SENSITIVITY OF MODEL-PREDICTED MUSCLE FORCES OF PATIENTS WITH CEREBRAL PALSY TO VARIATIONS IN MUSCLE-TENDON PARAMETERS

2021 ◽  
Vol 21 (01) ◽  
pp. 2150008
Author(s):  
YUNUS ZIYA ARSLAN ◽  
DERYA KARABULUT
Keyword(s):  
Cerebral Palsy ◽  
Modeling And Simulation ◽  
Muscle Fiber ◽  
Fiber Length ◽  
Muscle Force ◽  
Musculoskeletal Modeling ◽  
Healthy Children ◽  
Muscle Forces ◽  
Healthy Individuals ◽  
Slack Length

Computational musculoskeletal modeling and simulation platforms are efficient tools to gain insight into the muscular coordination of patients with motor disabilities such as cerebral palsy (CP). Muscle force predictions from simulation programs are influenced by the architectural and contractile properties of muscle-tendon units. In this study, we aimed to evaluate the sensitivity of major lower limb muscle forces in patients with CP to changes in muscle-tendon parameters. Open-access datasets of children with CP ([Formula: see text]) and healthy children ([Formula: see text]) were considered. Monte Carlo analysis was executed to specify how sensitive the muscle forces to perturbations between [Formula: see text]% and [Formula: see text]% of the nominal value of the maximum isometric muscle force, optimal muscle fiber length, muscle pennation angle, tendon slack length, and maximum contraction velocity of muscle. The sensitivity analysis revealed that muscle forces of CP patients and healthy individuals were most sensitive to perturbations in the tendon slack length ([Formula: see text]), while forces of CP patients were more sensitive to tendon slack length when compared to the healthy group ([Formula: see text]). Muscle forces of patients and healthy individuals were insensitive to the other four parameters ([Formula: see text]), except for the gracilis and sartorius muscles in which the proportion of optimal muscle fiber length to tendon slack length is higher than 1; forces of these two muscles were also sensitive to the optimal muscle fiber length. The results of this study are expected to contribute to our understanding of which parameters should be personalized when conducting musculoskeletal modeling and simulation of patients with CP.

scholarly journals Sensitivity of Estimated Muscle Force in Forward Simulation of Normal Walking

2010 ◽  
Vol 26 (2) ◽  
pp. 142-149 ◽  
Author(s):  
Ming Xiao ◽  
Jill Higginson
Keyword(s):  
Fiber Length ◽  
Muscle Force ◽  
Force Production ◽  
Muscle Group ◽  
Knee Extensors ◽  
Muscle Forces ◽  
Normal Walking ◽  
Forward Simulation ◽  
Individual Muscle ◽  
Slack Length

Generic muscle parameters are often used in muscle-driven simulations of human movement to estimate individual muscle forces and function. The results may not be valid since muscle properties vary from subject to subject. This study investigated the effect of using generic muscle parameters in a muscle-driven forward simulation on muscle force estimation. We generated a normal walking simulation in OpenSim and examined the sensitivity of individual muscle forces to perturbations in muscle parameters, including the number of muscles, maximum isometric force, optimal fiber length, and tendon slack length. We found that when changing the number of muscles included in the model, only magnitude of the estimated muscle forces was affected. Our results also suggest it is especially important to use accurate values of tendon slack length and optimal fiber length for ankle plantar flexors and knee extensors. Changes in force production by one muscle were typically compensated for by changes in force production by muscles in the same functional muscle group, or the antagonistic muscle group. Conclusions regarding muscle function based on simulations with generic musculoskeletal parameters should be interpreted with caution.

Evaluating the Effects of Ankle-Foot Orthosis Mechanical Property Assumptions on Gait Simulation Muscle Force Results

2017 ◽  
Vol 139 (3) ◽  
Author(s):  
Amy K. Hegarty ◽  
Anthony J. Petrella ◽  
Max J. Kurz ◽  
Anne K. Silverman
Keyword(s):  
Mechanical Property ◽  
Muscle Force ◽  
Musculoskeletal Modeling ◽  
Walking Ability ◽  
Muscle Forces ◽  
Walking Gait ◽  
Ankle Foot Orthosis ◽  
Simulation Techniques ◽  
Equilibrium Angle ◽  
Ankle Muscles

Musculoskeletal modeling and simulation techniques have been used to gain insights into movement disabilities for many populations, such as ambulatory children with cerebral palsy (CP). The individuals who can benefit from these techniques are often limited to those who can walk without assistive devices, due to challenges in accurately modeling these devices. Specifically, many children with CP require the use of ankle-foot orthoses (AFOs) to improve their walking ability, and modeling these devices is important to understand their role in walking mechanics. The purpose of this study was to quantify the effects of AFO mechanical property assumptions, including rotational stiffness, damping, and equilibrium angle of the ankle and subtalar joints, on the estimation of lower-limb muscle forces during stance for children with CP. We analyzed two walking gait cycles for two children with CP while they were wearing their own prescribed AFOs. We generated 1000-trial Monte Carlo simulations for each of the walking gait cycles, resulting in a total of 4000 walking simulations. We found that AFO mechanical property assumptions influenced the force estimates for all the muscles in the model, with the ankle muscles having the largest resulting variability. Muscle forces were most sensitive to assumptions of AFO ankle and subtalar stiffness, which should therefore be measured when possible. Muscle force estimates were less sensitive to estimates of damping and equilibrium angle. When stiffness measurements are not available, limitations on the accuracy of muscle force estimates for all the muscles in the model, especially the ankle muscles, should be acknowledged.

