![The five King’s classifications of scoliotic curve [3], illustrated from T1 to L5. a) Classification 1: Double curve of the thoracic and lumbar spine. b) Classificiation 2: Double curve of the thoracic and the lumbar spine with less prominent lumbar curvature. c) Classification 3: Single primary thoracic curve. d) Classification 4: Long thoracic curve. e) Classification 5: Double thoracic curve. Illustrated using OpenSim [33].](https://www.researchgate.net/profile/Madeleine-Lowery/publication/261442097/figure/fig1/AS:214233813708800@1428088729078/The-five-Kings-classifications-of-scoliotic-curve-3-illustrated-from-T1-to-L5_Q320.jpg)
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R E S E A R C H Open Access
Musculoskeletal modelling of muscle activation
and applied external forces for the correction of
scoliosis
Maurice Curtin
*
and Madeleine M Lowery
Abstract
Background: This study uses biomechanical modelling and computational optimization to investigate muscle
activation in combination with applied external forces as a treatment for scoliosis. Bracing, which incorporates
applied external forces, is the most popular non surgical treatment for scoliosis. Non surgical treatments which
make use of muscle activation include electrical stimulation, postural control, and therapeutic exercises. Electrical
stimulation has been largely dismissed as a viable treatment for scoliosis, although previous studies have suggested
that it can potentially deliver similarly effective corrective forces to the spine as bracing.
Methods: The potential of muscle activation for scoliosis correction was investigated over different curvatures both
with and without the addition of externally applied forces. The five King’s classifications of scoliosis were investigated
over a range of Cobb angles. A biomechanical model of the spine was used to represent various scoliotic curvatures.
Optimization was applied to the model to reduce the curves using combinations of both deep and superficial muscle
activation and applied external forces.
Results: Simulating applied external forces in combination with muscle activation at low Cobb angles (< 20 degrees)
over the 5 King’s classifications, it was possible to reduce the magnitude of the curve by up to 85% for classification 4,
75% for classifications 3 and 5, 65% for classification 2, and 60% for classification 1. The reduction in curvature was less
at larger Cobb angles. For King’s classifications 1 and 2, the serratus, latissimus dorsi, and trapezius muscles were
consistently recruited by the optimization algorithm for activation across all Cobb angles. When muscle activation and
external forces were applied in combination, lower levels of muscle activation or less external force was required to
reduce the curvature of the spine, when compared with either muscle activation or external force applied in isolation.
Conclusions: The results of this study suggest that activation of superficial and deep muscles may be effective in
reducing spinal curvature at low Cobb angles when muscle groups are selected for activation based on the curve type.
The findings further suggest the potential for a hybrid treatment involving combined muscle activation and applied
external forces at larger Cobb angles.
Keywords: Muscle activation, Electrical stimulation, Scoliosis, Optimization, Biomechanical modelling
Background
Idiopathic scoliosis effects approximately 3% of children
and adolescents [1] and is defined as a lateral curvature
of the spine with rotation of the vertebrae within the
curve. Scoliosis is traditionally evaluated using the Cobb
angle, measured between the intersection of the lines
tangential to the vertebral endplates which make up the
lowermost and uppermost parts of the scoliotic curve.
The presence of scoliosis is typically defined for Cobb
angles greater than 10 degrees. The scoliotic curve may be
classified using the widely accepted King’s classification
scheme which classifies curves into one of five categories,
based on the location and shape of the curve on the spine
[2], Figure 1. Scoliosis can be treated both surgically and
non-surgically. Surgical treatment involves the application
of rods and hooks to the vertebrae and the spine is then
pushed and fused into place. This generally restricts the
* Correspondence: maurice.curtin@ucdconnect.ie
School of Electrical, Electronic and Communications Engineering, University
College Dublin, Dublin, Belfield, Ireland
JNERJOURNAL OF NEUROENGINEERING
AND REHABILITATION
© 2014 Curtin and Lowery; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the
Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly credited.
