heavy loads, the spine is the most affected body district, the scien-
tific literature shows that the mainly used indexes are based on the trunk behavior in terms of
kinematics [
13
–
16
], forces at the L5/S1 level [
17
–
19
] and surface electromyography [
6
,
20
–
22
].
On the other hand, although a correct execution of the lifting by the lower limbs can
allow the trunk to stoop less reducing net moments, muscle forces, and internal spinal
load [
23
], lower limbs have received little consideration to date and few studies are available
in the literature [
24
–
28
]. Furthermore, lower limb work-related musculoskeletal diseases
Appl. Sci. 2024,14, 4635. https://doi.org/10.3390/app14114635 https://www.mdpi.com/journal/applsci

Appl. Sci. 2024,14, 4635 2 of 12
are still present and widespread [
29
], (e.g., it is possible to see the incidence and prevalence
of work-related musculoskeletal diseases in Italy at the link https://bancadaticsa.inail.it,
accessed on 8 April 2024). Finally, analyzing the behavior of some indices associated with
the lower limbs would be relevant to enrich the instrumental approaches with the further
chance to train high-performance artificial neural networks [30,31].
Moreover, with this goal, it would be useful to investigate the behavior of lower limb
muscle co-activation to understand how the central nervous system (CNS) modulates joint
stiffness by regulating the duration and intensity of concurrent activity of a pair or group
of muscles [22,32–34].
Muscle co-activation is thought to maintain effector-level control (low dimensional),
removing the need for individual muscle coordination control (high dimensional) [
32
].
However, it can be counterproductive, as it generates additional compression and shear
forces on the joint, that may lead to injury [
19
,
22
,
35
–
37
]. Lifting has been demonstrated to
enhance the co-activation of the trunk muscles, causing moments that do not add to the
required net trunk moment [6,22,31].
Lower limb co-activations could be calculated globally by considering all the muscles,
but also at the level of different spinal segments by mapping the simultaneous activity of
various muscles during lifting onto the anatomical rostro-caudal position of motor neuron
populations in the human spinal cord-derived from previously published studies during
walking [
38
–
43
]. Furthermore, co-activation could be calculated by considering either flexor
or extensor muscles separately [
43
]. Lifting usually requires the need to extend ankles,
knees, and hips through the action of the muscles that generate internal extensor moments.
On the contrary, it is functionally important that the flexor muscles do not generate an
opposing moment and this, among others, can occur when the motor task becomes more
demanding. Hence, both extensors and flexors approaches would allow us to consider
indices for biomechanical risk assessment starting from a simplified sensors setup.
For all these reasons, there is a need to better study the behavior of the lower limbs
during the execution of heavy lifting activities in an occupational context. Indeed, a
correct motor execution of the lower limbs during lifting allows for less overload of the
spine [
44
–
46
]. Furthermore, global co-activation of lower limb muscles could be used as an
index in instrumental risk assessment methods and to train machine learning algorithms for
automated risk level estimation. The two “rostro-caudal” and “flexor-extensor” approaches,
in addition to representing an in-depth analysis of the mechanisms adopted by the CNS,
would allow the calculation of the co-activation index starting from a simplified sensor
setup, which is always desirable in the workplace.
We proposed a novel approach to studying time-varying multi-muscle co-activation
function (TMCf ), which is a good indicator of the CNS’s overall strategy for modulating
the muscle co-activation during locomotion [
43
] and lifting [
6
,
22
]. This approach gives
an alternative viewpoint on the spatiotemporal motor control of the trunk and/or lower
limbs, highlighting how trunk and/or lower limb muscles are concurrently co-activated
to increase whole-limb stiffness, regardless of single-joint antagonist muscles or modular
activation of a group of muscles [47].
We hypothesized that the lower limb muscle co-activation increases when lifting with
a higher LI is performed and decreases in team lifting compared to that of one-person
lifting. Furthermore, we hypothesize that, due to the nature of the motor task, the co-
activation of the extensor muscles increases with the level of risk and that it varies across
the rostro-caudal recruitment map.
The current study aimed to investigate the concurrent contractions of multiple lower
limb muscles during liftings at various risk levels and lifting types (one-person vs. two-
person team lifting) to gain insight into how the CNS manages lower limb rigidity and to
include muscle co-coactivation indexes within instrumental-based tool risk assessment.

Appl. Sci. 2024,14, 4635 3 of 12
2. Materials and Methods
In this work, the experimental approach mentioned in ref. [
6
] and briefly summarized
below was used.
2.1. Experimental Procedures
Each subject lifted a crate in the sagittal plane (without trunk rotation) with both hands
at three different risk levels determined according to the NIOSH method alone and in team
with another subject, as detailed in ref. [6], Figure 1.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 3 of 13
person team lifting) to gain insight into how the CNS manages lower limb rigidity and to
include muscle co-coactivation indexes within instrumental-based tool risk assessment.
2. Materials and Methods
In this work, the experimental approach mentioned in ref. [6] and briefly summa-
rized below was used.
2.1. Experimental Procedures
Each subject lifted a crate in the sagial plane (without trunk rotation) with both
hands at three different risk levels determined according to the NIOSH method alone and
in team with another subject, as detailed in ref. [6], Figure 1.
Figure 1. This figure displays the experimental setup: (A) one-person and (B) two-person-team lift-
ing. The picture depicts how the load’s horizontal distance (H), and vertical displacement (D) were
controlled to meet the risk levels identified according to the NIOSH method, (lifting index, LI).
Table 1 shows the values of the experimental setup parameters that contribute to de-
termining the risk level given by the NIOSH lifting index (LI) both in one- and two-person
team lifting.
Table 1. This table reports for each lifting task the values of the load weight (L), the horizontal (H)
and vertical (V) locations, the vertical travel distance (D), the asymmetry angle (A), the lifting fre-
quency (F) and the hand-to-object coupling (C) and the corresponding values of the multipliers and
recommended weight limit (RWL) for one-person and two-person team lifting (RWL and RWLT,
respectively). LC was defined as 23 kg in RNLE. The value of LI for one-person lifting (LI) and for
two-person team lifting (LIT) were also reported.
Task
LC (kg)
H (cm)
HM V
(cm)
VM D
(cm)
DM A (°)
AM
F
(lift/min)
FM
C CM
L (kg)
RWL
RWLT
LI LIT
A 23 63 ~0.40
10 ~0.81
40 ~0.93
0 1 ≤2 1 good
1 7 6.85
4.59 1.02
0.51
B 23 60 ~0.42
31 ~0.868
54 ~0.90
0 1 ≤2 1 good
1 15 7.51
5.03 2.00
0.99
C 23 60 ~0.42
10 ~0.805
100
~0.87
0 1 ≤2 1 good
1 20 6.67
4.47 3.00
1.49
To ensure that all NIOSH parameters were effectively controlled, and risk levels were
correct, the positions of the feet for the various tasks, as well as the positioning of the box,
were marked on the ground with tape so that the horizontal distance (H) between the
center of the malleoli and the center of the load was actually (and for all subjects) 60 and
63 cm, for tasks A, B, and C, respectively. Furthermore, the maximum height to which the
weight had to be lifted was indicated with a three-level rod, resulting in vertical displace-
ments (D) of 40, 54, and 100 cm for jobs A, B, and C, respectively. Finally, the initial height
Figure 1. This figure displays the experimental setup: (A) one-person and (B) two-person-team
lifting. The picture depicts how the load’s horizontal distance (H), and vertical displacement (D)
were controlled to meet the risk levels identified according to the NIOSH method, (lifting index, LI).