Subject-Specific Estimates of Tendon Slack Length: A Numerical Method

2004 ◽  
Vol 20 (2) ◽  
pp. 195-203 ◽  
Author(s):  
Kurt Manal ◽  
Thomas S. Buchanan
Keyword(s):  
Numerical Method ◽  
Fiber Length ◽  
Muscle Force ◽  
Specific Input ◽  
Musculoskeletal Models ◽  
Subject Specific ◽  
Input Parameters ◽  
Slack Length

Tendon develops force proportional to the distance it is stretched beyond its slack length. Tendon slack length is an important parameter for musculoskeletal models because it can greatly affect estimations of muscle force. Unfortunately, tendon slack length is a difficult parameter to measure, and therefore values for it are not often reported in the literature. In this paper we present a numerical method for estimating tendon slack length from architectural parameters of the muscle. Specifically, tendon slack length is computed iteratively from musculotendon lengths determined when a corresponding joint is held at two angles, and from knowledge of the muscle's optimal fiber length. Idealized data generated using SIMM were used to test the tendon slack length algorithm. The method converged to within 1% of the “true” tendon slack length specified in the SIMM model. The advantage of the method outlined in this paper is that it yields subject-specific estimates of tendon slack length, given subject-specific input parameters.

A Numerical Method for Estimating Tendon Slack Length

2003 ◽  
Author(s):  
Kurt Manal ◽  
Thomas S. Buchanan
Keyword(s):  
Numerical Method ◽  
Fiber Length ◽  
Muscle Force ◽  
Isometric Force ◽  
Pennation Angle ◽  
Cross Sectional ◽  
Force Equation ◽  
Subject Specific ◽  
Maximum Isometric Force ◽  
Slack Length

Forces generated by muscle are transferred to bone via tendon. Since muscle force cannot be measured directly, computer modeling is a useful tool to enhance our understanding of normal and pathological movement. Hill-type muscle models have been used to estimate force based on information about a muscle’s architecture, activation and kinematics (Delp et al., 1995; Manal et al., 2002). Architectural parameters include optimal fiber length (lom), tendon slack length (lst), pennation angle (α), and maximum isometric force (Fmax). In addition, musculotendon length (lmt) and activation (a) are required inputs when estimating isometric muscle force (Equation I). Fm=f(lmt,lom,lst,Fmax,α,a)(1) Musculotendon length can be determined from MR images (Arnold et al., 2000), and activation recorded from EMGs (Manal, et al., 2002). Optimal fiber length and pennation angle can be measured experimentally (Murray, 2002), while Fmax can be estimated from the muscle’s physiologic cross-sectional area. Tendon slack length however cannot be measured readily, and therefore few estimates of lst can be found in the literature. In this paper we present a numerical method for estimating tendon slack from subject specific muscle parameters and musculotendon lengths. An advantage of this method is that it yields subject specific estimates of tendon slack length.

Feasibility of Highly Constrained Muscle Force Predictions for the Knee During Gait

2011 ◽  
Author(s):  
Jonathan P. Walter ◽  
Darryl D. D’Lima ◽  
Thor F. Besier ◽  
Benjamin J. Fregly
Keyword(s):  
Cerebral Palsy ◽  
Muscle Force ◽  
Human Muscle ◽  
Accurate Assessment ◽  
Muscle Forces

Accurate assessment of human muscle forces during walking could significantly aid in the analysis and treatment of common neuromusculoskeletal disorders such as osteoarthritis [1], stroke, and cerebral palsy. However, the inability to measure muscle forces in vivo along with the inability to calculate muscle forces directly has greatly hindered achievement of this goal. Due to its complexity, the knee is a particularly difficult joint for assessing in vivo muscle forces.

