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Electromyography is an important parameter to correctly evaluate dynamic muscle strength in gait analysis

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  • Save American Journal of Biomedical En gineer in g 2012, 2(6): 269-277 DOI: 10.5923/j.ajbe.20120206.06 Electromyography as an Important Parameter for a Proper Assessment of Dynamic Muscles Strength in Gait Analysis Emiliano P Rave ra1,2,*, Paola A Catalfamo1,2, Marcos J Crespo3, Arie l A A Braidot1 1Laboratory of Biomechanics, National University of Entre Ríos, Oro Verde, 3101, Argentina 2National Council of Scientific and Technical Research, Buenos Air es, C1033AAJ, Argentina 3Gait and M otion Analysis Laboratory, FLENI Institute for Neurological Research, Escobar, B1625XAF Argentina Abstract Normal gait is a functional movement involving the most efficient energy transfer. Cerebral Palsy describes a group of developmental d isorders of movement and posture causing activity restriction or d isability. It represents one of the most common cause of physical disability in ch ildren. Gait analysis provides great contributions to the understanding of gait disorders originating fro m musculoskeletal abnormalit ies. In these patients provides a mean for a mo re co mprehensive treatment plan. At this point, the potential and necessity of using correct biomechanical models that consistently study the abnormalities beco me clear. Reinforcing and correcting a simp le gait analysis and eliminating the unknowns when selecting the appropriate treatment are crucial in clin ical settings. In this paper a musculoskeletal th igh model with a simple rescaling method using subject-specific anthropometric data is presented. The thigh model estimates muscle forces of the six muscles through the walking based in static optimization without including electromyography data as input. We used the model proposed for identify the muscles strength involved in norma l and pathological gait. The forces obtaining for norma l subjects achieved high concordance with their electro myography data and outcomes to other musculoskeletal model recently presented. In other hand, in patients with cerebral palsy with crouch gait the outcomes of our musculoskeletal model don't show a correct concordance with their electro myography data. For this reason we get on the conclusion that take electro myography data became important in modeling a pathological gait as crouch gait. Keywords Musculoskeletal model, Dynamic muscles strength, Crouch gait 1. Introduction Norma l ga it is a functional move ment involving the most efficient energy transfer. Similarities between subjects are common but there are also small individual variations, so any deviation detected by comparing gait patterns are of great value. Cerebral Palsy (CP) describes a group of developmental disorders of movement and posture causing activity restriction or disability, wh ich are attributed to disturbances occurring in the fetal or in fant brain. It represents one the most common cause of physical disability in modern world and within the pediatrics orthopedics units[1][2][3]. Ga it an aly s is p ro v id es g reat co nt rib u t io ns t o th e understanding of gait disorders in CP. It allo ws an objective quantification of the causes of gait disorders orig inating fro m musculoskeletal abnormalit ies in patients with CP and it * Corresponding author: (E P Ravera) Published online at Copyright © 2012 Scientific & Academic Publishing. All Rights Reserved demonstrates superiority to the visual or observational analysis of movement[1]. It also provides a mean for a mo re co mprehensive treatment plan, including or excluding surgical procedures that can potentially decrease the number of surgical interventions in the life o f a patient with CP[4][5][6]. In general the abnormal gait pattern of patients with CP are the result of primary problems (as deficient selective motor control, abnormalities of balance and abnormal central nervous system tone drive); secondary abnormalities characterized as growth disorders (for example growth of bone and muscles growth) and tertia ry abnormalities as those compensations that the individual uses to circumvent the other abnormalities of gait [5]. So, much of the difficulty encountered in studying pathologic gait involves the separation of the true pathology. However, good treat ment demands their separation because, to optimize the efficiency of gait, we must correct the former and not interfere with the latter[7]. Thus, the models could improve recommendations for orthopaedic surgery, based on a quantitative description of how to modify muscle force generation and how these 270 Emiliano P Ravera et al.: Electromyography as an Important Parameter for a Proper Assessment of Dynamic M uscles Strength in Gait Analysis modifications affect the action of the muscles during crouch gait[8][9]. One of the most co mmon abnormalit ies in