Table 1shows the values of the experimental setup parameters that contribute to
determining the risk level given by the NIOSH lifting index (LI) both in one- and two-
person team lifting.
Table 1. This table reports for each lifting task the values of the load weight (L), the horizontal
(H) and vertical (V) locations, the vertical travel distance (D), the asymmetry angle (A), the lifting
frequency (F) and the hand-to-object coupling (C) and the corresponding values of the multipliers
and recommended weight limit (RWL) for one-person and two-person team lifting (RWL and RWLT,
respectively). LC was defined as 23 kg in RNLE. The value of LI for one-person lifting (LI) and for
two-person team lifting (LIT) were also reported.
Task LC
(kg)
H
(cm) HM V
(cm) VM D
(cm) DM A (◦) AM F
(lift/min)
FM C CM L
(kg)
RWL RWL
TLI
LI
T
A 23 63
~0.40
10
~0.81
40
~0.93
0 1 ≤2 1
good
1 7 6.85 4.59 1.02
0.51
B 23 60
~0.42
31
~0.868
54
~0.90
0 1 ≤2 1
good
1 15 7.51 5.03 2.00
0.99
C 23 60
~0.42
10
~0.805
100
~0.87
0 1 ≤2 1
good
1 20 6.67 4.47 3.00
1.49
To ensure that all NIOSH parameters were effectively controlled, and risk levels were
correct, the positions of the feet for the various tasks, as well as the positioning of the box,
were marked on the ground with tape so that the horizontal distance (H) between the center
of the malleoli and the center of the load was actually (and for all subjects) 60 and 63 cm,
for tasks A, B, and C, respectively. Furthermore, the maximum height to which the weight
had to be lifted was indicated with a three-level rod, resulting in vertical displacements (D)
of 40, 54, and 100 cm for jobs A, B, and C, respectively. Finally, the initial height of the load
center (V) from the ground was controlled using a support surface to ensure that it was
exactly 10 cm for tasks A and C and 31 cm for task B.
Each participant performed 3 repetitions of each risk condition for both one- and
two-person team lifting, so to have a total of 18 trials. The different liftings were executed at
random across the three risk conditions and one- and two-person team lifting to avoid bias.

Appl. Sci. 2024,14, 4635 4 of 12
Before starting the measurements, a global reference system was defined by executing
a calibration procedure according to [
48
] with a mean spatial accuracy of 0.2 mm. The
movement of one spherical marker covered with aluminum powder reflective material
was detected at a sampling frequency of 340 Hz by using an optoelectronic motion analysis
system (SMART-DX 6000 System, BTS, Milan, Italy) with eight infrared cameras. The
marker was placed over the right anterior vertex of the load (a plastic crate).
Surface electromyography (sEMG) has been recorded with a 16-channel Wi-Fi trans-
mission surface electromyograph (Mini Wave Infinity, Cometa, Milan, Italy) with a 2000 Hz
sampling frequency. After skin preparation, bipolar electrodes were placed according
to the Atlas of Muscle Innervation Zones [
49
] and the European Recommendations for
Surface Electro-myography [
50
], bilaterally over the rectus abdominis superior and erector
spinae longissimus muscles and over the following right lower limb’s muscles: peroneus
longus, soleus, gastrocnemius medialis and lateralis, tibialis anterior, biceps femoris, semi-
tendinosus, tensor fascia latae, vastus medialis and lateralis, rectus femoris, and gluteus
medius [51,52]. Kinematic, and sEMG data were recorded simultaneously.
2.2. Data Analysis
The raw sEMG data have been processed as in [
6
] with a with a self-written Matalb
(version 2018b 9.5.0.1178774, MathWorks, Natick, 193 MA, USA) script. Briefly, the raw
sEMG signals has been filtered and we determined the envelope. Then, for each muscle,
the sEMG envelope was amplitude-normalized to the maximum of each corresponding
muscle among all the trials [50,53,54].
2.3. Cycle Definition and Time Normalization
We determined the start and stop of each lifting with the same procedure already
detailed in ref. [
22
] by analyzing the vertical displacement and velocity of one of the four
markers placed on the load. Then, to be able to compare different lifting cycles, we time-
normalized all the liftings with a polynomial procedure to the same number of samples
(201 samples), as in ref. [22].
2.4. Global, Full Leg, Flexor, Extensor, and Rostro-Caudal Co-Activation
The time-varying multi-muscle co-activation function (TMCf ) was used to calculate
the simultaneous activation of the trunk and lower limb muscles [
6
,
22
,
43
] according to the
following formula:
T MC f (d(i),i)=1−1
1+e−12(d(i)−0.5).(∑M
m=1EMGm(i)/M)2
maxm=1...M[EMGm(i)]
where Mis the number of muscles considered, EMG
m
(i) is the sEMG sample value of the
m-th muscle at instant i, and d(i) is the mean of the differences between each pair of sEMG
values at instant i:
d(i)= ∑M−1
m=1∑M
n=m+1|EMGm(i)−E MGn(i)|
Lx(M!/(2!(M−2)!)) !
Lis the length of the sEMG signal (201 samples in this case),
M
!
/(2!(M−2)!)
is the total
number of possible differences between each pair of EMG
m
(i). This function’s values ranged
from 0 to 100%.
All the sixteen acquired muscles were inserted in the calculation of the TMCf to assess
global co-activation (TMCf
glob
). Moreover, the co-activation of all the lower limb muscles
(TMCf
full_leg
), extensor (TMCf
ext
), flexor (TMCf
flex
) muscles separately, and according to the
rostro-caudal organization (TMCf
L3
;TMCf
L4
;TMCf
L5
;TMCf
S1
;TMCf
S2
) [
40
,
42
,
43
,
47
,
55
,
56
]
was assessed using subgroups of muscles (see Table 2). Muscles were considered as flexors
or extensors based on their concentric function in the sagittal plane [
55
]. The biarticular
muscles were considered as flexors or extensors based on their proximal function [57].

Appl. Sci. 2024,14, 4635 5 of 12
Table 2. Each dot in the table indicates muscles included in the time-varying co-activation (TMCf )
function for each muscle co-activation investigated: global, full leg, extensor, flexor, and rostro-caudal
organization. The smallest dots indicate a halved weight (amplitude of muscle activity multiplied by
0.5) for that specific muscle in the TMCf function.
Muscles Global Full Leg Extensor Flexor L3 L4 L5 S1 S2
Rectus Abdominis Superior •
Erector Spinae Longissimus •
Glutes Medius • • • • • •
Rectus Femoris • • • • •
Vastus Lateralis • • • •
Vastus Medialis • • • • •
Tensor Fasciae Late • • • • • • •
Semitendinosus ••• ••••
Biceps Femoris ••• • • •
Tibialis Anterior • • • • • •
Gastrocnemio Medialis • • • • •
Gastrocnemio Lateralis • • • • •
Soleus • • •• •
Peroneus • • •• •
2.5. Co-Activation Parameters
Within each lifting, the following parameters were calculated for each TMCf: (i) the
synthetic co-activation index (CI
glob
;CI
full_leg
;CI
ext
;CI
flex
;CI
L3
;CI
L4
;CI
L5
;CI
S1
;CI
S2
), which
is computed as the mean value of each TMCf curve, representing the average of the co-
activation level over the lifting cycle, [% co-activation]; (ii) the maximum value of each
TMCf curve (Max
glob
;Max
full_leg
;Max
ext
;Max
flex
;Max
L3
;Max
L4
;Max
L5
;Max
S1
;Max
S2
), as
a punctual index, that returns instantaneous information about the peak at which each
co-activation arrives within each lifting cycle [% co-activation].