Knee Muscle Force Estimating Model Using Machine Learning Approach

2020 ◽  
Author(s):  
Anurag Sohane ◽  
Ravinder Agarwal
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Muscle Force ◽  
Vastus Lateralis ◽  
Input Parameter ◽  
Research Work ◽  
Cost Effective ◽  
Coefficient Of Determination ◽  
Muscle Forces ◽  
Knee Muscle

Abstract Various simulation type tools and conventional algorithms are being used to determine knee muscle forces of human during dynamic movement. These all may be good for clinical uses, but have some drawbacks, such as higher computational times, muscle redundancy and less cost-effective solution. Recently, there has been an interest to develop supervised learning-based prediction model for the computationally demanding process. The present research work is used to develop a cost-effective and efficient machine learning (ML) based models to predict knee muscle force for clinical interventions for the given input parameter like height, mass and angle. A dataset of 500 human musculoskeletal, have been trained and tested using four different ML models to predict knee muscle force. This dataset has obtained from anybody modeling software using AnyPyTools, where human musculoskeletal has been utilized to perform squatting movement during inverse dynamic analysis. The result based on the datasets predicts that the random forest ML model outperforms than the other selected models: neural network, generalized linear model, decision tree in terms of mean square error (MSE), coefficient of determination (R2), and Correlation (r). The MSE of predicted vs actual muscle forces obtained from the random forest model for Biceps Femoris, Rectus Femoris, Vastus Medialis, Vastus Lateralis are 19.92, 9.06, 5.97, 5.46, Correlation are 0.94, 0.92, 0.92, 0.94 and R2 are 0.88, 0.84, 0.84 and 0.89 for the test dataset, respectively.

Evaluating upper body movements during gait in healthy children and children with diplegic cerebral palsy

2007 ◽  
Vol 16 (3) ◽  
pp. 175-180 ◽  
Author(s):  
Jacqueline Romkes ◽  
Wietske Peeters ◽  
Aidia M. Oosterom ◽  
Sara Molenaar ◽  
Iris Bakels ◽  
...  
Keyword(s):  
Cerebral Palsy ◽  
Upper Body ◽  
Healthy Children ◽  
Body Movements

AN ALGORITHM TO CALCULATE THE COMPONENTS OF ALL MUSCLE FORCES DURING EACH PHASES OF THE GAIT CYCLE, FOR THE PELVIC-FEMUR COMPLEX

2005 ◽  
Vol 05 (04) ◽  
pp. 539-548 ◽  
Author(s):  
SANTANU MAJUMDER ◽  
AMIT ROYCHOWDHURY ◽  
SUBRATA PAL
Keyword(s):  
Finite Element ◽  
Hip Joint ◽  
Computational Models ◽  
Gait Cycle ◽  
Muscle Force ◽  
Fe Analysis ◽  
Muscle Forces ◽  
Force Magnitude ◽  
Flexion Extension ◽  
Force Components

With the help of finite element (FE) computational models of femur, pelvis or hip joint to perform quasi-static stress analysis during the entire gait cycle, muscle force components (X, Y, Z) acting on the hip joint and pelvis are to be known. Most of the investigators have presented only the net muscle force magnitude during gait. However, for the FE software, either muscle force components (X, Y, Z) or three angles for the muscle line of action are required as input. No published algorithm (with flowchart) is readily available to calculate the required muscle force components for FE analysis. As the femur rotates about the hip center during gait, the lines of action for 27 muscle forces are also variable. To find out the variable lines of action and muscle force components (X, Y, Z) with directions, an algorithm was developed and presented here with detailed flowchart. We considered the varying angles of adduction/abduction, flexion/extension during gait. This computer program, obtainable from the first author, is able to calculate the muscle force components (X, Y, Z) as output, if the net magnitude of muscle force, hip joint orientations during gait and muscle origin and insertion coordinates are provided as input.

scholarly journals MM-MRE: a new technique to quantify individual muscle forces during isometric tasks of the wrist using MR elastography

2019 ◽  
Author(s):  
Andrea Zonnino ◽  
Daniel R. Smith ◽  
Peyton L. Delgorio ◽  
Curtis L. Johnson ◽  
Fabrizio Sergi
Keyword(s):  
Shear Wave ◽  
Muscle Force ◽  
Muscle Mechanics ◽  
Neuromuscular Control ◽  
Joint Torque ◽  
New Technique ◽  
Muscle Forces ◽  
Individual Muscle ◽  
Complete Set ◽  
A New Technique

AbstractNon-invasive in-vivo measurement of individual muscle force is limited by the infeasibility of placing force sensing elements in series with the musculo-tendon structures. At the same time, estimating muscle forces using EMG measurements is prone to inaccuracies, as EMG is not always measurable for the complete set of muscles acting around the joints of interest. While new methods based on shear wave elastography have been recently proposed to directly characterize muscle mechanics, they can only be used to measure muscle forces in a limited set of superficial muscles. As such, they are not suitable to study the neuromuscular control of movements that require coordinated action of multiple muscles.In this work, we present multi-muscle magnetic resonance elastography (MM-MRE), a new technique capable of quantifying individual muscle force from the complete set of muscles in the forearm, thus enabling the study of the neuromuscular control of wrist movements. MM-MRE integrates measurements of joint torque provided by an MRI-compatible instrumented handle with muscle-specific measurements of shear wave speed obtained via MRE to quantify individual muscle force using model-based estimator.A single-subject pilot experiment demonstrates the possibility of obtaining measurements from individual muscles and establishes that MM-MRE has sufficient sensitivity to detect changes in muscle mechanics following the application of isometric joint torque with self-selected intensity.

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