children with CP is crouch gait, characterized by excessive knee flexion during the termina l swing phase and initial loading response, and also increased dorsiflexion during the whole gait cycle. However, it is thought that this abnormality may be due to a variety of neuromuscular disorders which are not easily detectable. Crouch gait is common ly treated by a combination of orthopaedic procedures with the objective of diminishing the excessive knee flexion and imp roving the walking pattern[10] [11][12]. So, this pathological gait needs to be treated since the flexion angle at the knee and hip joint tend to increase with age, due to increased body weight, which co mpro mises the ability for independent walking[11]. Methods of treatment to decrease spasticity allows the child to have greater range of motion, less spastic response to stretch, and better potential to develop using voluntary muscle activ ity during gait. So, surgical procedures to muscle lengthening are used but insufficient muscle strength can be a major cause of ongoing disability[5]. However, the causes of an appropriate treatment for gait abnormalities are difficult to determine because the movements generated by muscular forces of patients are not completely resolved and clearly understood yet[13][14]. Hence the exp licit identification of the anomaly does not necessarily present a direct solution[15]. Due to the mechanical redundancy displayed by the human loco motor system, activ ities such as walking can be performed in d ifferent ways and in many comb inations of muscle forces wh ich can generate the same net joint mo ment. However, there are similarit ies in the muscle activation patterns (MAP) of different people performing the same well-learned task[16]. Experimental measurements of muscle forces during loco motion of animals showed that the MAP are in general stereotyped[17]. These consistencies suggest that the central nervous system uses specific princip les for the control of indiv idual muscle forces, and that principles are the same for different persons. That leads to the hypothesis that central nervous systemselects optimal MAP with a set of criteria that are still unknown[16]. Assuming that the central nervous system minimizes stress on the muscles in the body, leads to the hypothesis that muscle strength may be found as the solution to an optimization problem. A treatment plan is further complicated because there is not clear scientific basis to determine how the neuro-muscular-skeletal impairment contributes to the abnormal movement[18]. Abnormalit ies of the knee may be related to dynamic conditions such as spasticity, secondary static muscle contractures of the hip, knee and ankle, or a combination of short and spastic muscles. A correct determination of the etiology of abnormal patterns of the knee is the key to select the appropriate therapy[19], presenting a major challenge at present since there is not sufficient theoretical basis to determine the biomechanical causes of abnormal gait of these patients[20] [21][22]. At this point, the potential and necessity of using correct biomechanical models that consistently study the abnormalities become clear. Reinforcing and correcting a simp le gait analysis and eliminating the unknowns when selecting the appropriate treatment are crucial in clinical s ettin gs [23]. The main limitations of the musculoskeletal models are that, a) they are anatomically and physiologically incomp lete; b) they show insufficient accuracy in the estimation of the used parameters; c) it is difficult to validate them. A musculoskeletal thigh model with a simple rescaling method using subject-specific anthropometric data to fit the parameters is proposed in this study. The model estimates muscle forces fro m six muscles of the thigh during walking. The model uses a quadratic approximation method algorithm to find the solution of a non-linear mathematic program based on static optimization. Electro myography of each subject under analysis was use to correlate it with the muscles strength modeled as a validation procedure for the outcome of the proposed musculoskeletal model. Also, the outcomes of the presented model fro m unimpaired subjects were compared with results published in the literature. Finally, the model was used to identify muscles strength involved in pathological gait o f CP patients who presented crouch gait. 2. Method 2.1. Participants and Procedure CP and control group (CG) were recruited for the study. The study included a first group of 10 ambulatory patients with CP, age 8– 23 years, 1.50–1.94 m in height and 49–105 kg in weight and the second group included five unimpaired subjects, age 7–11 years, 1.24–1.67 m in height and 22– 53 kg in weight (Table 1). Table 1. Description of participants, age, height, weight and spat iot emporal gait paramet ers, all present ed as mean (min–max) Age (years) Height (m) Weight (kg) Velocity (m/seg) Stride length (m) Cadence steps/min