2.6. Statistical Analysis
Statistical analyses have been performed using SPSS 20.0 (IBM SPSS) software. For
each subject, we averaged the data from all the trials at the same risk level and lifting type
(i.e., one-person or two-person team lifting). Firstly, we checked if the data were normally
distributed with the Shapiro–Wilk normality test, then we investigated if there was effect
of the risk level (low, Task A, medium, Task B, or high, Task C, determined according to the
NIOSH method) or of the lifting type by executing a two-way repeated measure ANOVA.
Finally, we performed a post hoc analysis with Bonferroni’s correction, if the repeated
measure ANOVA test revealed a main effect. In all the cases, if the pvalues were lower
than 0.05, the difference was considered statistically significant.
3. Results
3.1. Subjects
The study included thirteen participants (seven males, age range: 29–43 years, mean
age = 40.29
±
5.09 years, height = 1.71
±
0.06 m, weight = 68.93
±
6.35 kg, body mass
index [BMI] = 23.41
±
0.91 kg/m
2
; and six females, age range: 29–48 years, mean age mean
age = 32.83
±
8.40 years, height = 1.61
±
0.04 m, weight = 55.83
±
9.20 kg, body mass
index [BMI] = 21.38
±
2.76 kg/m
2
). During the current study, all the enrolled subjects
were not taking part in any clinical drug trials and had no history of upper and lower limb
and trunk surgery, orthopedic or neurological diseases, vestibular system disorders, or
back pain. Other exclusion criteria included inability to give informed written consent,
orthopedic diseases, metabolic or inflammatory conditions, visual impairments or back
pain, current pregnancy, current pharmacological treatment and/or infections that may
influence the functional status during working posture and movement assessment, and
obesity or overweight. Participants provided written informed consent after receiving a
thorough explanation of the experimental procedure and prior to participating in the study,
which adhered to the Helsinki Declaration and was approved by the local ethics committee

Appl. Sci. 2024,14, 4635 6 of 12
(N. 0078009/2021). To prevent bias, neither any information about the expected outcomes
was given.
3.2. TMCf Maps
As in [
43
], we reconstructed the spinal maps of the co-activation in the lumbosacral
enlargement by mapping the TMCf profiles onto the rostro-caudal location of the mo-
toneuron pools. Figure 2shows the mean of the segmental TMCf at three risk levels for
each spinal segment over the lifting cycles performed by a one-person team and
Figure 3
illustrates it over the lifting cycles executed by a two-person team. The maps show dif-
ferent co-activation loci at each lumbar segment (especially at the L3 level) at the be-
ginning of the lifting both in one-person and two-person team lifting at all three risk
levels (
Figures 2and 3
) and by an increased co-activation from the beginning to 80% of
the lifting cycle at the S2 sacral segment both in one-person and two-person team lifting
(
Figures 2and 3
).
Figures 2and 3
show that under medium risk conditions (LI = 2), the
TMCf at level S2 is around 15% from the beginning to 80% of the cycle in one-person team
lifting, whereas it only remains at this level at the very beginning of the cycle (from 0% to
10% of the lifting cycle) in two-person team lifting. Under high-risk conditions (LI = 3), the
effect is even more pronounced; in Figure 2in one-person liftings, the TMCf at segment S2
is between 20 and 30%, whereas in two-person liftings, it is around 17% from the beginning
to 80% of the cycle.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 6 of 13
The study included thirteen participants (seven males, age range: 29–43 years, mean
age = 40.29 ± 5.09 years, height = 1.71 ± 0.06 m, weight = 68.93 ± 6.35 kg, body mass index
[BMI] = 23.41 ± 0.91 kg/m2; and six females, age range: 29–48 years, mean age mean age =
32.83 ± 8.40 years, height = 1.61 ± 0.04 m, weight = 55.83 ± 9.20 kg, body mass index [BMI]
= 21.38 ± 2.76 kg/m2). During the current study, all the enrolled subjects were not taking
part in any clinical drug trials and had no history of upper and lower limb and trunk
surgery, orthopedic or neurological diseases, vestibular system disorders, or back pain.
Other exclusion criteria included inability to give informed wrien consent, orthopedic
diseases, metabolic or inflammatory conditions, visual impairments or back pain, current
pregnancy, current pharmacological treatment and/or infections that may influence the
functional status during working posture and movement assessment, and obesity or over-
weight. Participants provided wrien informed consent after receiving a thorough expla-
nation of the experimental procedure and prior to participating in the study, which ad-
hered to the Helsinki Declaration and was approved by the local ethics commiee (N.
0078009/2021). To prevent bias, neither any information about the expected outcomes was
given.
3.2. TMCf Maps
As in [43], we reconstructed the spinal maps of the co-activation in the lumbosacral
enlargement by mapping the TMCf profiles onto the rostro-caudal location of the moto-
neuron pools. Figure 2 shows the mean of the segmental TMCf at three risk levels for each
spinal segment over the lifting cycles performed by a one-person team and Figure 3 illus-
trates it over the lifting cycles executed by a two-person team. The maps show different
co-activation loci at each lumbar segment (especially at the L3 level) at the beginning of
the lifting both in one-person and two-person team lifting at all three risk levels (Figures
2 and 3) and by an increased co-activation from the beginning to 80% of the lifting cycle
at the S2 sacral segment both in one-person and two-person team lifting (Figures 2 and 3).
Figures 2 and 3 show that under medium risk conditions (LI = 2), the TMCf at level S2 is
around 15% from the beginning to 80% of the cycle in one-person team lifting, whereas it
only remains at this level at the very beginning of the cycle (from 0% to 10% of the lifting
cycle) in two-person team lifting. Under high-risk conditions (LI = 3), the effect is even
more pronounced; in Figure 2 in one-person liftings, the TMCf at segment S2 is between
20 and 30%, whereas in two-person liftings, it is around 17% from the beginning to 80%
of the cycle.
Figure 2. Spatiotemporal maps of the co-activation of the muscles innervated by the lumbosacral
enlargement in one-person team lifting at low (LI = 1, green), medium (LI = 2, yellow), and high (LI
= 3, red) risk levels. The top panels show the output paern of each segment (mean ± SD) in a color
scale. The lowest plots show the co-activation (TMCf averaged across participants, mean ± SD) as a
function of the lifting cycle and spinal segment level (L3–S2).
Figure 2. Spatiotemporal maps of the co-activation of the muscles innervated by the lumbosacral
enlargement in one-person team lifting at low (LI = 1, green), medium (LI = 2, yellow), and high
(
LI = 3
, red) risk levels. The top panels show the output pattern of each segment (mean
±
SD) in a
color scale. The lowest plots show the co-activation (TMCf averaged across participants, mean
±
SD)
as a function of the lifting cycle and spinal segment level (L3–S2).
Appl. Sci. 2024, 14, x FOR PEER REVIEW 7 of 13
Figure 3. Spatiotemporal maps of the co-activation of the muscles innervated by the lumbosacral
enlargement in two-person team lifting at low (LI = 1, green), medium (LI = 2, yellow), and high (LI
= 3, red) risk levels. The top panels show the output paern of each segment (mean ± SD) in a color
scale. The lowest plots show the co-activation (TMCf averaged across participants, mean ± SD) as a
function of the lifting cycle and spinal segment level (L3–S2).
3.3. TMCf Indices
The two-way repeated measures ANOVA showed a significant main effect of the lift-
ing type on CIglob, CIfull_leg, CIext, CIL3, CIL4, CIL5, CIS1, CIS2 (Table 3), Maxglob, Maxfull_leg, Maxext,
MaxL3, MaxL4 , MaxL5, MaxS1, MaxS2 (Table 4) and of the LI on CIglob, CIfull _leg, CIext, CIL4, CIL5,
CIS1, CIS2 (Table 3), Maxglob, Maxfull_leg, Maxext, MaxL3, MaxL4, MaxL5, MaxS1, MaxS2 (Table 4).
