A musculoskeletal model of the upper extremity for use in the
development of neuroprosthetic systems
Dimitra Blana
*
, Juan G. Hincapie, Edward K. Chadwick, and Robert F. Kirsch
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
Abstract
Upper extremity neuroprostheses use functional electrical stimulation (FES) to restore arm motor
function to individuals with cervical level spinal cord injury. For the design and testing of these
systems, a biomechanical model of the shoulder and elbow has been developed, to be used as a
substitute for the human arm. It can be used to design and evaluate specific implementations of FES
systems, as well as FES controllers. The model can be customized to simulate a variety of pathological
conditions. For example, by adjusting the maximum force the muscles can produce, the model can
be used to simulate an individual with tetraplegia and to explore the effects of FES of different muscle
sets. The model comprises six bones, five joints, nine degrees of freedom, and 29 shoulder and arm
muscles. It was developed using commercial, graphics-based modeling and simulation packages that
are easily accessible to other researchers and can be readily interfaced to other analysis packages. It
can be used for both forward-dynamic (inputs: muscle activation and external load; outputs:motions)
and inverse-dynamic (inputs: motions and external load; outputs: muscle activation) simulations.
Our model was verified by comparing the model-calculated muscle activations to electromyographic
signals recorded from shoulder and arm muscles of five subjects. As an example of its application
to neuroprosthesis design, the model was used to demonstrate the importance of rotator cuff muscle
stimulation when aiming to restore humeral elevation. It is concluded that this model is a useful tool
in the development and implementation of upper extremity neuroprosthetic systems.
Keywords
shoulder; elbow; biomechanics; musculoskeletal model; functional electrical stimulation
1. Introduction
Neuroprosthetic systems restore function after spinal cord injury (SCI) using a combination of
functional electrical stimulation (FES) and reconstructive surgeries such as tendon transfers
(Keith and Lacey, 1991). An FES system consists of a controller that outputs the muscle
excitations needed for a particular task and electrodes that deliver the stimulation to the
appropriate paralyzed muscles. Upper extremity neuroprostheses to date have focused on
restoring hand function in individuals with mid to low cervical level injuries (C5–C8), using
open loop controllers that are tuned and tested using trial and error (Keith et al. 1988, Kilgore
et al. 1989). However, purely experimental methods are inefficient in cases of high cervical
*Corresponding author. Case Western Reserve University, Wickenden Building 119, 10900 Euclid Ave, Cleveland OH 44116, USA,
Tel: +12163688541, Fax: +12163684969, e-mail address: dimitra.blana@case.edu.
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NIH Public Access
Author Manuscript
J Biomech. Author manuscript; available in PMC 2009 January 1.
Published in final edited form as:
J Biomech. 2008 ; 41(8): 1714–1721. doi:10.1016/j.jbiomech.2008.03.001.
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level injuries (C1–C4), where almost all voluntary muscle function below the neck is lost. In
these cases, there are a large number of shoulder and elbow muscles that must be controlled
by the neuroprostheses, many of which generate moments about two or more degrees of
freedom. For example, the biceps muscle originates on the scapula and inserts on the radius,
which means it crosses the glenohumeral, humero-ulnar, and radio-ulnar joints. As a result,
the task of determining which muscles to stimulate and developing appropriate control schemes
becomes more complicated. Trial and error techniques would require extensive testing of all
possible muscle combinations, a time-consuming process for both the researcher and the FES
participant. The use of a musculoskeletal model can facilitate the design and testing of the
system, minimizing the inconvenience to the subjects.
There have been a number of computer models of the shoulder developed in the last few
decades, and most of them are strictly inverse. The first models were two-dimensional,
restricted to single-motion patterns, and did not include all the shoulder muscles (DeLuca and
Forrest 1973, Dul 1987). There are very few three-dimensional models, mostly because of the
complexity of the shoulder mechanism. The “Swedish” shoulder model was developed by
Karlsson and Peterson, based on morphological measurements by Hogfors et al (Karlsson and
Peterson 1992, Hogfors et al. 1987). The “Delft” model, by van der Helm, is based on
morphological data from Veeger et al (van der Helm 1994, Veeger et al. 1991, 1997). The
“Newcastle” shoulder model, described by Charlton and Johnson, is based on data from
Johnson et al., van der Helm and Veeger (Charlton and Johnson 2000). The only forward model
developed to date, by van der Helm and Chadwick, is still under development and has very
slow simulation speed (van der Helm and Chadwick 2002).