Stance phase (%) CP n=20 trial 13.5 (8-23) 1.45 (1.08-1.83) 42.3 (25-79) 0.97 (0.55-1.35) 0.97 (0.66-1.38) 121 (103-141) 60.83 (54-68) CG n=10 trial 9.8 (7-14) 1.42 (1.24-1.67) 36.6 (22-53) 1.10 (0.97-1.28) 1.11 (1.07-1.18) 117.2 (105-133) 58.5 (57.5-59.5) p-Value 0.1172 0.7463 0.5315 0.6527 0.0001 0.7446 0.6839 Several parameters are beco ming the standard description of the gait of ch ildren with CP as it enables clinicians and researchers to have a common language[24]. American Journal of Biomedical Engineer ing 2012, 2(6): 269-277 271 Gross Motor Functional Classification Scale (GMFCS), provides a means of describing children's loco motion function ranging from running and walking independently to requiring assistance to move a wheelchair. The Functional Mobility Scale (FMS) to further classify children who are ambulatory based on their walking ability at 5 meters, 50 meters, and 500 meters. The Functional Assessment Questionnaire (FAQ), ask children and their caregivers a variety of questions aimed at determining the child's ability to walk short distances as well as manage common a rchitectural barrie rs. Another tool, approaches using quantified motion analysis. The Gillette Gait Index (GDI), is a system of normalcy of gait using deviations fro m the normal walking patterns derived fro m three-dimensional gait analysis. By co mbing, the qualitative and quantitative informat ion that show these parameters provide a clear picture of the ambulatory capabilit ies of these patients, see Table 2. filter with a cutoff frequency of 6 Hz and order 8. Muscles electrica l activity data was recorded using surface dynamic electro myography (Teleemg BTS Bioengineering, Italy). The eight channels of acquisition were used with a sampling frequency of 2000 Hz for normal subjects and patients with CP. Electro myography data was rectified and filtered with a low-pass Butterworth filter with a cutoff frequency of 6 Hz and order 2, and their peak value was used for normalizat ion. Anthropometric measurements (height, weight, leg length, knee jo int width and distance between anterior superior iliac spines (ASIS)) for each part icipant were recorded by an experienced physiotherapist. 2.3. Musculoskeletal Model Figure 1 show the musculoskeletal mechanical system proposed in this study. This model consists of three segments (pelvis, femu r and tib ia) and six muscles. Table 2. Generals funct ional characterist ics of gait patt erns of pat ient s P at ient P at ient s 1 2 FAQ 7 9 Functional Range GMFCS FMS GDI (right/left) II 6.5.5 75.39 / 71.46 II 6.6.5 72.85 / 68.10 3 8 II 6.5.N 64.21 / 69.04 4 8 II 6.6.5 68.80 / 68.98 5 8 III 5.5.4 62.12 / 67.61 6 9 7 9 8 9 II 5.4.N 60.45 / 62.78 I 6.6.6 71.99 / 78.79 I 6.6.6 66.39 / 72.51 9 10 10 9 II 5.5.5 71.27 / 71.54 II 6.6.5 67.45 / 71.55 The selection criteria fo r patient with CP were : Gross Motor Function Classification System (GMFCS) levels 1, 2 or 3 (ambulant without walking aids), no orthopaedic surgery or botulinu m to xin treat ment within the last 6 months, diagnosis of symmetric Spastic Diplegia (SD) and walked with crouch gait ( ≥ 25 degree of knee flexion in mid stance). Data fro m both, healthy and CP populations were provided by the Gait and Movement Laboratory at FLENI Institute for Neurological Research, Escobar, Argentina. The Hospital Research Ethics Co mmittee reviewed and approved this study. The protocol was explained to the subjects and a consent form was signed by the subjects or their carers. 2.2. Data Collected Kinemat ic data was recorded by a mot ion capture system (Elite 2002 BTS Bioengineering, Italy) with 8 different cameras (100 Hz) and two force plates (Kistler 9281E, Kistler Group, Swit zerland). Twenty two retro-reflective skin markers were placed over bony landmarks (as indicated by the Davis protocol[25]). After measurement, all data was imported into MATLA B (MathWorks, Natick, MA). Marker trajectories were filtered with a zero -lagged Butterworth Figure 1. Scheme of musculoskeletal thigh model Four of the muscles were considered biarticular: Tensor Fascia Lata (TFL), Semimembranosus (Semi), Sartorius (S) and Rectus Femoris (RF) and two were modeled as monoarticular: Gluteus (G) and Iliac (I). The model has six degrees of freedom (DoF), three fo r the hip joint and three for the knee. The selection criteria used for selecting the muscles included in our musculoskeletal model were: ● Antagonistic muscles with biart icular actions, and major physiological cross section area (PCSA). ● Monoarticular muscles such as the gluteus and iliac because they have great importance in the movement of the hip. The model used estimations of the patches of origin and insertion and the directions of the muscles were assumed to be linear between the origin and insertion sites, see Table 3. 