Table 3. The table shows the results of the two-way repeated measures ANOVA (F, dF, and p values)
on the co-activation index (CI) calculated for each TMCf. Bold indicates significant differences.
Lifting Type Risk Level Lifting Type Risk Level
F p F p F p
CIglob F(1,12) = 60.402 <0.001 F(2,24) = 71.477 <0.001 F(2,24) = 0.752 0.482
CIfull_leg F(1, 12) = 58.307 <0.001 F(2,24) = 72.454 <0.001 F(2,24) = 1.138 0.337
CIext F(1,12) = 82.544 <0.001 F(2,24) = 75.031 <0.001 F(2,24) = 3.975 0.032
CIflex F(1,12) = 2.491 0.141 F(2,24) = 0.847 0.441 F(2,24) = 0.328 0.723
CIL3 F(1,12) = 5.310 0.040 F(2,24) = 2.404 0.112 F(2,24) = 0.082 0.921
CIL4 F(1,12) = 7.219 0.020 F(2,24) = 24.425 <0.001 F(2,24) = 0.311 0.735
CIL5 F(1,12) = 58.770 <0.001 F(2,24) = 109.746 <0.001 F(2,24) = 1.938 0.166
CIS1 F(1,12) = 83.003 <0.001 F(2,24) = 112.766 <0.001 F(2,24) = 2.105 0.144
CIS2 F(1,12) = 106.360
<0.001 F(1.322,15.861) = 103.238
<0.001 F(2,24) = 3.558 0.044
Table 4. This table shows the results of the two-way repeated measures ANOVA (F, df, and p values)
on the maximum value (Max) calculated for each TMCf. Bold indicates significant differences.
Lifting Type Risk Level Lifting Type Risk Level
F p F p F p
Max
glob F(1,12) = 14.974 0.002 F(2,24) = 22.637 <0.001 F(2,24) = 0.456 0.639
Max
full_leg F(1,12) = 6.737 0.023 F(2,24) = 30.956 <0.001 F(1.253,15.034) = 0.781 0.469
Max
ext F(1,12) = 17.749 0.001 F(2,24) = 63.992 <0.001 F(2,24) = 2.906 0.074
Max
flex F(1,12) = 0.643 0.438 F(2,24) = 1.925 0.168 F(1.316,15.794) = 1.344 0.276
Max
L3 F(1,12) = 9.122 0.011 F(2,24) = 4.840 0.017 F(1.302,15.624) = 1.719 0.212
Max
L4 F(1,12) = 24.425 <0.001 F(2,24) = 10.115 0.001 F(1.324,15.890) = 0.536 0.523
Max
L5 F(1,12) = 8.044 0.015 F(2,24) = 30.870 <0.001 F(1.232,14.780) = 0.032 0.903
Max
S1 F(1,12) = 40.144 <0.001 F(2,24) = 44.657 <0.001 F(1.347,16.164) = 2.340 0.140
Max
S2 F(1,12) = 39.398 <0.001 F(2,24) = 54.751 <0.001 F(2,24) = 1.621 0.219
Figure 3. Spatiotemporal maps of the co-activation of the muscles innervated by the lumbosacral
enlargement in two-person team lifting at low (LI = 1, green), medium (LI = 2, yellow), and high
(
LI = 3
, red) risk levels. The top panels show the output pattern of each segment (mean
±
SD) in a
color scale. The lowest plots show the co-activation (TMCf averaged across participants, mean
±
SD)
as a function of the lifting cycle and spinal segment level (L3–S2).

Appl. Sci. 2024,14, 4635 7 of 12
3.3. TMCf Indices
The two-way repeated measures ANOVA showed a significant main effect of the
lifting type on CI
glob
,CI
full_leg
,CI
ext
,CI
L3
,CI
L4
,CI
L5
,CI
S1
,CI
S2
(Table 3), Max
glob
,Max
full_leg
,
Max
ext
,Max
L3
,Max
L4
,Max
L5
,Max
S1
,Max
S2
(Table 4) and of the LI on CI
glob
,CI
full_leg
,CI
ext
,
CI
L4
,CI
L5
,CI
S1
,CI
S2
(Table 3), Max
glob
,Max
full_leg
,Max
ext
,Max
L3
,Max
L4
,Max
L5
,Max
S1
,
MaxS2 (Table 4).
Table 3. The table shows the results of the two-way repeated measures ANOVA (F, dF, and pvalues)
on the co-activation index (CI) calculated for each TMCf. Bold indicates significant differences.
Lifting Type Risk Level Lifting Type Risk Level
FpFpFp
CIglob F(1,12) = 60.402 <0.001 F(2,24) = 71.477 <0.001 F(2,24) = 0.752 0.482
CI
full_leg F(1,12) = 58.307 <0.001 F(2,24) = 72.454 <0.001 F(2,24) = 1.138 0.337
CIext F(1,12) = 82.544 <0.001 F(2,24) = 75.031 <0.001 F(2,24) = 3.975 0.032
CIflex F(1,12) = 2.491 0.141 F(2,24) = 0.847 0.441 F(2,24) = 0.328 0.723
CIL3 F(1,12) = 5.310 0.040 F(2,24) = 2.404 0.112 F(2,24) = 0.082 0.921
CIL4 F(1,12) = 7.219 0.020 F(2,24) = 24.425 <0.001 F(2,24) = 0.311 0.735
CIL5 F(1,12) = 58.770 <0.001 F(2,24) = 109.746 <0.001 F(2,24) = 1.938 0.166
CIS1 F(1,12) = 83.003 <0.001 F(2,24) = 112.766 <0.001 F(2,24) = 2.105 0.144
CIS2 F(1,12) = 106.360 <0.001 F(1.322,15.861) = 103.238 <0.001 F(2,24) = 3.558 0.044
Table 4. This table shows the results of the two-way repeated measures ANOVA (F, df, and pvalues)
on the maximum value (Max) calculated for each TMCf. Bold indicates significant differences.
Lifting Type Risk Level Lifting Type Risk Level
FpFpFp
Maxglob F(1,12) = 14.974 0.002 F(2,24) = 22.637 <0.001 F(2,24) = 0.456 0.639
Maxfull_leg F(1,12) = 6.737 0.023 F(2,24) = 30.956 <0.001 F(1.253,15.034) = 0.781 0.469
Maxext F(1,12) = 17.749 0.001 F(2,24) = 63.992 <0.001 F(2,24) = 2.906 0.074
Maxflex F(1,12) = 0.643 0.438 F(2,24) = 1.925 0.168 F(1.316,15.794) = 1.344 0.276
MaxL3 F(1,12) = 9.122 0.011 F(2,24) = 4.840 0.017 F(1.302,15.624) = 1.719 0.212
MaxL4 F(1,12) = 24.425 <0.001 F(2,24) = 10.115 0.001 F(1.324,15.890) = 0.536 0.523
MaxL5 F(1,12) = 8.044 0.015 F(2,24) = 30.870 <0.001 F(1.232,14.780) = 0.032 0.903
MaxS1 F(1,12) = 40.144 <0.001 F(2,24) = 44.657 <0.001 F(1.347,16.164) = 2.340 0.140
MaxS2 F(1,12) = 39.398 <0.001 F(2,24) = 54.751 <0.001 F(2,24) = 1.621 0.219
Figures 4and 5show the results of pairwise comparisons of risk levels and lifting
styles for CI and Max of TMCf. For brevity and conciseness, only the approaches that
resulted in statistically significant differences are shown.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 8 of 13
Figures 4 and 5 show the results of pairwise comparisons of risk levels and lifting
styles for CI and Max of TMCf. For brevity and conciseness, only the approaches that re-
sulted in statistically significant differences are shown.