In this study, a new model of the shoulder and elbow was developed that can be used for both
inverse and forward dynamic simulations, thus providing a complete description of the upper
extremity. Verification of the model was achieved in an inverse dynamic manner, by correlating
the EMG activity in different muscles during arm movements performed by able-bodied
individuals, to the activation patterns predicted by inverse dynamic simulations using the
model. Finally, an example of the model use in neuroprosthesis design is presented. The model
was adjusted to simulate a C3 SCI individual, and the effect of two different sets of stimulated
muscles in the performance of a humeral abduction movement was investigated.
2. Methods
2.1 Model Construction
The muscle and joint parameters for the model were obtained from cadaver studies by Klein-
Breteler et al (1999). These parameters include the position of joint centers, inertial parameters
for body segments, the number of elements representing each muscle, and the optimal fiber
length, origin and insertion, tendon slack length, physiological cross-sectional area and
pennation angle of every element.
The software used to create the model was SIMM (Musculographics, Inc.), a graphics-based
system developed especially for musculoskeletal modeling. The model consists of six bones
(sternum, clavicle, scapula, humerus, ulna and radius) and five joints (sterno-clavicular,
acromio-clavicular, glenohumeral, humero-ulnar and radio-ulnar). The three joints at the
shoulder are ball joints, with three degrees of freedom each, while the elbow and forearm joints
are one-degree-of-freedom, single-axis joints. The model includes the so-called
scapulothoracic gliding plane (van der Helm 1994), which means that the medial border of the
scapula is constrained to maintain contact with the thorax.
The muscles are represented by one or more elements, depending on their size and the width
of their attachment site (Table 1). The model includes 29 muscles, with a total of 138 elements.
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If the muscle wraps over a bone surface, a “wrap object” is used to represent that surface such
that the muscle path is not allowed to pass through the bone.
In order to run dynamic simulations, two additional software packages were used. The first
package was SD/FAST (Parametric Technology Corp), which uses parameter files created by
SIMM to compute the equations of motion of the modeled system. The second package, the
Dynamics Pipeline (Musculographics, Inc.), connects SIMM to SD/FAST in order to perform
forward and inverse dynamic simulations. In the case of forward dynamic simulations, the
model inputs are muscle activations and possible external loads, and the output is the resulting
movement. In the case of inverse dynamic simulations, the desired movement and possible
external loads are the inputs, and the required muscle activations are the outputs.
In the second case, since there are more muscles than degrees of freedom in the musculoskeletal
system, a non-linear constrained optimization routine, CFSQP (AEM Design) is used in order
to solve the load-sharing problem (Tsirakos et al, 1997). Three types of constraints are included:
first, for every degree of freedom, the muscle forces are constrained to produce the torques
required by the input movement. Second, the muscle forces acting on the glenohumeral joint
are constrained to pull the humerus towards the glenoid cavity, thus ensuring the joint stability.
And third, the muscle forces acting on the scapula are constrained to keep the scapula pressed
against the thorax, in accordance with the scapulothoracic gliding plane, described earlier. The
objective function currently used was proposed by Praagman et al (2006) to minimize energy
consumption:
where E
f
and E
a
are the muscle energy consumption due to the detachment of cross bridges
and re-uptake of calcium respectively, m is the muscle mass, F
m
is the muscle force, PCSA is
the physiological cross-sectional area, F
max
is the maximum muscle force, and c
1
and c
2
are
two constants chosen to achieve equal contributions from the linear and nonlinear terms at 50%
activation (Praagman et all, 2006).
2.2 Experimental setup
Five able-bodied subjects participated in the study after giving informed consent. The subjects
were asked to perform a set of movements with their right arm, while their arm position and
the EMG signals of selected arm muscles were recorded. Figure 1 shows the experimental
setup.