272 Emiliano P Ravera et al.: Electromyography as an Important Parameter for a Proper Assessment of Dynamic M uscles Strength in Gait Analysis The positions of the patches of the six muscles were used fro m the estimated ones by using cadaveric references and axis systems embedded on and lin ked to the movement of the pelvis and the leg respectively[26]. The multi segment model used in this study was based on a simp le rescaling method, by adjusting the parameters using subject-specific anthropometric data. Table 3. Musculoskeletal morphological parameters. Patch estimation as percent ages of the anthropometric dat a (ASIS length or knee diameter). (*) Proportional to the height, (**) Mid position between anterior and posterior iliac superior spine, (***) 7% within the origin of the glut eus P CSA cm 2 S 8.8 RF 28.9 TFL 5.9 Sem 15.75 G 49.35 I 16.25 Origin (%) X Y Z 11.4 26.6 12.5 11.4 13.3 6.3 11.4 26.6 12.5 22.2 18.2 0.0 ** ** ** *** *** *** Insertion (%) X Y Z 31.3 17.7 3.7 35.2 0.0 0.0 3.0 15.2 20.8 21.3 17.7 3.7 31.2 6.3 * 15.5 31.2 8.3 * 21.5 For calculate the time-varying muscle force vectors a line action is assumed. The model assumes that the force transmission by the muscle acts along a straight line connecting equation (1) the def inpeodinτtrsik of as origin and insertion. So, the unit muscle force vector the for the ith muscle[26]. τrik = r rro / i ro / i = r rri ri − − r rro ro (1) A single straight-line model between the anatomical origin and insertion is not adequate for the Rectus Femo ris because the patella acts as an important lever in the knee joint. In this case the insertion is re-calculated as the difference between a vector fixed to the tibia and another fixe to the femu r (Figure 1). Mathematically the direct ion of real act ion is calculated asr seen irn (2), r rpatella = rtibia + rfemur rr rtibia + rfemur (2) The time-varying muscle mo ment may be expressed as the mo ment generated r by amrun=itrrfio×rcτreik(3). (3) wrriheisrethemloicsatthioenmoof ment associated with ith muscle origin/ins the ith mus ertion with cle res and pect to the joint center[27]. 2.3.1. Individual Muscle Strength Dynamics We define muscle strength dynamics as forces that are developed for the individual muscles throughout the gait cycle. A static optimizat ion was used to calculate the muscle strength for each samp le independently (4). Thus, muscle forces are constrained by two physiological limitations: muscles may provide only contraction forces and these forces are limited to a maximu m. ( ) Min f G fi(m) restric ∑6 f i( m )τrik r ≤ Fk i =1 ; k = 1,2 (4) ∑ ( ) 6 fi(m) r ri ×τrik r ≤ Mk ; k = 1,2 i =1 r r f (m) i ≥ 0 ; i = 1,2,L6 were Fk and M k are the intersegmental joint force and net joint mo ment and f (m) i is the norm of ith muscle. The Optimization problem have inequality constraints because only a limited g roup of muscles of the th igh were modeled and also the forces developed by ligaments and cartilage of the joints were neglected. The optimization problem was solved through the MATLAB Optimization Toolbo x. The “fminco m” function that finds a constrained minimu m of a scalar function of several variab les starting at an initial estimate was used. This is generally referred to constrained nonlinear optimization or nonlinear programming (NLP). The algorith m used in solving the NLP is the successive quadratic approximat ions, which is based on successive approximations to the Lag rangian function. Appro ximation to the Hessian matrix is given by the method of Broyden-Fletcher-Go ldfarb-Shanno (BFGS) and using the approximation of Sherman-Morrison-Woodbury leads to the expression of the inverse of the Hessian (5). were vector skr = xk Bk +1 = r xk +1 − r xk Bk − and Bk sk skT Bk + yk ykT skT yk Bk = sk ∇L( r xk ykT +1 ) sk − ∇L( r xk represents the forces at the time k ) , (5) . The wh o s e ele ments ∇L( r xk ) are the norm of is the gradient muscles strength fi(m) . The of the Lagrangian function vector at the time k . This method is considered one of the more effect ive quasi-Newton methods, showing an overall superlinear convergence, high robustness in problems where the objective function is nonlinear, satisfying the secant rule and ensuring that the Hessian matrix is positive semidefinite in resolution quadratic problem[28]. Among the objective functions used in static optimizat ion in gait, currently the use of polynomials criteria stands up. A polynomial optimizat ion function of third order with normalizat ion factor which in this case was Muscle PCSA was implemented (6). The physiological behavior behind this criterion is to min imize muscle fat igue[16]. ( ) ∑ G f ( i m) = 6  f (m) i i=1  PSCAi 3  (6) American Journal of Biomedical Engineer ing 2012, 2(6): 269-277 273 This static optimization problem was restricted with the tridimensional net total torque of the six muscles actuating on the joints of the knee and hip. Polynomial functions may describe physiological conditions. They require the use of comp lementary restrictions that prevent individual muscle forces fro m exceeding their physiological maximu m when the external