Curtin and Lowery Journal of NeuroEngineering and Rehabilitation 2014, 11:52
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overall movement of the spine, but is necessary in extreme
cases of scoliosis. An alternative is non-surgical treatment
which takes place over a number of years. Bracing is cur-
rently the most popular of non-surgical treatment methods
for scoliosis [3,4]. Alternative non-surgical methods have
made use of muscle activation patterns for scoliosis cor-
rection and include electrical stimulation [5-7], therapeutic
exercise [8] and postural control [9]. All of the above
methods have demonstrated varied levels of success in
controlling the progression of scoliotic curves. Electrical
stimulation, in particular, has had limited success. A
prospective study by Nachemson et al. comparing the
effectiveness of bracing and stimulation for the correction
of scoliosis concluded that electrical stimulation is not an
effective treatment [10]. However, variation in electrode
placements and stimulation details, such as the duration
of the applied stimulation, were not considered. Fur-
thermore, the Cobb angles included in that study were
restricted to a minimum of 25°, with lower Cobb angles
not considered. A similar study by Rowe et al. [11] reported
a weighted mean success rate of 0.39 with muscle stimula-
tion for the treatment of scoliosis in contrast to rates of
up to 0.93 with bracing. A more recent in vivo study
has hinted at reviving stimulation treatment for scoliosis
[12]. The results of that study suggest that that electrical
stimulation may be effective in correcting scoliotic curva-
ture, particularly at Cobb angles of 20° or less. Specific
therapeutic exercises developed for scoliosis correction
which also elicit muscle activation have demonstrated
success in reducing the progression rate of scoliotic curva-
ture and reducing the magnitude of the Cobb angle [8].
These may provide an alternative non-surgical method to
limit the progression of spinal curvature using muscle
activation.
Biomechanical models of the spine have been used for
several decades to investigate muscle and brace force
patterns and their effects on scoliotic curves [13-21]. It
has been hypothesised that forces which deliver small
initial correction will achieve a larger correction in the
long term with continuing treatment [22]. Computational
studies for scoliosis correction have, therefore, assumed a
force pattern which will deliver the best possible immediate
correction to the curve [17,20]. The majority of modelling
studies which have considered treatments for scoliosis have
focused on bracing and surgery [20,23,24]. To examine the
corrective potential of muscle forces on a scoliotic curve,
Wynarsky et al. [17] applied an optimization to a computer
model of a curve, comparing simulations of muscle forces
and brace forces. It was suggested that within the defined
constraints, muscle activation is more effective at correcting
the curve than passive brace forces. However it was also
noted that it was not possible to reproduce the specific
optimal muscle activation patterns in vivo. The conclusion
of the modelling study presented in [17] is in contrast to
the findings of [10], and poses the question as to whether
muscle forces elicited by electrical stimulation or targeted
physical therapy could potentially deliver a more effective
corrective treatment than can be achieved by bracing or
postural control alone.
The study presented in this paper addresses this question
across a range of spinal configurations using the concept of
generalised external forces in place of forces specific to a
particular brace type. There has not been much progress in
the field of non-surgical scoliosis correction in many years.
With the advance in electrode technology in recent years,
targeted stimulation of superficial and deep muscles is now
possible through the use of implantable electrodes [25].
Muscle activation may thus have potential in scoliosis cor-
rection that has not yet been explored. Both superficial and
deep muscle groups were, therefore, analysed in this study.
The aim of this study was to investigate the potential
of deep and superficial muscle activation in combination
Figure 1 The five King’s classifications of scoliotic curve [3], illustrated from T1 to L5. a) Classification 1: Double curve of the thoracic and
lumbar spine. b) Classificiation 2: Double curve of the thoracic and the lumbar spine with less prominent lumbar curvature. c) Classification 3:
Single primary thoracic curve. d) Classification 4: Long thoracic curve. e) Classification 5: Double thoracic curve. Illustrated using OpenSim [33].
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with applied external forces, for the correction of scoliosis
in a computer model. Previous studies have shown that
lateral placement of electrodes on the convex side of the
curve delivers the best results using surface electrode
stimulation [5]. The results obtained using the model
are tested against these guidelines to examine whether a
theoretically more effective electrode placement exists
and if so, whether it varies across all of the five King’s
classifications. These curve classifications were chosen
to avoid limiting the study to one typical scoliotic curve
type and to examine how the muscle activation patterns
adapted across the different curvatures.