The arm movements were recorded using an Optotrak system (Northern Digital Inc.) that
consists of three cameras capable of recording the 3D positions of light emitting diodes (LED)
located within the workspace. Sets of LED were fixed over the thorax, humerus and forearm
of the subjects to locate the three-dimensional position of these segments. The locations of the
scapula and clavicle are difficult to track dynamically using markers fixed to the skin, because
they show large displacements with respect to the skin. However, there is a well-defined
relation between scapular and humeral motion, which is that during a normal arm elevation
movement, about one-third is due to scapulothoracic motion, and two-thirds is due to
glenohumeral rotation (De Groot and Brand, 2001). Moreover, because of the closed chain
between the thorax, clavicle and scapula created by the sternoclavicular and acromioclavicular
joints, and the scapulothoracic gliding plane, the orientations of the clavicle and scapula are
related. Based on these relations, a three-dimensional regression model was built, using the
positions of scapular, clavicular, and humeral bony landmarks recorded in a series of static
trials covering the workspace. The bony landmarks were chosen according to van der Helm,
1997, and the recorded positions consisted of five levels of humeral elevation between 0° and
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120°, evenly distributed in that range, in the coronal (i.e. elevation to the side), scapular (45°
from coronal) and sagittal planes (to the front), with the elbow fully extended. During the
dynamic trials, the regression model was used along with the orientation of the humerus to
determine the dynamic orientation of the scapula and the clavicle.
EMG data were recorded in the first three subjects using surface electrodes on six muscles. In
order to evaluate the model predictions for more muscles, including ones too deep to be easily
accessible by surface electrodes, the last two subjects had surface electrodes placed on eight
muscles, and fine wire percutaneous electrodes placed on three muscles: infraspinatus,
supraspinatus and pronator teres. The list of muscles for each subject is shown in table 2. All
EMG signals were rectified, low pass filtered at a cutoff frequency of 4 Hz, and normalized
by their values during maximum voluntary contractions.
The same movements were performed by all subjects. They were relatively slow (joint
velocities around 30 degrees/second), in order to simulate the expected performance of a person
with an FES system. They consisted of a set of planar movements: shoulder abduction/
adduction in the coronal, scapular, and sagittal planes, horizontal flexion/extension, internal/
external rotation, elbow flexion/extension and forearm pronation/supination, reaching
movements to three different heights: knee level, shoulder level, and above shoulder level, and
two movements simulating activities of daily living (ADL): eating, and combing the hair. Data
were recorded at 30Hz.
2.3 Model evaluation
The model predictions were evaluated based on the shape and timing of the signals and not the
actual amplitude, because EMG amplitude depends on the properties and locations of the
electrodes used and therefore is not a reliable measure of muscle activation. For this reason,
the model was evaluated by cross-correlation of the model-predicted activations and the EMG
signals from the selected muscles.
In order to avoid correlating signals with very low amplitudes but (in the case of EMG) with
noisy baseline activity, muscles with EMG values that did not rise above a specified threshold
for the duration of a given trial were excluded from the correlation calculations for that trial.
The threshold was defined as one standard deviation above the baseline, as described in Hodges
and Bui 1996. For these cases, a different measure was used to summarize the ability of the
model to predict the lack of activity in a given muscle: the false positive rate. That is, the
number of trials in which muscle activity was predicted by the model but none was measured
using EMG was expressed as a percentage of the total number of trials where no activity was
measured using EMG.
2.4 Application to C3 SCI with and without FES
A spinal cord injury at the C3 level causes paralysis of almost all upper extremity muscles,
with the possible exception of the levator scapulae and upper trapezius (Kirsch et al, 2001).
To simulate a subject with C3 SCI, all other muscles were set to have zero active force, and in
addition, the maximum force for the levator scapulae and upper trapezius was set to 50% of
able-bodied maximum force to simulate the effects of possible muscle denervation and partial
paralysis (within the range of 20–60% measured by Kobetic and Marsolais, 1994).
Two sets of muscle candidates for FES were evaluated: the first included the serratus anterior
and deltoids, and the second included the serratus anterior, deltoids and three rotator cuff
muscles (supraspinatus, infraspinatus and subscapularis). To simulate FES, the maximum
forces of these sets of muscles were set to 50% of the corresponding able-bodied maximum
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muscle forces. This reduction simulates possible muscle denervation, disuse atrophy, and
limited spatial muscle activation due to electrode placement.
The task used to evaluate the effect of FES was abduction of the arm in the coronal plane from
0 to 100 degrees of elevation, with the elbow fully extended. Inverse dynamic simulations were
performed by the model under four conditions: (1) able-bodied, (2) C3 SCI, (3) C3 SCI with
FES of muscle set 1, and (4) C3 SCI with FES of muscle set 2. The maximum humeral elevation
for each case was determined either by the successful completion of the movement (i.e. the
model reached 100 degrees of elevation), or by the highest elevation achieved before the
simulation failed. In the case of failure, the model specified which constraint was violated: the
muscle forces were either insufficient for the required joint torques, they caused instability of
the glenohumeral joint, or they were unable to keep the scapula pressed against the thorax.
