Use of Optimization Techniques to Predict Muscle Forces

1978 ◽  
Vol 100 (2) ◽  
pp. 88-92 ◽  
Author(s):  
R. D. Crowninshield
Keyword(s):  
Muscle Strength ◽  
Muscle Force ◽  
Optimization Techniques ◽  
Emg Activity ◽  
Muscle Action ◽  
Muscle Forces ◽  
Distribution Problem ◽  
Joint Function ◽  
Individual Muscle ◽  
Synergistic Muscle

Optimization solutions to the indeterminate distribution problem of determining muscle forces are discussed. A method of predicting muscle force during joint function is presented which encourages the prediction of synergistic muscle action with physiologically reasonable individual muscle forces. The method uses limits on muscle strength that are a set portion of the lowest muscle strength that will permit a solution at the particular joint moment. The method is shown to correlate well with recorded EMG activity.

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.

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.

Determining Muscle Forces in the Leg During Normal Human Walking—An Application and Evaluation of Optimization Methods

1978 ◽  
Vol 100 (2) ◽  
pp. 72-78 ◽  
Author(s):  
D. E. Hardt
Keyword(s):  
Linear Programming ◽  
Muscle Force ◽  
Tensile Force ◽  
Optimization Techniques ◽  
Joint Torque ◽  
Human Walking ◽  
Programming Algorithm ◽  
Optimization Approach ◽  
Muscle Forces ◽  
Optimal Criterion

The individual muscle forces in the leg during human walking are unknown, because of a greater number of muscles when compared to degrees of freedom at the joints. The muscle force-joint torque equations can be solved, however, using optimization techniques. A linear programming solution of these equations applied at discrete, time-independent steps in the walking cycle using dynamic joint torque data is presented. The use of this technique, although capable of providing unique solutions, gives questionable muscle force histories when compared to electromyographic data. The reasons for the lack of confidence in the solution are found in the inherent limitations imposed by the linear programming algorithm and in the simplistic treatment of the muscles as tensile force sources rather than complex mechanochemical transducers. The definition of a physiologically rationalized optimal criterion requires both a global optimization approach and more complete modelling of the system.

An Analytical Examination of Muscle Force Estimations Using Optimization Techniques

1993 ◽  
Vol 207 (3) ◽  
pp. 139-148 ◽  
Author(s):  
J H Challis ◽  
D G Kerwin
Keyword(s):  
Objective Function ◽  
Muscle Force ◽  
Human Movement ◽  
Optimization Techniques ◽  
Muscle Model ◽  
Muscle Forces ◽  
Objective Functions ◽  
Physiological Properties ◽  
Maximal Activation

Muscle forces are often estimated during human movement using optimization procedures. The optimization procedures involve the minimization of an objective function relating to the muscle forces. In this study 15 different objective functions were evaluated by examining the analytical solutions to the objective functions and by comparing their force predictions with the forces estimated using a validated muscle model. The muscle forces estimated by the objective functions were shown to give poor correspondence with the muscle model predicted muscle forces. The objective function estimates were criticized for not taking sufficient account of the physiological properties of the muscles. As a consequence of the analysis of the objective functions an alternative, simpler function was presented with which to estimate muscle forces in vivo. This function required that to satisfy a given joint moment, the force exerted by each of the muscles divided by the maximum force possible by the muscle was constant for all muscles. For this function the maximum muscle force was determined using a muscle model assuming maximal activation.