Methods
A model of the thoracolumbar spine, ribcage and ster-
num incorporating deep and superficial muscles of the
lumbar and thoracic regions was constructed in Matlab
(Mathworks, MA, USA). The five King’s classifications
[2] were simulated by adjusting the vertebral positions
relative to one another. For each classification, the Cobb
angle was adjusted across the range 10° –60°. In King’s
classifications 1 and 2, curves are present in both the
lumbar and thoracic areas, the Cobb measurement was
thus based on the lumbar curve in these cases, and the
thoracic curve was adjusted proportionally. The model
inputs were the forces elicited from muscle activation
patterns and applied external forces. The outputs of the
model were the resultant distances moved by each individ-
ual body in the model.
The deep and superficial muscle architecture of the
lumbar and abdominal regions was included as described
by Stokes and Morse [26]. The multifidus muscles were
excluded as they are unlikely to deliver corrective forces
to the spine due to their location and orientation. The
thoracic muscle architecture was based on that described
in [16]. Muscles were classified as being either ‘superficial’
or ‘deep’according to their accessibility for electrical stimu-
lation. Although the focus was not exclusively on muscle
activation as a result of electrical stimulation, this was
deemed the most suitable method to classify the muscle
groups. The superficial muscles consisted of the latissimus
dorsi, trapezius, rectus abdominis, serratus, pectorals and
external abdominals. The deep muscles were identified as
the psoas, intercostals, erector spinae, quadratus lumborum
and internal abdominals.
Global vertebral positions of the model were based on
a cadaver study [18], while all remaining joint stiffness
and body positions were based on the model presented
by Takashima et al. [16]. Gravity was omitted from the
model, as the subject was assumed to be prone. All force
displacement relationships were solved using a direct
stiffness procedure [14]. The main model parameters are
presented in Tables 1 and 2. The model was locked at the
sacrum to ensure no displacement below the lumbosacral
joint. Translational displacement of the T1 vertebra was
restricted in the x(medial/lateral) and z(anterior/posterior)
directions was restricted to simulate the in vivo mechanical
restraints of the thorax [20].
Three external forces, limited to a maximum of 100 N
were available to the model. These were comprised of
two thoracic and one lumbar force, as applied in [20].
The thoracic forces were free to act on any of the ten
ribs while the lumbar force could act on any one of the
five lumbar vertebrae. All forces acted in the transverse
plane. The force locations were chosen based on a typical
three-point corrective force pattern applied by a brace
[28]. The forces included in this study, however, were not
intended to be representative of a specific brace and could
potentially be delivered in vivo through either postural
control or with the application of a brace.
For each King’s classification and Cobb angle, an
optimization routine was performed to identify the set
of corrective forces which reduced the curvature by
minimizing the displacement of all vertebrae from the
sagittal plane. All muscle groups were made available to
the model, both with and without the inclusion of the ex-
ternally applied forces. The simulations and optimizations
Table 1 Model positions
xyz
T1 vertebra 0 454.9 19.4
T2 vertebra 0 437.3 −14.4
T3 vertebra 0 418 −20.8
T4 vertebra 0 397.2 −23
T5 vertebra 0 375.3 −22.8
T6 vertebra 0 351.5 −21.5
T7 vertebra 0 326.8 −20.4
T8 vertebra 0 302.5 −20
T9 vertebra 0 278.2 −20.2
T10 vertebra 0 252.8 −20.4
T11 vertebra 0 225.1 −19.5
T12 vertebra 0 196 −15.8
L1 vertebra 0 166.3 −8.4
L2 vertebra 0 133.9 3.6
L3 vertebra 0 100.6 17.4
L4 vertebra 0 64.8 26.2
L5 vertebra 0 31.7 16.2
Left Rib 1 24.15 437.4 41.65
Left costo-vertebral attachment T1 15.15 454.9 29.65
Left costo-transverse attachment T1 40 454.6 9.9
Left Costal-cartilage attachment 1 30 437.4 41.65
The global positions of the model components [15,26,27]. Values are
summarized and data for the left side of the model can be replicated on the
right side by inverting the x value. Remaining data can be found in [16,27],
and translated based on data presented in this table. Data are presented
in mm.
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were repeated with deep muscle groups excluded from the
model. This was done to examine the potential contri-
bution to the overall results by the deep muscle groups
which are typically more difficult to isolate.