Figure 4. Violin plots of the mean of the TMCf function over the lifting cycle (CI) over all the subjects
for each risk level (LI = 1 green, LI = 2 yellow, LI = 3 red) in one-person and two-person team lifting
for each muscle co-activation investigated: global (CIglob), full leg (CIfull_leg), extensor (CIext), flexor
(CIflex), and rostro-caudal organization (from L3 to S2: CIL3, CIL4, CIL5, CIS1 and CIS2). The doed black
lines correspond to the mean of each CI value over all the subjects. An asterisk (*) indicates signifi-
cant differences.
Figure 5. Violin plots of the maximum of the TMCf function over the lifting cycle (Max) over all the
subjects for each risk level (LI = 1 green, LI = 2 yellow, LI = 3 red) in one-person and two-person
team lifting for each muscle co-activation investigated: global (Maxglob), full leg (Maxfull_leg), extensor
(Maxext), flexor (Maxflex), and rostro-caudal organization (from L3 to S2: MaxL3, MaxL4, MaxL5, MaxS1
and MaxS2). The doed black lines correspond to the mean of each Max value over all the subjects.
An asterisk (*) indicates significant differences.
4. Discussion
With this work, we investigated the behavior of global muscle co-activation, the one
calculated with the rostro-caudal approach and then separating flexors and extensors,
during lifting activities under different risk conditions and performed by a single person
and in a team. Team lifting is one of the ergonomic strategies suggested in ISO 11228-1
[12] to decrease the exposure of workers to biomechanical risk and could influence lower
limb co-activation.
As already carried out for the analysis of the trunk [6], to beer understand how a
two-person team lifting strategy might influence the biomechanical risk, intended as a
mechanical risk due to ergonomic risk factors, such as aspects of the job that post a
Figure 4. Violin plots of the mean of the TMCf function over the lifting cycle (CI) over all the subjects
for each risk level (LI = 1 green, LI = 2 yellow, LI = 3 red) in one-person and two-person team lifting
for each muscle co-activation investigated: global (CI
glob
), full leg (CI
full_leg
), extensor (CI
ext
), flexor
(CI
flex
), and rostro-caudal organization (from L3 to S2: CI
L3
, CI
L4
, CI
L5
, CI
S1
and CI
S2
). The dotted
black lines correspond to the mean of each CI value over all the subjects. An asterisk (*) indicates
significant differences.

Appl. Sci. 2024,14, 4635 8 of 12
Appl. Sci. 2024, 14, x FOR PEER REVIEW 8 of 13
Figures 4 and 5 show the results of pairwise comparisons of risk levels and lifting
styles for CI and Max of TMCf. For brevity and conciseness, only the approaches that re-
sulted in statistically significant differences are shown.
Figure 4. Violin plots of the mean of the TMCf function over the lifting cycle (CI) over all the subjects
for each risk level (LI = 1 green, LI = 2 yellow, LI = 3 red) in one-person and two-person team lifting
for each muscle co-activation investigated: global (CIglob), full leg (CIfull_leg), extensor (CIext), flexor
(CIflex), and rostro-caudal organization (from L3 to S2: CIL3, CIL4, CIL5, CIS1 and CIS2). The doed black
lines correspond to the mean of each CI value over all the subjects. An asterisk (*) indicates signifi-
cant differences.
Figure 5. Violin plots of the maximum of the TMCf function over the lifting cycle (Max) over all the
subjects for each risk level (LI = 1 green, LI = 2 yellow, LI = 3 red) in one-person and two-person
team lifting for each muscle co-activation investigated: global (Maxglob), full leg (Maxfull_leg), extensor
(Maxext), flexor (Maxflex), and rostro-caudal organization (from L3 to S2: MaxL 3, MaxL4, MaxL5, MaxS1
and MaxS2). The doed black lines correspond to the mean of each Max value over all the subjects.
An asterisk (*) indicates significant differences.
4. Discussion
With this work, we investigated the behavior of global muscle co-activation, the one
calculated with the rostro-caudal approach and then separating flexors and extensors,
during lifting activities under different risk conditions and performed by a single person
and in a team. Team lifting is one of the ergonomic strategies suggested in ISO 11228-1
[12] to decrease the exposure of workers to biomechanical risk and could influence lower
limb co-activation.
As already carried out for the analysis of the trunk [6], to beer understand how a
two-person team lifting strategy might influence the biomechanical risk, intended as a
mechanical risk due to ergonomic risk factors, such as aspects of the job that post a
Figure 5. Violin plots of the maximum of the TMCf function over the lifting cycle (Max) over all the
subjects for each risk level (LI = 1 green, LI = 2 yellow, LI = 3 red) in one-person and two-person
team lifting for each muscle co-activation investigated: global (Max
glob
), full leg (Max
full_leg
), extensor
(Max
ext
), flexor (Max
flex
), and rostro-caudal organization (from L3 to S2: Max
L3
, Max
L4
, Max
L5
, Max
S1
and Max
S2
). The dotted black lines correspond to the mean of each Max value over all the subjects.
An asterisk (*) indicates significant differences.
4. Discussion
With this work, we investigated the behavior of global muscle co-activation, the one
calculated with the rostro-caudal approach and then separating flexors and extensors,
during lifting activities under different risk conditions and performed by a single person
and in a team. Team lifting is one of the ergonomic strategies suggested in ISO 11228-1 [
12
]
to decrease the exposure of workers to biomechanical risk and could influence lower limb
co-activation.
As already carried out for the analysis of the trunk [
6
], to better understand how
a two-person team lifting strategy might influence the biomechanical risk, intended as
a mechanical risk due to ergonomic risk factors, such as aspects of the job that post a
mechanical stress to the employee (i.e., forceful exertion, repetition, awkward or static
postures
. . .
) and that can cause ergonomic injuries and/or illnesses (e.g., injuries and
illnesses of the muscles, nerves, tendons, ligaments, joints, cartilage and spinal discs), we
evaluated the effect of two factors: the lifting type (i.e., one- vs. two-person team lifting)
and risk level (low LI = 1, medium LI = 2, and high LI = 3). Moreover, we decided to
investigate the TMCf, because it is already known for the trunk that it is related to the force
acting at the lumbosacral level and is sensitive enough to be able to discriminate between
the different levels of risk [6,22,30].
More in detail, regarding the TMCf maps we found that the activity profiles of the co-
activation of the muscles innervated at the level of the sacral segments widens considerably
as the risk level increases in one-person lifting, while in team lifting it remains contained.
In single lifting, spinal maps demonstrated a propensity toward a greater spread level of
the TMCf during most parts of the lifting cycle, initially affecting the sacrum and lower
lumbar regions while the risk level increases, while this does not happen in team lifting.
Such a pattern highlights how team lifting is an ergonomic tool also effective in reducing
co-activation of the lower limb and how this tool contributes to reducing the concurrent
activation of the muscles innervated by the distinct spinal levels, both lumbar and sacral.