Direct Validation of Model-Predicted Muscle Forces in the Cat Hindlimb During Locomotion

2020 ◽  
Vol 142 (5) ◽  
Author(s):  
Derya Karabulut ◽  
Suzan Cansel Dogru ◽  
Yi-Chung Lin ◽  
Marcus G. Pandy ◽  
Walter Herzog ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Muscle Force ◽  
Medial Gastrocnemius ◽  
Electromyographic Activity ◽  
Muscle Forces ◽  
Static Optimization ◽  
Individual Muscle ◽  
Length Changes ◽  
Musculoskeletal Systems

Abstract Various methods are available for simulating the movement patterns of musculoskeletal systems and determining individual muscle forces, but the results obtained from these methods have not been rigorously validated against experiment. The aim of this study was to compare model predictions of muscle force derived for a cat hindlimb during locomotion against direct measurements of muscle force obtained in vivo. The cat hindlimb was represented as a 5-segment, 13-degrees-of-freedom (DOF), articulated linkage actuated by 25 Hill-type muscle-tendon units (MTUs). Individual muscle forces were determined by combining gait data with two widely used computational methods—static optimization and computed muscle control (CMC)—available in opensim, an open-source musculoskeletal modeling and simulation environment. The forces developed by the soleus, medial gastrocnemius (MG), and tibialis anterior muscles during free locomotion were measured using buckle transducers attached to the tendons. Muscle electromyographic activity and MTU length changes were also measured and compared against the corresponding data predicted by the model. Model-predicted muscle forces, activation levels, and MTU length changes were consistent with the corresponding quantities obtained from experiment. The calculated values of muscle force obtained from static optimization agreed more closely with experiment than those derived from CMC.

Considerations for Predicting Individual Muscle Forces in Athletic Movements

1987 ◽  
Vol 3 (2) ◽  
pp. 128-141 ◽  
Author(s):  
Walter Herzog
Keyword(s):  
Muscle Force ◽  
Load Sharing ◽  
Knee Extension ◽  
Muscle Contractions ◽  
Optimal Designs ◽  
Muscle Forces ◽  
Design Considerations ◽  
Individual Muscle ◽  
Linear And Nonlinear ◽  
Design Characteristics

Linear and nonlinear optimal designs have been used abundantly to predict the forces exerted by individual muscles for everyday movements such as walking. Individual muscle force predictions for athletic movements, those involving large ranges of motion and fast velocities of muscle contractions, are almost nonexistent. The purpose of this paper is to illustrate some of the design characteristics that must be considered for predicting individual muscle forces in athletic movements. To do this, the load sharing between two muscles, derived from nonlinear optimal designs, is considered in two ways: (a) in hypothetical experiments of muscle contractions, and (b) in real experiments of knee extension movements performed by one subject. The results suggested that additional design considerations must be made when predicting forces in athletic movements compared to everyday movements.

Optimization Techniques in Occupational Biomechanics

1989 ◽  
Vol 33 (11) ◽  
pp. 672-676
Author(s):  
Ashraf Genaldy ◽  
Abdolazim Houshyar
Keyword(s):  
Mathematical Description ◽  
Muscle Force ◽  
Optimization Techniques ◽  
Muscle Forces ◽  
Joint Mechanics ◽  
Body Function ◽  
Biomechanical Models

In most detailed representations of joint mechanics incorporating the effects of muscle forces in biomechanical models the number of available force-carrying structures crossing the joints are in excess of the number of available equilibrium of the joint. Unless one makes gross anatomical and functional simplifications, the mathematical description of joint mechanics involves an undetermined set of equations. Different approaches have been taken by researchers to solve this statically indeterminate problem, but the intuitive reasonableness of optimization in body function has led investigators to use numeral optimization procedures in the prediction of muscle force activity. This paper reviews and evaluates various optimization techniques applied to occupational biomechanics.

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.

scholarly journals A Pilot Study of Individual Muscle Force Prediction during Elbow Flexion and Extension in the Neurorehabilitation Field

Sensors ◽  
2016 ◽  
Vol 16 (12) ◽  
pp. 2018 ◽  
Author(s):  
Jiateng Hou ◽  
Yingfei Sun ◽  
Lixin Sun ◽  
Bingyu Pan ◽  
Zhipei Huang ◽  
...  
Keyword(s):  
Pilot Study ◽  
Muscle Force ◽  
Elbow Flexion ◽  
Force Prediction ◽  
Individual Muscle ◽  
Flexion And Extension
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