3. Results
Figure 2 demonstrates the evaluation procedure, using as an example, the middle deltoid of
subject 2 during a humeral abduction movement. It shows the raw EMG data (part A), the
processed EMG data (part B), the corresponding model-predicted middle deltoid activation
(part C), and a comparison of the processed EMG and model-predicted activation (part D). In
part D, it is shown that the timing of the measured EMG and the model-predicted activation
are quite similar. Also note that in part D, the magnitudes of the signals are not the same, but
this is not taken into account since the model verification is based on timing, not amplitude.
The cross-correlation in this case was 0.850.
Figure 3 shows an example of a typical correlation between EMG and model-predicted
activation. This is also a humeral abduction movement, and the muscles shown are the serratus
anterior (A), supraspinatus (B) and infraspinatus (C). These plots show that the model-
predicted activation peaks generally match the EMG peaks. The similarity of shape is reflected
in the cross-correlation values, which in this case were 0.610, 0.650 and 0.625 respectively.
Figure 4 shows a situation where the correlation between EMG signals and model predictions
is less accurate, for the biceps (A) and triceps (B) during a repeated elbow flexion/extension
movement. In this figure, the amplitudes were not scaled, to highlight the model-predicted
inactivity of the triceps, which does not agree with the active EMG measurement. The cross-
correlation values for the biceps and triceps were 0.391 and 0.290 respectively.
Figure 5 shows the cross-correlation values between activations and EMG signals for the
measured muscles of each of the five subjects, averaged across all tasks. Averaged across
subjects, the cross-correlation ranged from 0.282 for the triceps to 0.726 for the middle deltoid.
The overall mean correlation was 0.455. In most subjects, the three heads of the deltoid had
the best predictions, and the biceps and triceps had the worst. For the last two subjects that had
more EMG electrodes, the prediction was poor for the latissimus dorsi, but generally good for
the muscles that stabilize the scapula and the humerus: serratus anterior, supraspinatus and
infraspinatus.
The false positive rate is presented in Table 3. The lowest value was 23.3% for the triceps,
while the highest was 80% for the middle deltoid, but it should be noted that this was calculated
from only five trials, since the middle deltoid showed above-threshold EMG activity in all but
five trials. Overall, no muscle activation was systematically overestimated by the model.
Figure 6 shows the results of inverse dynamic simulations performed by the model adjusted
to represent an able-bodied subject, a C3 SCI individual without FES, and a C3 SCI individual
with FES of two different muscle sets. The maximum humeral elevation possible by the able-
bodied simulation was 100 degrees. Without FES, the model failed immediately (i.e., zero
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elevation) due to the extended paralysis at this level of injury. Simulation of FES of the serratus
anterior and deltoids produced humeral elevation of 12 degrees. For higher elevations, the
model failed because glenohumeral stability was not maintained. Addition of the rotator cuff
muscles to the simulation increased the maximum elevation to 81 degrees (i.e., almost to the
able-bodied range).
4. Discussion
A dynamic model of the upper extremity has been described. It was developed using a set of
commercially available software packages, with a user-friendly, graphical interface. It can be
used both for forward and inverse dynamic simulations. For the three heads of the deltoid,
which are primary arm movers, the model predicts activations that correspond to the timing of
the measured EMGs quite accurately. The same can be said for the serratus anterior and the
rotator cuff muscles (infraspinatus and supraspinatus), whose role is to stabilize the scapula
and the glenohumeral joint respectively. Our model can therefore accurately predict the
functions of key muscles of the upper extremity, making it a valuable tool for exploratory
simulations aiming to provide insight into the performance of the real arm under various
conditions. Specifically, this model can be a useful tool for the design and testing of upper
extremity neuroprosthetic systems.