Interestingly, our results are in accordance with what has already been published on
myotomal charts [
55
,
58
], which showed that muscles with larger activations include tibialis
anterior, peroneus longus, soleus, gastrocnemius medialis and lateralis, biceps femoris and
semitendinosus. They are innervated from the spinal cord’s more distal segments (L4–S2)
and have a greater range of activity, mostly involving the sacral segments and then, in more
severe neurological patients, the lumbar segments. This type of behavior can be explained
in two ways. Voluntary control is pyramidal and is therefore significantly expressed in
distal districts. Furthermore, in heavy lifting activities, the kinematic chain remains open
for the upper limbs and is closed for the lower limbs. This indicates the necessity to control

Appl. Sci. 2024,14, 4635 9 of 12
the ground reaction force that acts distally on the lower limbs. For the reasons listed above,
co-activation increases mainly distally due to the need to stabilize the entire system by
responding to the stresses determined by the reaction force. We observed enhanced TMCf
of these muscles with the risk level in the one-person team lifting and a strong mitigation
of this effect in the two-person team lifting.
Co-activation widening may potentially be a compensatory technique, as prolonging
co-contractions has been shown to stabilize joints [6,22,32,33].
Regarding synthetic indexes computed over the co-activation maps, we considered
the mean and the maximum value of the lifting cycles. The CI is the mean of the co-
activation function over the lifting cycle, and it has been chosen because it is connected
with the average level of TMCf during the lifting cycle, hence it provides information
about the overall task execution. The Max over the lifting cycle is a timely index that
indicates the maximum value of antagonist muscle activation while lifting. It has been
proven that, in terms of the trunk, it relates to peak loads that can produce severe spinal
injuries, resulting in degeneration and pain [
59
]. In the obtained results we can observe
that, in the global full leg and extensors approach there is a significant increase in CIs as
risk levels increase in both one-person and two-person team lifting. Regarding the Max,
the same results emerge in the approach that takes into consideration only the extensor
muscles. Considering the flexors there are no statistically significant differences in terms of
CI and Max. This is understandable, as the flexor muscles play a less significant role within
the task under consideration, unlike the role of the extensor muscles which generate the
necessary moment concentrically in lifting and counteract the external moment of lowering
by contracting eccentrically.
In fact, the full leg and extensor approach shows a significant reduction of CI at all
risk levels and of Max at LI = 3 for the Max in two-person compared to the one-person
team lifting.
Moving on to the rostro-caudal approach, results between LI pairs are observed in
muscles innervated at the L3 level, while the co-activation increases significantly again
proceeding towards L4 At L5 co-activation is significantly reduced in two-person compared
to the one-person team lifting, up to levels S1 and S2 in which there is a similar behavior to
that observed in co-activation with a global approach.
These results are in relation to what we have already found for the trunk both in the
case of lifting performed individually and in teams [6,22,31].
Furthermore, our findings are consistent with the necessity for the CNS for greater
co-activation, and therefore the rigidity of the lower limb, to cope with greater efforts and
gain stability.
Finally, the fact that in teams the co-activation at the same level of risk is almost always
lower than that which occurs in single lifts, shows that the need to coordinate between
subjects does not affect the ability of individual coordination.
Our findings indicate that the CNS streamlines motor regulation of lifting by adjusting
whole-limb stiffness based on risk level and lifting type.
Our findings indicate that the CNS reduces motor control of lifting by adjusting whole-
limb stiffness based on risk level and lifting type. The first limitation of this study is that the
electromyographic activity of only one of the two subjects of the team was investigated and,
in the future, it will be necessary to investigate both the involved subjects; then, the study
is still based on a small number of participants, so another need is to increase the sample
size; together with the expansion of the examined sample, it will be possible to analyze the
data differently by gender, as in this case, for the few subjects available, we have mixed
males and females with different anthropometric characteristics, as well as leg extensor
muscle and back extensor muscle strength levels, which is an additional limitation of the
study. Furthermore, it will also be necessary to evaluate the case of asymmetric lifting in
which the rotation of the trunk must be taken into consideration.
Another limitation is related to the absence of information about the habitual physical
activity of the participants so the results obtained should be interpreted with caution.

Appl. Sci. 2024,14, 4635 10 of 12
Furthermore, for the biomechanical characterization of the lower limb, it is necessary
to expand the study by also considering other factors such as the analysis of kinematics,
and the evaluation of any compensation and stability [14,15,60–62].
Lastly, considering the diffusion and popularity that wearable robotic technologies are
acquiring, another future development to take could be to assess the effects of wearable
technologies on the lower limb while performing single vs. team lifting tasks.
5. Conclusions
In conclusion, this study highlights that the global lower limb muscle co-activation
indexes can be associated with different levels of risk in both one-person and two-person
lifting. Furthermore, muscles innervated by more distal spinal segments, or the extensors
alone may be included in simplified co-activation indexes to be used in instrumental
approaches for biomechanical risk assessment. Lastly, this study adds credence to the
idea that team lifting is an effective ergonomic intervention that can be used to reduce
biomechanical risk.
Author Contributions: Conceptualization, G.C., T.V., M.S. and A.R.; methodology, G.C., T.V. and
A.R.; software, G.C.; validation, G.C., T.V., M.S. and A.R.; formal analysis, G.C.; investigation, G.C.
and T.V.; resources, A.R.; data curation, G.C.; writing—original draft preparation, G.C., T.V. and
A.R.; writing—review and editing, G.C., T.V., M.S. and A.R.; visualization, G.C., T.V., M.S. and A.R.;
supervision, M.S. and A.R.; project administration, A.R.; funding acquisition, A.R. All authors have
read and agreed to the published version of the manuscript.
Funding: The research presented in this article was carried out as part of the SOPHIA project, which
has received funding from the European Union’s Horizon 2020 research and innovation program
under Grant Agreement No. 871237 and as part of “Bando Ricerche in Collaborazione” 2022 ID 57
funded by INAIL.
Institutional Review Board Statement: The study was conducted in accordance with the Declaration
of Helsinki and approved by the local ethics committee (N. 0078009/2021).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The Data are available in a publicly accessible repository at the link:
https://humandatacorpus.org/.
Conflicts of Interest: The authors declare no conflicts of interest.
References
1.
de Kok, J.; Vroonhof, P.; Snijders, J.; Roullis, G.; Clarke, M.; Peereboom, K.; Dorst, P.; van Isusi, I. Work-Related Musculoskeletal
Disorders: Prevalence, Costs and Demographics in the EU; European Agency for Safety and Health at Work: Maastricht, The
Netherlands, 2019. [CrossRef]
2.
Govaerts, R.; Tassignon, B.; Ghillebert, J.; Serrien, B.; De Bock, S.; Ampe, T.; El Makrini, I.; Vanderborght, B.; Meeusen, R.; De
Pauw, K. Prevalence and incidence of work-related musculoskeletal disorders in secondary industries of 21st century Europe: A
systematic review and meta-analysis. BMC Musculoskelet. Disord. 2021,22, 751. [CrossRef] [PubMed]
3. Violante, F.S. Criteria for diagnosis and attribution of an occupational musculoskeletal disease. Med. Lav. 2020,111, 249.
4.
Bao, S.; Howard, N.; Lin, J.-H. Are work-related musculoskeletal disorders claims related to risk factors in workplaces of the
manufacturing industry? Ann. Work Expo. Health 2019,64, 152–164. [CrossRef] [PubMed]
5.
CWA 17938:2023; Guideline for Introducing and Implementing Real-Time Instrumental-Based Tools for Biomechanical Risk
Assessment. European Committee for Standardization: Brussels, Belgium, 2023. Available online: https://researchportal.vub.be/
en/publications/cwa-17938-guideline-for-introducing-and-implementing-real-time-in (accessed on 29 April 2024).
6.