The optimization was performed using the glcDirect
solver developed by Tomlab optimization software (Tomlab,
WA, USA) for Matlab (Mathworks, MA, USA). This
solver implements an extended version of the DIRECT
algorithm developed by Jones et al., [29]. The sum of
the squares of the distances of the vertebrae from the
sagittal plane, D, was chosen as the objective function to
be minimized,
D¼Xn
i¼1Vdi−Vn
i
ðÞ
2ð1Þ
where Vd
i
is the global starting position of vertebra iand
Vn
i
is the global position of vertebra iin a normal, straight
spine. nrepresents the total number of vertebrae. This
follows the approach used in previous studies of spinal
curvature correction in which through minimisation of
the objective function, the scoliotic curve was moved as
close towards a normal alignment as possible [17,20].
The initial values for the objective functions are listed
in Table 3. Static equilibrium of the model was imposed,
following the approach described by Stokes and Gardner-
Morse [19],
FMþFEXT −KD ¼0ð2Þ
where F
M
represents muscle forces, F
EXT
represents exter-
nal forces, Krepresents model stiffness, and Drepresents
model displacement. All individual model body displace-
ments relative to one another were limited to 5 degrees
rotation and 5 mm translationinthesagittalplane,
and 2 degrees rotation and 2 mm translation in all
other planes following an approach previously used to
represent physiological limits [19]; each external force
was constrained between 0 N and 100 N and the activa-
tion levels of each individual muscle was constrained to a
value of either 0 or 1. Partial activation of muscles was not
considered in this study to eliminate solutions comprised
of complex muscle activation patterns that would be
difficult to elicit in vivo. This made it possible to identify
on key muscle groups that could contribute to the curve
correction by delivering substantial forces to the model.
Results
The overall curve correction obtained by the optimization
algorithm was less for larger values of the Cobb angle.
The ability of the applied muscle and external forces to
correct the curvature also varied across the five King’s
Classifications, reflecting the different morphology of the
curves, Figure 2(a-e). Taking the most effective muscle
and external force combination, at a Cobb angle of 20°,
the objective function was reduced by approximately 85%
for classification 4, 75% for classifications 3 and 5, 65% for
classification 2, and 60% for classification 1, Figure 2. At a
Cobb angle of 40°, the objective function was reduced by
approximately 60% for classification 5, 55% for classifi-
cations 3 and 4, and 40% for classifications 1 and 2.The
differences observed in the percentage reduction between
classifications increased further when analysing the applica-
tion of external forces only. The difference was most appar-
entinclassification1whencomparingtheimprovement
achieved using external forces only, to that achieved using
other muscle and force combinations. Compared with
muscle and external force combinations, the reduction
in objective function was approximately 20% lower using
external forces only between Cobb angles of 10° and 30°,
Figure 2a. As the Cobb angle increased, the difference
between application of external forces alone and the
remaining muscle and force combinations was less
pronounced.
A muscle group was considered active if any muscle
within that group was activated for a given optimization.
For King’s classifications 1 and 2, the serratus, latissimus
dorsi, and trapezius, were recruited by the optimization
algorithm for activation. The serratus was targeted similarly
Table 2 Model stiffness
Tx (N/mm) Ty (N/mm) Tz (N/mm) Rxyz
(Nm/deg)
T1 686.7 588.6 196.2 0.3
T2 1177.2 1079.1 294.3 0.6
T4 2060.1 1863.9 588.6 1.7
T5 1863.9 1667.7 588.6 1.7
T6 1765.8 1569.6 588.6 1.7
T7 1471.5 1373.4 588.6 1.7
T8 1471.5 1275.3 588.6 1.8
T9 1471.5 1373.4 686.7 1.8
T10 1471.5 1373.4 686.7 2.1
T11 1471.5 1079.1 784.8 1.7
T12 1765.8 981 981 1.5
L1 1569.6 882.9 1177.2 1.5
L3 1471.5 784.8 1177.2 1.5
L4 1373.4 686.7 1079.1 1.3
L5 1079.1 588.6 882.9 1.2
Costo-vertebral 49.05 49.05 49.05 0.2
Costo-transverse 49.05 49.05 49.05 0.2
Costal cartilage 73.575 73.575 24.525 0.2
Rotational (Rxyz) and translational (Tx, Ty, Tz) stiffness values for the model
components [13,15]. Stiffness values are relative (between adjacent bodies). The
global coordinate system was implemented with the positive x-axis (medial/lateral)
to the left of the model, the positive y-axis (superior/inferior) facing upwards, and
the positive z-axis (anterior/posterior) facing forward.