Validation of musculoskeletal models is generally difficult to achieve. Thorough validation is
not practical because the muscle excitation patterns that are the model inputs are not directly
measurable in the actual musculoskeletal system. For his model of the upper extremity, van
der Helm compared the predicted muscle forces with electromyographical (EMG) recordings
(van der Helm 1994). The problem with this approach is that it did not take into account the
dependency of the EMG amplitude on muscle length. van der Helm concluded that EMG
amplitude cannot be used to validate musculoskeletal models, and he pointed out that in this
case, only verification, and not strict validation of the model is possible. Instead of EMG,
Praagman et al. (2003) suggested the use of local oxygen consumption in a muscle, measured
by near infrared spectroscopy (NIRS). However, the dynamic properties cannot be evaluated
with this method since NIRS cannot be used for dynamic measurements. In other studies,
evaluation of the model is performed by comparing predicted forces with measurements from
the literature. This method cannot give quantitative conclusions, since the modeling parameters
are different among the studies, but it can result in a qualitative agreement (Karlsson and
Peterson 1992, Charlton and Johnson, 2000).
In this study, the performance of the model was verified by comparing the shape over time of
the calculated muscle activations to the corresponding EMG recordings. Figure 3 shows that
the best correlation is achieved for the three heads of the deltoid, while for muscles like the
biceps and triceps, the agreement is not good. One reason for this might be co-contraction of
the biceps and triceps during the low velocity movements examined, something that would not
be predicted by the model using the selected energy-related cost function for load sharing.
Figure 4 shows an example of this. The EMG signals for the biceps and triceps reveal significant
co-contraction during flexion, as opposed to the model that does not predict any triceps activity,
most likely because in this task, extension can be achieved entirely through gravity. Since the
nervous system probably uses different strategies for different conditions, a cost function that
takes into account the stiffness of the arm could work better for this movement. However, an
FES system does not aim to replace the nervous system, so the choice of cost function, even
if not a perfect model of an intact nervous system, can still lead to muscle activation patterns
that, applied by the FES system, will produce the desired movement.
In some cases, disagreement between EMG and model-predicted activation can probably be
attributed to EMG measurement errors. The latissimus dorsi has a small cross-correlation value
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(0.310, averaged across subjects) and it would not be expected to be active unless the subject
pushes down or adducts against resistance (Jenkins 2002). Since neither of these actions were
included in the task set, it is likely that the surface electrodes detected EMG signals from
neighboring muscles such as the serratus anterior. In fact, a cross-correlation analysis of the
signals from latissimus dorsi and serratus anterior produces an average value of 0.886. This
could be due to poorly placed electrodes, or (more likely) to inter-muscle crosstalk, a common
problem when surface electromyography is used (Solomonow et al 1994).
Another possible reason for disagreement between EMG and model-predicted activation could
be that the cadaver-based model parameters do not correspond well to the participants of this
study. Parameters like the maximum muscle force cannot be directly measured in individual
subjects. However, it is unlikely that such differences will significantly impact the effectiveness
of model-based simulations in reaching general conclusions about the configuration of
potential FES systems. In the example of humeral abduction presented above, the model
predicted that including rotator cuff muscles in the stimulation set can greatly improve the
restored function. Even though the exact level of improvement may be different depending on
the specific person’s remaining function and possible denervation, the model results can be
used as a guideline for choosing the most effective muscle set for FES.
In conclusion, we believe that the model presented here can significantly facilitate the design
of neuroprosthetic systems, by replacing the current trial-and-error methods that would be
impractical in the case of high level injuries with a simulation system that can explore the
mechanical considerations associated with different movements and different stimulated
muscle sets. The model was successfully verified using EMG as a measure of muscle activation
and is now ready for use in aiding the development of advanced FES systems for restoring arm
and shoulder movements.
Acknowledgements
Peter Loan and Musa Audu, Ph.D. are acknowledged for their help with SIMM and optimization methods, respectively.
This study was funded by NIH NINDS N01 NS 1233.
References
Charlton, IW.; Johnson, GR. An interactive musculoskeletal model of the upper limb; Proceedings of the
3rd Conference of the International Shoulder Group; Newcastle upon Tyne, UK. 2000.
de Groot JH, Brand R. A three-dimensional regression model of the shoulder rhythm”. Clinical
Biomechanics 2001;16:735–743. [PubMed: 11714550]
DeLuca CJ, Forrest WJ. Force analysis of individual muscles acting simultaneously on the shoulder
during isometric abduction. Journal of Biomechanics 1973;6:385–393. [PubMed: 4732938]
Dul, J. Shoulder muscle load during work with elevated arms; Proceedings of the 11
th
International
Congress in Biomechanics; Amsterdam, the Netherlands. 1987.