load increase in normal subjects, or under abnormal or pathological conditions of internal loads. As pathological conditions of internal load are expected in patient with SD CP, muscular forces were restricted by two physiological limitat ions: ● Muscles only can develop contraction forces. ● These contractile forces have a maximu m physiological limit. The cost functions used in optimization problems that represent muscle fatigue and metabolic cost as the polynomial, can pred ict: (i) the reciprocal coactivation of one-joint antagonist muscles; (ii) the coactivation of one-joint synergists with their two-jo int antagonist; (iii) the simu ltaneous activation of synergists crossing the same joints and (iv) a strong relationship between force and activation of two-joint muscles and mo ments at the two joint[16]. 3. Results The net joint mo ments of patients and CG obtained fro m the lin k model segment are within the values shown in the literature (Figure 2)[5][28]. These results are used as input for the static optimizat ion problem in the healthy subjects and patients with CP. Figure 3 shows the norm of muscle forces throughout the gait cycle found by our model for subjects with and without pathological movement. This model shows a good relationship between the data of electro myography activity in different muscle groups and the estimation of dynamics muscle strength for unimpaired subjects, see Figure 4. Also seen in Figure 4 that the model respects the areas where these muscles are very important in driving. The results of the model show that Semimemb ranosus muscle is ma inly activated during stance and at the end of swing phase, which coincides with the literature[29]. Figure 5 shown that our model presents good concordance in the results observed in healthy subjects recently presented by other authors[8], in areas where forces have achieved their maximu m level. The main differences between the two models are given in the Rectus Femoris (Figure 5). Van der Krogt et al.[8], show that in the end of stance phase there is a peak above 80% of maximu m value. Th is is not observed in the present model. However, the model proposed in this study shows that the Rectus Femoris presents a peak o f strength at the end of the swing phase. In the other hand at the same mo ment the Rectus Femoris contracts in comb ination with its antagonistic muscles that result in a soft extension knee jo int, which coincides with the electro myography data recorded (Figure 4). Fi gure 2. Mean and standard deviat ion of moment of hip, knee and ankle joint s of pat ient s with CP (red line) and normal subject s (blue line) 274 Emiliano P Ravera et al.: Electromyography as an Important Parameter for a Proper Assessment of Dynamic M uscles Strength in Gait Analysis Fi gure 3. Mean and standard deviat ion of Norm of muscles strengt h dynamics for healthy subject s and pat ient with crouch gait Figure 4. Norm of muscle strength and electromyographic activity of healthy subjects. Rectus Femoris and Semimembranosus (red line- grey shadow) versus the EMG (green-black bars) normalized to their maximum American Journal of Biomedical Engineer ing 2012, 2(6): 269-277 275 Fi gure 5. Norm of muscle forces t hroughout the gait cycle compared with the result s observed by ot her musculoskelet al model [8]. These result s are normalized to the maximum value for comparison. Blue lines represent our model and Van der Krogt et al. model in red lines Figure 6 shows the mean and standard deviation of muscles strength dynamics of the Rectus Femo ris and Semimembranosus throughout the gait cycle obtained by the model and the electro myography data for patients with crouch gait. Figure 6. Norm of muscle strength and electromyographic activity of patients with CP. Rectus Femoris and Semimembranosus (red line- grey shadow) versus the EMG (green-black bars) normalized to their maximum We see that in spite of the similarities shown between these relationships decreased in patients with neurological muscle strength dynamics and EM Gs data in healthy subjects, diseases such as CP who develop crouch gait. 276 Emiliano P Ravera et al.: Electromyography as an Important Parameter for a Proper Assessment of Dynamic M uscles Strength in Gait Analysis An increase respect CG in the knee extensor mo ment may be due to an increase in the passive force generated by the hamstrings (Figure 2)[3]. A knee fle xion during all phases of the patient's gait may have been caused by a co-contraction of the Hamstrings and Rectum Femoris. Fro m these results, it is possible to see that the muscle groups that act during the flexion of knee jo int, such as Hamstrings and Tensor Fascia Lata were decreased in CP patients respect to CG and the strength of Rectus Femoris was increased throughout the gait cycle (Figure 3). Also, as shown in the Figure 6, the muscles strength dynamics of this patient's not present a good similitude with their EM Gs data recorded. These results lead to be noted that in patients with gait disorders this parameter can be critical for a determination of the accurate value of muscles strength through the gait cycle. 