Chini, G.; Varrecchia, T.; Tatarelli, A.; Silvetti, A.; Fiori, L.; Draicchio, F.; Ranavolo, A. Trunk muscle co-activation and activity in
one-and two-person lifting. Int. J. Ind. Ergon. 2022,89, 103297. [CrossRef]
7.
Waters, T.R.; Putz-Anderson, V.; Garg, A. Applications Manual for the Revised NIOSH Lifting Equation; Department of Health and
Human Services: Cincinnati, OH, USA, 1994.
8.
ISO/TR 12295; Ergonomics—Application Document for ISO Standards on Manual Handling (ISO 11228-1, ISO 11228-2 and ISO
11228-3) and Static Working Postures (ISO 11226). ISO: Geneva, Switzerland, 2014.
9.
Visser, S.; van der Molen, H.F.; Kuijer, P.P.; Hoozemans, M.J.; Frings-Dresen, M.H. Evaluation of team lifting on work demands,
workload and workers’ evaluation: An observational field study. Appl. Ergon. 2014,45, 1597–1602. [CrossRef] [PubMed]

Appl. Sci. 2024,14, 4635 11 of 12
10.
Ajoudani, A.; Albrecht, P.; Bianchi, M.; Cherubini, A.; Del Ferraro, S.; Fraisse, P.; Fritzsche, L.; Garabini, M.; Ranavolo, A.; Rosen,
P.H.; et al. Smart collaborative systems for enabling flexible and ergonomic work practices [industry activities]. IEEE Robot.
Autom. Mag. 2020,27, 169–176. [CrossRef]
11.
Ranavolo, A.; Ajoudani, A.; Cherubini, A.; Bianchi, M.; Fritzsche, L.; Iavicoli, S.; Sartori, M.; Silvetti, A.; Vanderborght, B.;
Varrecchia, T.; et al. The sensor-based biomechanical risk assessment at the base of the need for revising of standards for human
ergonomics. Sensors 2020,20, 5750. [CrossRef] [PubMed]
12. ISO 11228-1; Ergonomics—Manual Handling—Part 1: Lifting and Carrying. ISO: Geneva, Switzerland, 2021.
13.
Kotowski, S.E.; Davis, K.G.; Shockley, K. Impact of order and load knowledge on trunk kinematics during repeated lifting tasks.
Hum. Factors 2007,49, 808–819. [CrossRef] [PubMed]
14.
Graham, R.B.; Costigan, P.A.; Sadler, E.M.; Stevenson, J.M. Local dynamic stability of the lifting kinematic chain. Gait Posture
2011,34, 561–563. [CrossRef] [PubMed]
15.
Graham, R.B.; Sadler, E.M.; Stevenson, J.M. Local dynamic stability of trunk movements during the repetitive lifting of loads.
Hum. Mov. Sci. 2012,31, 592–603. [CrossRef]
16.
Kazemi, Z.; Mazloumi, A.; Arjmand, N.; Keihani, A.; Karimi, Z.; Ghasemi, M.S.; Kordi, R. A Comprehensive Evaluation of Spine
Kinematics, Kinetics, and Trunk Muscle Activities During Fatigue-Induced Repetitive Lifting. Hum. Factors 2022,64, 997–1012.
[CrossRef]
17.
Varrecchia, T.; Conforto, S.; De Nunzio, A.M.; Draicchio, F.; Falla, D.; Ranavolo, A. Trunk Muscle Coactivation in People with and
without Low Back Pain during Fatiguing Frequency-Dependent Lifting Activities. Sensors 2022,22, 1417. [CrossRef] [PubMed]
18.
Ranavolo, A.; Draicchio, F.; Varrecchia, T.; Silvetti, A.; Iavicoli, S. Wearable monitoring devices for biomechanical risk assessment
at work: Current status and future challenges—A systematic review. Int. J. Environ. Res. Public Health 2018,15, 2001; Erratum in
Int. J. Environ. Res. Public Health 2018,15, 2569. [CrossRef] [PubMed]
19.
Weston, E.B.; Dufour, J.S.; Lu, M.L.; Marras, W.S. Spinal loading and lift style in confined vertical space. Appl. Ergon. 2020,
84, 103021. [CrossRef] [PubMed]
20.
Marras, W.S.; Mirka, G.A. Electromyographic Studies of the Lumbar Trunk Musculature during the Generation of Low level
Trunk Acceleration. J. Orthop. Res. 1993,11, 811–817. [CrossRef] [PubMed]
21.
Granata, K.P.; Marras, W.S. The influence of trunk muscle coactivity on dynamic spinal loads. Spine 1995,20, 913–919. [CrossRef]
[PubMed]
22.
Ranavolo, A.; Varrecchia, T.; Iavicoli, S.; Marchesi, A.; Rinaldi, M.; Serrao, M.; Conforto, S.; Cesarelli, M.; Draicchio, F. Surface
electromyography for risk assessment in work activities designed using the “revised NIOSH lifting equation”. Int. J. Ind. Ergon.
2018,68, 34–45. [CrossRef]
23.
Hwang, S.; Kim, Y.; Kim, Y. Lower extremity joint kinetics and lumbar curvature during squat and stoop lifting. BMC Musculoskelet.
Disord. 2009,10, 15. [CrossRef] [PubMed]
24.
Alemi, M.M.; Geissinger, J.; Simon, A.A.; Chang, S.E.; Asbeck, A.T. A passive exoskeleton reduces peak and mean EMG during
symmetric and asymmetric lifting. Electromyogr. Kinesiol. 2019,47, 25–34. [CrossRef]
25.
Boocock, M.G.; Taylor, S.; Mawston, G.A. The influence of age on spinal and lower limb muscle activity during repetitive lifting.
J. Electromyogr. Kinesiol. 2020,55, 102482. [CrossRef]
26.
Brinkmann, A.; Fifelski-von Böhlen, C.; Hellmers, S.; Meyer, O.; Diekmann, R.; Hein, A. Physical Burden in Manual Patient
Handling: Quantification of Lower Limb EMG Muscle Activation Patterns of Healthy Individuals Lifting Different Loads
Ergonomically. HEALTHINF 2021,5, 451–458. [CrossRef]
27.
Larivière, C.; Gagnon, D.; Loisel, P. A biomechanical comparison of lifting techniques between subjects with and without chronic
low back pain during freestyle lifting and lowering tasks. Clin. Biomech. 2002,17, 89–98. [CrossRef] [PubMed]
28.
Sakata, K.; Kogure, A.; Hosoda, M.; Isozaki, K.; Masuda, T.; Morita, S. Evaluation of the age-related changes in movement
smoothness in the lower extremity joints during lifting. Gait Posture 2010,31, 27–31. [CrossRef] [PubMed]
29.
INAIL. Italian Worker’s Compensation Authority Annual Report. Part IV. Statistics, Accidents and Occupational Diseases. 2022.
Available online: https://bancadaticsa.inail.it (accessed on 8 April 2024).
30.
Varrecchia, T.; De Marchis, C.; Draicchio, F.; Schmid, M.; Conforto, S.; Ranavolo, A. Lifting activity assessment using kinematic
features and neural networks. Appl. Sci. 2020,10, 1989. [CrossRef]
31.
Varrecchia, T.; De Marchis, C.; Rinaldi, M.; Draicchio, F.; Serrao, M.; Schmid, M.; Conforto, S.; Ranavolo, A. Lifting activity
assessment using surface electromyographic features and neural networks. Int. J. Ind. Ergon. 2018,66, 1–9. [CrossRef]
32. Latash, M.L. Muscle coactivation: Definitions, mechanisms, and functions. J. Neurophysiol. 2018,120, 88–104. [CrossRef]
33.