Hodges PW, Bui BH. A comparison of computer-based methods for the determination of onset of muscle
contraction using electromyography. Electroencephalography and clinical Neurophysiology
1996;101:511–519. [PubMed: 9020824]
Hogfors C, Sigholm G, Herberts P. Biomechanical model of the human shoulder – I. Elements. Journal
of Biomechanics 1987;20(2):157–166. [PubMed: 3571296]
Jenkins, DB. Hollinshead’s functional anatomy of the limbs and back. 8th edition. Saunders; 2002.
Karlsson D, Peterson B. Towards a model for force predictions in the human shoulder. Journal of
Biomechanics 1992;25(2):189–199. [PubMed: 1733994]
Keith MW, Peckham PH, Thrope GB, Buckett JR, Stroh KC, Menger V. Funcional neuromuscular
stimulation neuroprostheses for the tetraplegic hand. Clinical Orthopeadics 1988;233:25–33.
Keith MW, Lacey EH. Surgical rehabilitation of the tetraplegic upper extremity. Journal of Neurologic
Rehabilitation 1991;5:75–87.
Blana et al. Page 7
J Biomech. Author manuscript; available in PMC 2009 January 1.
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A

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u
t
h
o
r

M
a
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r
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o
r

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s
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H
-
P
A

A
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h
o
r

M
a
n
u
s
c
r
i
p
t

Kilgore KL, Peckham PH, Thrope GB, Keith MW, Gallaher-Stone KA. Synthesis of Hand Grasp Using
Functional Neuromuscular Stimulation. IEEE Transactions on Biomedical Engineering
1989;36:761–770. [PubMed: 2787284]
Kirsch RF, Acosta AM, van der Helm FCT, Rotteveel RJJ, Cash LA. Model-based development of
neuroprostheses for restoring proximal arm function. Journal of Rehabilitation Research and
Development 2001;38:619–626. [PubMed: 11767969]
Klein-Breteler MD, Spoor CW, van der Helm FCT. Measuring muscle and joint geometry parameters of
a shoulder for modeling purposes. Journal of Biomechanics 1999;32:1191–1197. [PubMed:
10541069]
Kobetic R, Marsolais EB. Synthesis of paraplegic gait with multichannel functional neuromuscular
stimulation. IEEE Transactions in Rehabilitation Engineering 1994;2:66–79.
Praagman M, Veeger HE, Chadwick EK, Colier WN, van der Helm FCT. Muscle oxygen consumption,
determined by NIRS, in relation to external force and EMG. Journal of Biomechanics 2003;36:905–
912. [PubMed: 12757798]
Praagman M, Chadwick EK, van der Helm FCT, Veeger HE. The relationship between two different
mechanical cost functions and muscle oxygen consumption. Journal of Biomechanics 2006;39:758–
765. [PubMed: 16439246]
Solomonow M, Baratta R, Bernardi M. Surface and wire EMG crosstalk in neighboring muscles. Journal
of Electromyography and Kinesiology 1994;4:131–142.
Tsirakos D, Baltzopoulos V, Bartlett R. Inverse optimization: functional and physiological considerations
related to the force-sharing problem. Critical Reviews in Biomedical Engineering 1997;25:371–407.
[PubMed: 9505137]
van der Helm FCT. A finite element musculoskeletal model of the shoulder mechanism. Journal of
Biomechanics 1994;27:551–569. [PubMed: 8027090]
van der Helm, CFT. A standardized protocol for motion recordings of the shoulder; Proceedings of the
1
st
Conference of the International Shoulder Group; Delft, the Netherlands. 1997.
van der Helm, FCT.; Chadwick, EK. A forward-dynamic shoulder and elbow model; Proceedings of the
4
th
Conference of the International Shoulder Group; Cleveland, OH, USA. 2002.
Veeger HE, van der Helm FCT, can der Woude LH, Pronk GM, Rozendal RH. Inertia and muscle
contraction parameters for musculoskeletal modelling of the shoulder mechanism. Journal of
Biomechanics 1991;24:615–629. [PubMed: 1880145]
Veeger HE, Yu B, An KN, Rozendal RH. Parameters for modeling the upper extremity. Journal of
Biomechanics 1997;30:647–652. [PubMed: 9165401]
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Figure 1.