4. Conclusions Information about level and d istribution of dynamic muscle strength in unimpaired subjects and children with CP would help to better understand the action of the musculoskeletal system that generates the net mo ment jo ints. One limitation of this model is the limited nu mber of modeled muscles. Also, it has been assumed that each muscle generate fo rces only in the direction of a single fiber that is in the center of the muscle. Electro myography data was not used for limiting the timing of the activity of the muscles. This simplification was adopted because it is not possible to record the electro myography signal fro m all modeled muscles in this work. However, the recorded signals (EM Gs) of several muscles were co mpared with the dynamics muscle strength estimate by the model. For the unimpaired group, the lower limb strength calculated in all modeled muscle groups presented a stereotyped morphology, as seen from the similar morphology presented by all healthy subjects (Figure 3), in accordance with previous studies[16]. Also, for this group, muscle forces presented expected physiological features such as a delay between dynamic strengths and EMG data recorder. This delay is related to the fact that total strength is the sum of the recruit ment of motor units, and the motor unit action potential reaches the maximu m 40 to 100 milliseconds later to the electrical activation recorder[24]. The forces obtained by this model achieved high concordance with the electro myography data and other musculoskeletal model[8]. Hence, it is considered that the forces estimated for healthy subjects by this model consistently approximate the actual forces acting in hip and knee jo ints. Hicks et al.[13], demonstrated that crouch gait reduces the capacity of muscles to extend the hip and knee joints in stance phase. As a consequence, muscles must work harder to maintain a particu lar limb position, which helps to explain the increase in energy expenditure when walking in a crouched posture. Also previous simu lations indicate that crouch gait is characterized by larger muscle forces due to support body weight and propel the body forward throughout single-limb s tan ce[30 ]. Our musculoskeletal model showed only an increased strength in the Rectus Femoris and a decrement on the other modeled muscles, but little similarity with EM Gs data (Figure 6) in contrast with these facts. This may be related to the fact that in this model does not take account the electro myography as an input parameter to restrict the value of dynamics muscles strength. For this reason we get on the conclusion that take electro myography data became important in modeling a pathological gait as crouch gait. So, the inclusion of electromyography data in the modeling of muscles strength behavior in patients with pathological gait as CP will he lp to look for the increases in the co-contraction associated with their degree of spasticity of some muscles. However, despite of the simp licit ies that present our model, in the number of modeled muscles and the lack of EM Gs data; in healthy subjects there were satisfactory results. It demonstrates that in these subjects the EM G is not a critical parameter to include in the musculoskeletal model to find the dynamics muscles strength. This study showed a musculoskeletal thigh model with knee and hip joint (6 DoF and six muscles) that despite of the simp lifications and our naive model there were good results in the subjects without gait pathologies. Future work will focuses on the inclusion of electro myography data, more muscles and DoF to achieve to a better understanding of muscle behavior in patients with CP. Anyway the main problem is to record EM Gs data for all modeled muscles in c linica l practice. ACKNOWLEDGEMENTS The authors would like to thank FLENI Institute for Neurological Research for providing data fro m patients and healthy subjects and to the National Council of Scientific and Technical Research (CONICET), PICTO 222-2009 (A GENCIA) and PID 6125 (UNER) to provide the funds needed for this research. REFERENCES [1] U. G. Narayanan, “The role of gait analysis in the orthopaedic management of ambulatory cerebral palsy.,” Current opinion in pediatrics, vol. 19, no. 1, pp. 38–43, Feb. 2007. [2] J. M . Quinby and A. Abraham, “M usculoskeletal problems in cerebral palsy,” Current Paediatrics, vol. 15, no. 1, pp. 9–14, Feb. 2005. American Journal of Biomedical Engineer ing 2012, 2(6): 269-277 277 [3] Z. Karim, A. Kranzl, M. Gfoehler, and M . G. Pandy, “Biomechanics of a crouched gait caused by spastic cerebral palsy in children,” 8th World Congress on Computional M echanics, pp. 4–5, 2008. [4] B. Lofterød, T. Terjesen, I. Skaaret, A.-B. Huse, and R. 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