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.

scholarly journals Comparative Assessment of Priority Listing and Mixed Integer Linear Programming Unit Commitment Methods for Non-Interconnected Island Systems

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 657 ◽  
Author(s):  
Georgios Psarros ◽  
Stavros Papathanassiou
Keyword(s):  
Linear Programming ◽  
Power Systems ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Unit Commitment ◽  
Renewable Energy Sources ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Droop Characteristic

The generation management concept for non-interconnected island (NII) systems is traditionally based on simple, semi-empirical operating rules dating back to the era before the massive deployment of renewable energy sources (RES), which do not achieve maximum RES penetration, optimal dispatch of thermal units and satisfaction of system security criteria. Nowadays, more advanced unit commitment (UC) and economic-dispatch (ED) approaches based on optimization techniques are gradually introduced to safeguard system operation against severe disturbances, to prioritize RES participation and to optimize dispatch of the thermal generation fleet. The main objective of this paper is to comparatively assess the traditionally applied priority listing (PL) UC method and a more sophisticated mixed integer linear programming (MILP) UC optimization approach, dedicated to NII power systems. Additionally, to facilitate the comparison of the UC approaches and quantify their impact on systems security, a first attempt is made to relate the primary reserves capability of each unit to the maximum acceptable frequency deviation at steady state conditions after a severe disturbance and the droop characteristic of the unit’s speed governor. The fundamental differences between the two approaches are presented and discussed, while daily and annual simulations are performed and the results obtained are further analyzed.

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.

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.

ESTIMATING MUSCLE FORCES AND KNEE JOINT TORQUE USING SURFACE ELECTROMYOGRAPHY: A MUSCULOSKELETAL BIOMECHANICAL MODEL

2017 ◽  
Vol 17 (04) ◽  
pp. 1750069 ◽  
Author(s):  
JIANGCHENG CHEN ◽  
XIAODONG ZHANG ◽  
LINXIA GU ◽  
CARL NELSON
Keyword(s):  
Knee Joint ◽  
Surface Electromyography ◽  
Neural Control ◽  
Muscle Activation ◽  
Muscle Force ◽  
Dynamic Properties ◽  
Biomechanical Model ◽  
Joint Torque ◽  
Muscle Forces ◽  
Flexion Extension

Surface electromyography (sEMG) is a useful tool for revealing the underlying musculoskeletal dynamic properties in the human body movement. In this paper, a musculoskeletal biomechanical model which relates the sEMG and knee joint torque is proposed. First, the dynamic model relating sEMG to skeletal muscle activation considering frequency and amplitude is built. Second, a muscle contraction model based on sliding-filament theory is developed to reflect the physiological structure and micro mechanical properties of the muscle. The muscle force and displacement vectors are determined and the transformation from muscle force to knee joint moment is realized, and finally a genetic algorithm-based calibration method for the Newton–Euler dynamics and overall musculoskeletal biomechanical model is put forward. Following the model calibration, the flexion/extension (FE) knee joint torque of eight subjects under different walking speeds was predicted. Results show that the forward biomechanical model can capture the general shape and timing of the joint torque, with normalized mean residual error (NMRE) of [Formula: see text]10.01%, normalized root mean square error (NRMSE) of [Formula: see text]12.39% and cross-correlation coefficient of [Formula: see text]0.926. The musculoskeletal biomechanical model proposed and validated in this work could facilitate the study of neural control and how muscle forces generate and contribute to the knee joint torque during human movement.

A Novel Optimization Approach to Generate Physiological Human Walking Patterns

2007 ◽  
Author(s):  
Marko Ackermann
Keyword(s):  
Energy Expenditure ◽  
Dynamic Optimization ◽  
Musculoskeletal System ◽  
Large Scale ◽  
Inverse Dynamics ◽  
Human Walking ◽  
Optimization Approach ◽  
Muscle Coordination ◽  
Muscle Forces ◽  
State Equations