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Table 3 Objective functions
10° 20° 30° 40° 50° 60°
Classification 1 265.31 1272.02 2988.77 5346.61 8256.51 11618.49
Classification 2 267.73 1268.14 2917.32 5064.02 7522.67 10102.04
Classification 3 637.78 2508.58 5487.59 9375.95 13913.97 18798.27
Classification 4 735.26 2918.7 6483.99 11322.78 17288.05 24198.57
Classification 5 352.27 1397.05 3098.9 5400.69 8226.4 11484.81
The initial objective functions described in Equation 1for the 5 classifications. Data are presented every 10° for Cobb angles between 10° and 60°. Values are
presented in mm
2
.
Figure 2 The percentage reduction in objective function across all Cobb angles for the individual King’s classifications (a-e). Results are
presented when superficial and deep muscles are available to the model, superficial muscles only, both muscle combinations with externally
applied forces, and applied external forces only.
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Figure 3 Superficial Muscles: The percentage of optimizations for which each superficial muscle group was selected for activation for
the individual King’s classifications (a-e). Column 1 illustrates the result for when superficial and deep muscles were included in the model.
Column 2 illustrates the result for when only superficial muscles were included in the model. Columns 3 and 4 illustrate the equivalent results
with the addition of applied external forces to the model. The graph also indicates which side of the thoracic curve that the muscles were
located. The top area of each column represents the concave side, while the bottom area represents the convex side.
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Figure 4 Deep Muscles: The percentage of optimizations for which each deep muscle group was selected for activation for the individual
King’s classifications (a-e). Column 1 illustrates the result for when superficial and deep muscles were included in the model. Column 2 illustrates
the equivalent results with the addition of applied external forces to the model. The graph also indicates which side of the thoracic curve that the
muscles were located. The top area of each column represents the concave side, while the bottom area represents the convex side.
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for classifications 3–5, Figure 3. The rectus abdominis
muscle group was omitted from Figure 3 as it was only
chosen during a small percentage of optimizations in clas-
sification 4 (<5%).
When deep muscles were included in the optimisation,
the psoas, longissimus pars lumborum and intercostals
muscles were consistently selected by the optimization,
Figure 4. The distribution of muscles activated on each
side of the curve varied with the curve classification. In
King’s classifications 1 and 2, deep muscles were predom-
inantly activated on the convex side of the thoracic curve,
Figure 4(a,b). This was also true for the majority of
superficial muscles when external forces were not included
in the optimization, Figure 3(a,b). In classifications 3–5,
additional activation of muscles was introduced on the
concave side, Figures 3(c-e) and 4(c-e).
The optimization algorithm consistently identified the
largest average external forces when muscles were excluded
from the optimization, Figure 5. Similarly, the greatest
percentage of muscles activated across all classifications
occurred when external forces were excluded from the
optimization, Figure 6. When both muscle forces and
applied external forces were available to the optimization
algorithm, the lowest external forces were required when
both superficial and deep muscles were included in the
optimization. The average reduction in external force when
muscles were also included in the optimization ranged
between 25-35% of the maximum average external force
applied, Figure 5. This averages over all 3 external
forces, representing a reduction of up to 90 N in total in
some cases. The corrective external forces identified by the
optimisation algorithm for each model are summarized in
Table 4.
Discussion
In this study, a musculoskeletal model of the spine was
used to explore optimal combinations of muscle activation
and applied external forces to reduce the magnitude of
spinal curvature for different King’s classifications, across
a range of Cobb angles. For Cobb angles lower than
20°, simulated muscle activation was more effective in
reducing the objective function than application of external
forces alone. This occurred over all classifications excluding
classification 4, Figure 2. This observation is consistent
with the conclusion of a previous simulation study that
muscle activation can be equally effective as bracing at
reducing the severity of a scoliotic curve [17], although
it is noted that general external forces in place of a brace
were simulated in the present study. The effectiveness of
the simulated muscle activation at low Cobb angles is also
consistent with clinical successreportedinpreviousstudies
[5,6,12]. The simulation studies indicate reduced efficacy at
higher Cobb angles which may account in part for the poor
clinical outcomes of neuromuscular electrical stimulation
at Cobb angles greater than 20 degrees [10].