Experimental Setup. Sets of LED were fixed over the thorax, humerus and forearm of the
subjects, and the three-dimensional position of these segments was recorded using three
cameras. The positions of the clavicle and scapula were estimated using a regression model
based on the scapulohumeral rhythm. Surface and percutaneous electrodes measured the EMG
signals from selected shoulder and arm muscles.
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Figure 2.
EMG recording, processing, and comparison to model-predicted activations. For example, the
EMG and model-predicted activations of middle deltoid muscle during a repeated humeral
abduction movement are illustrated. Raw EMG (A), processed (rectified, low pass filtered at
4Hz and normalized) EMG (B), model-predicted activation (C), processed EMG (black line)
and model-predicted activation (grey line) (D). For part D, the vertical axis for the EMG is on
the left, and for the activation is on the right.
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Figure 3.
Serratus anterior (A), supraspinatus (B), and infraspinatus (C) signals during a repeated
humeral elevation movement. For all muscles, the processed EMG signal is the black line, and
the model-predicted activation is the grey line. The vertical axes for the EMG signals are on
the left, and for the model-predicted activations are on the right.
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Figure 4.
Biceps (A) and triceps (B) signals during a repeated elbow flexion/extension movement. The
processed EMG signal is the black line, and the model-predicted activation is the grey line.
Co-contraction of the biceps and triceps is recorded in the EMG signals, but is not predicted
by the model.
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Figure 5.
Cross-correlation between EMG and model-predicted muscle activations, averaged across
tasks. Every row corresponds to one muscle, and every column to one subject. The vertical
scale of every plot ranges from 0 to 1.
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Figure 6.
Maximum humeral elevation achieved in four cases: (a) able-bodied (b) C3 SCI (c) C3 SCI
with FES of serratus anterior and deltoids (d) C3 SCI with FES of serratus anterior, deltoids,
infraspinatus, supraspinatus and subscapularis.
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Table 1
The muscles included in the model, and the number of elements that represent each one.
Muscle Number of elements Muscle Number of elements
Trapezius, scapular part (mid and
lower)
11 Biceps, long head 1
Trapezius, clavivular part (upper) 2 Biceps, short head 2
Levator scapulae 2 Triceps, long head 4
Pectoralis minor 4 Triceps, medial head 5
Rhomboid 5 Triceps, lateral head 5
Serratus anterior 12 Latissimus dorsi 6
Deltoid, scapular part (post and
mid)
11 Pectoralis major, thoracic part 6
Deltoid, clavicular part (anterior) 4 Pectoralis major, clavicular part 2
Coracobrachialis 3 Brachialis 7
Infraspinatus 6 Brachioradialis 3
Teres minor 3 Pronator teres 2
Teres major 4 Supinator 5
Supraspinatus 4 Pronator quadratus 3
Subscapularis 11 Anconeus 5
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Table 2
Electrodes used to measure EMG signals for correlation to model-predicted activations. In the first three subjects, only
surface electrodes were used, placed on six muscles. In the last two subjects, eight surface and three fine wire
percutaneous electrodes were used.
subjects S1 and S2 S3 S4 and S5
surface biceps biceps biceps
electrodes triceps triceps triceps
anterior deltoid anterior deltoid anterior deltoid
middle deltoid
posterior deltoid posterior deltoid posterior deltoid
upper trapezius upper trapezius upper trapezius
pectoralis major pectoralis major
latissimus dorsi
serratus anterior
percutaneous infraspinatus
electrodes supraspinatus
pronator teres
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Table 3
False positive rate. The first column is the number of trials in which muscle activity was predicted by the model but
none was measured using EMG. The second column is the total number of trials where no activity was measured using
EMG. The third column is the false positive rate, defined as the ratio of the first two columns.
EMG off act. on Total EMG off False positive rate (%)
anterior deltoid 5 13 38.5
middle deltoid 4 5 80.0
posterior deltoid 20 33 60.6
biceps 7 25 28.0
triceps 10 43 23.3
upper trapezius 14 30 46.7
pectoralis major 17 48 35.4
latissimus dorsi 9 34 26.5
serratus anterior 6 15 40.0
supraspinatus 4 14 28.6
infraspinatus 11 41 26.8
pronator teres 8 25 32.0
Total 115 326 35.3
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