Dynamic simulation of the musculoskeletal system is increasingly being used to study human normal and pathological walking. The common approach to predict walking patterns is based on the assumption that the central nervous system minimizes an intrinsic performance criterion. For instance, during walking, the energy expenditure per unit of distance traveled was shown to play a key role. The resulting optimal control problem is almost exclusively solved by the so-called dynamic optimization. Dynamic optimization relies on the parameterization of neural excitations using nodal values serving as optimization variables. The reconstructed neural excitations are then used to numerically integrate the differential equations describing the dynamics of the musculoskeletal system. This approach has been successfully applied to predict salient normal walking patterns, including muscle coordination and energy expenditure. In spite of the growing use of dynamic optimization, the extremely high computational effort arising from the several numerical integrations of the large-scale state equations required prevents it from being more widely applied, e.g., for bioassistive devices. Approaches based on inverse dynamics have the potential to reduce the high computation effort by avoiding the necessity of numerically integrating the state equations, but have been poorly explored in biomechanics. The development of an inverse dynamics-based approach to generate near-optimal human walking patterns that deals with the overdeterminacy of muscle actuation in conjunction with Hill-type muscle models widely used in biomechanics is proposed in this paper. The approach is based on the parameterization of the motion and muscle forces. The neural excitations are obtained by inverting the muscle contraction and activation dynamics. The compatibility between motion and muscle forces is guaranteed by checking the fulfillment of the equations of motion of the skeletal system at control points. The approach is implemented and human normal and pathological gaits are generated and applied to the design of transtibial prostheses.

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.

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 The Effects of External Loads and Muscle Forces on the Knee Joint Ligaments during Walking: A Musculoskeletal Model Study

2021 ◽  
Vol 11 (5) ◽  
pp. 2356
Author(s):  
Carlo Albino Frigo ◽  
Lucia Donno
Keyword(s):  
Knee Joint ◽  
Cruciate Ligament ◽  
Musculoskeletal Model ◽  
Optimization Approach ◽  
Muscle Forces ◽  
Collateral Ligaments ◽  
Flexion Extension ◽  
Lateral Collateral Ligaments ◽  
Gastrocnemius Muscles ◽  
External Loads

A musculoskeletal model was developed to analyze the tensions of the knee joint ligaments during walking and to understand how they change with changes in the muscle forces. The model included the femur, tibia, patella and all components of cruciate and collateral ligaments, quadriceps, hamstrings and gastrocnemius muscles. Inputs to the model were the muscle forces, estimated by a static optimization approach, the external loads (ground reaction forces and moments) and the knee flexion/extension movement corresponding to natural walking. The remaining rotational and translational movements were obtained as a result of the dynamic equilibrium of forces. The validation of the model was done by comparing our results with literature data. Several simulations were carried out by sequentially removing the forces of the different muscle groups. Deactivation of the quadriceps produced a decrease of tension in the anterior cruciate ligament (ACL) and an increase in the posterior cruciate ligament (PCL). By removing the hamstrings, the tension of ACL increased at the late swing phase, while the PCL force dropped to zero. Specific effects were observed also at the medial and lateral collateral ligaments. The removal of gastrocnemius muscles produced an increase of tension only on PCL and lateral collateral ligaments. These results demonstrate how musculoskeletal models can contribute to knowledge about complex biomechanical systems as the knee joint.

scholarly journals Optimal Fractional PID Controller for Buck Converter Using Cohort Intelligent Algorithm

2021 ◽  
Vol 4 (3) ◽  
pp. 50
Author(s):  
Preeti Warrier ◽  
Pritesh Shah
Keyword(s):  
Fractional Order ◽  
Pid Controller ◽  
Power Converters ◽  
Buck Converter ◽  
Optimization Techniques ◽  
Superior Performance ◽  
Computational Time ◽  
Optimization Approach ◽  
Response Characteristics ◽  
Fractional Order Controller

The control of power converters is difficult due to their non-linear nature and, hence, the quest for smart and efficient controllers is continuous and ongoing. Fractional-order controllers have demonstrated superior performance in power electronic systems in recent years. However, it is a challenge to attain optimal parameters of the fractional-order controller for such types of systems. This article describes the optimal design of a fractional order PID (FOPID) controller for a buck converter using the cohort intelligence (CI) optimization approach. The CI is an artificial intelligence-based socio-inspired meta-heuristic algorithm, which has been inspired by the behavior of a group of candidates called a cohort. The FOPID controller parameters are designed for the minimization of various performance indices, with more emphasis on the integral squared error (ISE) performance index. The FOPID controller shows faster transient and dynamic response characteristics in comparison to the conventional PID controller. Comparison of the proposed method with different optimization techniques like the GA, PSO, ABC, and SA shows good results in lesser computational time. Hence the CI method can be effectively used for the optimal tuning of FOPID controllers, as it gives comparable results to other optimization algorithms at a much faster rate. Such controllers can be optimized for multiple objectives and used in the control of various power converters giving rise to more efficient systems catering to the Industry 4.0 standards.

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