Although the inclusion of deep muscles altered the
results in certain cases, most notably Classifications 1
and 2, Figure 2(a,b), the results indicate that regardless of
whether deep muscles were available to the optimizer, the
same superficial muscle groups were targeted consistently
across the majority of Cobb angles, Figure 3. However, the
level of activation will vary across the superficial muscle
groups. Therefore, there will not necessarily be a reduc-
tion in the number of superficial muscle groups activated
when deep muscles are also targeted, but the same level of
activation will not be required.
Earlier studies [5,12] have proposed that the optimal
location for muscle stimulation for scoliosis correction is
on the convex side of the curve. This held true for classifi-
cations 1–3 with the exception of the trapezius and pecto-
rals where between 70% and 80% of the muscles activated
were on the convex side of the thoracic curve, Figures 3
and 4. The serratus and intercostal muscle groups used
the highest percentage of muscles on the convex side of
the thoracic curve across all classifications, Figures 3
and 4. This agrees with clinical guidelines for delivering
a straightening force to the spine by applying lateral elec-
trical stimulation to the convex side of the curve [5].
For the majority of the curves, the activation of both
the trapezius and the pectorals tended to occur on the
Figure 5 External forces. The average of the 3 external forces applied to the model over all Cobb angles for the five classifications.
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concave side of the thoracic curves. A similar activation
pattern also emerged for the single scoliotic configuration
simulated in [17]. The correctional effects of the latissimus
dorsi in the optimization are consistent with a previous
study in which the latissimus dorsi in rabbits was activated
to induce a concave curve on a straight spine [30]. To
investigate this further, these muscle groups were isolated
in the model, and similar activation patterns were applied.
It was found that the latissimus dorsi induced a straight-
ening force on the convex curve, while the trapezius and
pectorals stabilized the spine. This suggests that stimula-
tion on the concave side of the thoracic curve, in addition
to the traditional stimulation on the convex side, may lead
to better results and that the electrodes location should be
chosen based on the type and location of the spinal curve.
The presence of the lumbar curves in classifications 1
and 2 did not result in muscle activation on their respective
convex sides. The psoas and quadratus lumborum muscle
groups were activated depending on the orientation of the
lumbar curve in each classification. For classifications 1
and 2, the majority of muscle groups were activated on
the concave side of the lumbar curve (convex side of the
thoracic curve), Figure 4(a,b). Concave activation was also
noted in the lumbar region of the curve in classification 4,
Figure 4d. The muscles were activated in order to contract
from the concave side to straighten the lumbar curvature.
For classifications 3 and 5, the lumbar curve was small
and the balance of the muscle activation for the psoas
muscle group was almost even, Figure 4(c,e).
Similar patterns emerged when analysing the average
force and the percentage of muscles activated across all
Cobb angles and classifications. The highest average force
applied to the model always occurred when muscles were
excluded from the optimization, Figure 5. Although the
reduction in objective function is numerically similar
for many of the muscle and external force combinations,
Figure 2, the composition of the results in terms of relative
contribution of muscular and external force differed
between combinations, Figures 5 and 6.
While computational models provide a means to exam-
ine complex in vivo systems, it is important to note the
limitations inherent in the model and optimization. To
perform a large number of optimizations efficiently, the
direct stiffness method was chosen to simulate the rela-
tionship between the force applied to the model and the
displacements within the model model. Another approach
would be to use spring and damper systems to simulate
the model stiffness and to include muscle models such as
the Hill muscle model [31]. However, these methods
would not be computationally efficient for the number of
optimizations considered in this study. Since model dis-
placements were small, soft tissue properties other than
joint stiffness and intercostal tissue stiffness were not
included, as they were assumed to be negligible. While
the multifidus muscles are likely important in maintaining
static equilibrium, their contribution in the present study
Figure 6 Muscle activation. The percentage of available muscles selected for activation over all Cobb angles for the five classifications.