Le, P.; Best, T.M.; Khan, S.N.; Mendel, E.; Marras, W.S. A review of methods to assess coactivation in the spine. J. Electromyogr.
Kinesiol. 2017,32, 51–60. [CrossRef] [PubMed]
34.
Rosa, M.C.N.; Marques, A.; Demain, S.; Metcalf, C.D.; Rodrigues, J. Methodologies to assess muscle co-contraction during gait in
people with neurological impairment–a systematic literature review. J. Electromyogr. Kinesiol. 2014,24, 179–191. [CrossRef]
35.
Waters, T.R.; Putz-Anderson, V.; Garg, A.; Fine, L.J. Revised NIOSH equation for the design and evaluation of manual lifting
tasks. Ergonomics 1993,36, 749–776. [CrossRef]
36. Marras, W.S. Occupational low back disorder causation and control. Ergonomics 2000,43, 880–902. [CrossRef]
37.
Plamondon, A.; Gagnon, M.; Desjardins, P. Validation of two 3-D segment models to calculate the net reaction forces and moments
at the L5/S1 joint in lifting. Clin. BioMech. 1996,11, 101–110. [CrossRef] [PubMed]

Appl. Sci. 2024,14, 4635 12 of 12
38.
Lacquaniti, F.; Ivanenko, Y.P.; Zago, M. Patterned control of human locomotion. J. Physiol. 2012,590, 2189–2199. [CrossRef]
[PubMed]
39.
Yakovenko, S.; Mushahwar, V.; VanderHorst, V.; Holstege, G.; Prochazka, A. Spatiotemporal activation of lumbosacral motoneu-
rons in the locomotor step cycle. J. Neurophysiol. 2002,87, 1542–1553. [CrossRef] [PubMed]
40.
Ivanenko, Y.P.; Poppele, R.E.; Lacquaniti, F. Spinal cord maps of spatiotemporal alpha-motoneuron activation in humans walking
at different speeds. J. Neurophysiol. 2006,95, 602–618. [PubMed]
41.
Monaco, V.; Ghionzoli, A.; Micera, S. Age-related modifications of muscle synergies and spinal cord activity during locomotion.
J. Neurophysiol. 2010,104, 2092–2102. [CrossRef] [PubMed]
42.
Ivanenko, Y.P.; Dominici, N.; Cappellini, G.; Di Paolo, A.; Giannini, C.; Poppele, R.E.; Lacquaniti, F. Changes in the spinal
segmental motor output for stepping during development from infant to adult. J. Neurosci. 2013,33, 3025–3036. [CrossRef]
[PubMed]
43.
Fiori, L.; Castiglia, S.F.; Chini, G.; Draicchio, F.; Sacco, F.; Serrao, M.; Tatarelli, A.; Varrecchia, T.; Ranavolo, A. The Lower Limb
Muscle Co-Activation Map during Human Locomotion: From Slow Walking to Running. Bioengineering 2024,11, 288. [CrossRef]
[PubMed]
44.
von Arx, M.; Liechti, M.; Connolly, L.; Bangerter, C.; Meier, M.L.; Schmid, S. From Stoop to Squat: A Comprehensive Analysis of
Lumbar Loading among Different Lifting Styles. Front. Bioeng. Biotechnol. 2021,9, 769117. [CrossRef]
45.
Bazrgari, B.; Shirazi-Adl, A.; Arjmand, N. Analysis of squat and stoop dynamic liftings: Muscle forces and internal spinal loads.
Eur. Spine J. 2007,16, 687–699. [CrossRef] [PubMed] [PubMed Central]
46.
Van Dieën, J.H.; Van Hoozemans MJ, M.; Van Toussaint, H.M. A Review of Biomechanical Studies on Stoop and Squat Lifting.
Proc. Hum. Factors Ergon. Soc. Annu. Meet. 2000,44, 643–646. [CrossRef]
47.
Ivanenko, Y.P.; Cappellini, G.; Dominici, N.; Poppele, R.E.; Lacquaniti, F. Modular control of limb movements during human
locomotion. J. Neurosci. 2007,27, 11149–11161. [CrossRef] [PubMed]
48.
Wu, G.; Van der Helm, F.C.; Veeger, H.D.; Makhsous, M.; Van Roy, P.; Anglin, C.; Nagels, J.; Karduna, A.R.; McQuade, K.;
Wang, X.; et al. ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint
motion—Part II: Shoulder, elbow, wrist and hand. J. Biomech. 2005,38, 981–992. [CrossRef] [PubMed]
49.
Barbero, M.; Merletti, R.; Rainoldi, A. Atlas of Muscle Innervation Zones: Understanding Surface Electromyography and Its Applications;
Springer Science & Business Media: Berlin/Heidelberg, Germany, 2012.
50.
Hermens, H.J.; Freriks, B.; Disselhorst-Klug, C.; Rau, G. Development of Recommendations for SEMG Sensors and Sensor
Placement Procedures. J. Electromyogr. Kinesiol. 2000,10, 361–374. [CrossRef] [PubMed]
51.
Merletti, R.; Cerone, G.L. Tutorial. Surface EMG Detection, Conditioning and Pre-Processing: Best Practices. J. Electromyogr.
Kinesiol. 2020,54, 102440. [CrossRef]
52.
Merletti, R.; Muceli, S. Tutorial. Surface EMG Detection in Space and Time: Best Practices. J. Electromyogr. Kinesiol. 2019,49, 102363.
[CrossRef] [PubMed]
53.
Marras, W.S.; Davis, K.G. A non-MVC EMG normalization technique for the trunk musculature: Part 1. Method development.
J. Electromyogr. Kinesiol. 2001,11, 1–9. [CrossRef] [PubMed]
54.
Burden, A. How should we normalize electromyograms obtained from healthy participants? What we have learned from over 25
years of research. J. Electromyogr. Kinesiol. 2001,20, 1023–1035. [CrossRef] [PubMed]
55.
Kendall, F.P.; McCreary, E.K.; Provance, P.G.; Rodgers, M.M.; Romani, W.A. Muscles: Testing and Function with Posture and Pain;
Lippincott Williams & Wilkins: Baltimore, MD, USA, 2005; Volume 5, pp. 1–100.
56.
Dewolf, A.H.; Sylos-Labini, F.; Cappellini, G.; Zhvansky, D.; Willems, P.A.; Ivanenko, Y.; Lacquaniti, F. Neuromuscular age-related
adjustment of gait when moving upwards and downwards. Front. Hum. Neurosci. 2021,15, 749366. [PubMed]
57.
Prilutsky, B.L. Coordination of two-and one-joint muscles: Functional consequences and implications for motor control. Mot.
Control. 2000,4, 1–44. [CrossRef]
58.
Sharrard, W.J.W. The segmental innervation of the lower limb muscles in man: Arris and Gale lecture delivered at the Royal
College of Surgeons of England on 2nd January 1964. Ann. R. Coll. Surg. Engl. 1964,35, 106.
59. Adams, M.A.; Dolan, P. Spine biomechanics. J. Biomech. 2005,38, 1972–1983. [CrossRef]
60.
Granata, K.P.; Orishimo, K.F. Response of trunk muscle coactivation to changes in spinal stability. J. Biomech. 2001,34, 1117–1123.
[CrossRef]
61. Granata, K.P.; Wilson, S.E. Trunk posture and spinal stability. Clin. Biomech. 2001,16, 650–659. [CrossRef]
62.
Granata, K.P.; Slota, G.P.; Wilson, S.E. Influence of fatigue in neuromuscular control of spinal stability. Hum. Factors 2004,46,
81–91. [CrossRef]
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