Table 4 Applied external forces
Classification 1 All muscles Superficial muscles No muscles
Left 58.22 (23.5) 77.26 (16.18) 97.67 (6.07)
Right 78.9 (13.11) 80.06 (23.79) 99.98 (0.05)
Lumbar 87.59 (12.23) 86.5 (11.83) 99.27 (1.17)
Classification 2 All muscles Superficial muscles No muscles
Left 66.5 (18.16) 81.44 (21.18) 99.63 (1.48)
Right 74.43 (15.86) 85.98 (13.22) 98.19 (5.57)
Lumbar 85.03 (12.9) 94.87 (6.15) 99.96 (0.09)
Classification 3 All muscles Superficial muscles No muscles
Left 61.2 (25.85) 71.32 (23.31) 87.32 (15.21)
Right 71.21 (17.1) 81.7 (13.61) 92.61 (3.95)
Lumbar 50.1 (29.4) 76.03 (23.86) 99.11 (2.01)
Classification 4 All muscles Superficial muscles No muscles
Left 49.52 (25.84) 65.65 (23.99) 81.64 (23.23)
Right 65.06 (17.19) 81.4 (11.96) 97 (3.94)
Lumbar 47.2 (36.63) 83.03 (21.65) 87.44 (23.2)
Classification 5 All muscles Superficial muscles No muscles
Left 68.9 (21.64) 68.8 (21.58) 91.05 (18.2)
Right 67.41 (16.44) 81.25 (11.74) 95.07 (6)
Lumbar 58.8 (37.53) 75.13 (24.12) 94.75 (7.73)
Average left, right and lumbar forces and their standard deviations applied
over all Cobb angles across the five Classifications. Values are presented in N.
Curtin and Lowery Journal of NeuroEngineering and Rehabilitation 2014, 11:52 Page 9 of 11
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was assumed to be negligible as the focus was the applica-
tion of instantaneous corrective force to the model while
in the prone position. The mechanical properties of the
model were identical on the left and right sides in the nor-
mal upright position. It has been demonstrated by that
side dominance affects muscle properties with regards to
activation levels and fatigability [32]. This would also need
to be considered in practice. The same objective function
was utilised in all models to allow comparison across the
conditions simulated. The sum of the squares of the verte-
bral distances was chosen, as it had been used in previous
studies. Different objective functions such as combina-
tions of individual vertebral rotations or curve areas could
have also been used, and it is possible that tailoring
different objective functions to each model would improve
individual results. Kyphosis and lordosis, conditions often
associated with scoliosis [6] were not examined in this
study, as the main focus was the varying Cobb angle for
the scoliotic curve. They could be accounted for by
adjusting the objective function accordingly. Externally
applied forces were not intended to represent any specific
treatment directly but could be adjusted to be representa-
tive of brace forces or other therapeutic treatments involv-
ing external forces applied to the torso. Similarly, muscle
activations elicited by the optimization were not necessar-
ily representative of either electrical stimulation or phys-
ical therapy, and further adjustment would be necessary
to represent a more realistic in vivo treatment. The results
of this study show the instantaneous correction achieved
by the optimizer. This provides an insight into which
muscles should be activated to correct specific curves to
achieve the best initial result but does not necessarily
reflect the results that would be achieved in a long term
in vivo treatment. The long-term sustainability of such
treatments in vivo is therefore beyond the scope of this
modelling study.
Conclusion
This study suggests that consideration of the curve classi-
fication and Cobb angle is critical when assessing the po-
tential for muscle activation for scoliosis correction. The
results have indicated that superficial muscle activation on
the convex side of the curve provides the best corrective
results, supporting previous in vivo stimulation stud-
ies. They further suggest that additional benefit may be
achieved through the simultaneous stimulation of muscle
groups on the concave side of the curve.
The results suggest the potential for a hybrid treatment
at low Cobb angles involving muscle activation, pos-
sibly achieved through electrical stimulation, and applied
external forces which may offer a more comfortable and
effective clinical alternative to bracing alone. The identifi-
cation of muscles capable of applying a corrective force to
scoliotic curves could also be useful in both postural
control devices and therapeutic exercises. Further studies
are necessary to investigate this potential before recom-
mending any clinical application, most notably in vivo
studies and an investigation of the sustainability resulting
from these treatments.
Competing interests
The authors declare that they have no competing interests.
Authors’contributions
MC participated in the study design and carried out the model simulations
and optimizations. ML participated in the study design and helped to draft
the manuscript. All authors read and approved the final manuscript.
Acknowledgements
This research was funded by an Enterprise Ireland grant TD/2008/345.
Received: 23 April 2013 Accepted: 28 March 2014
Published: 7 April 2014
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doi:10.1186/1743-0003-11-52
Cite this article as: Curtin and Lowery: Musculoskeletal modelling of
muscle activation and applied external forces for the correction of
scoliosis. Journal of NeuroEngineering and Rehabilitation 2014 11:52.
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