scholarly journals Towards Subject-Specific Strength Training Design through Predictive Use of Musculoskeletal Models

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Michael Plüss ◽  
Florian Schellenberg ◽  
William R. Taylor ◽  
Silvio Lorenzetti
Keyword(s):  
Muscle Force ◽  
Inverse Dynamics ◽  
Gluteus Medius ◽  
The Body ◽  
External Loading ◽  
Detailed Knowledge ◽  
Muscle Forces ◽  
Specific Strength ◽  
Musculoskeletal Models ◽  
Flexion Extension

Lower extremity dysfunction is often associated with hip muscle strength deficiencies. Detailed knowledge of the muscle forces generated in the hip under specific external loading conditions enables specific structures to be trained. The aim of this study was to find the most effective movement type and loading direction to enable the training of specific parts of the hip muscles using a standing posture and a pulley system. In a novel approach to release the predictive power of musculoskeletal modelling techniques based on inverse dynamics, flexion/extension and ab-/adduction movements were virtually created. To demonstrate the effectiveness of this approach, three hip orientations and an external loading force that was systematically rotated around the body were simulated using a state-of-the art OpenSim model in order to establish ideal designs for training of the anterior and posterior parts of the M. gluteus medius (GM). The external force direction as well as the hip orientation greatly influenced the muscle forces in the different parts of the GM. No setting was found for simultaneous training of the anterior and posterior parts with a muscle force higher than 50% of the maximum. Importantly, this study has demonstrated the use of musculoskeletal models as an approach to predict muscle force variations for different strength and rehabilitation exercise variations.

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 The Effects of Walking Speed on Tibiofemoral Loading Estimated Via Musculoskeletal Modeling

2014 ◽  
Vol 30 (2) ◽  
pp. 197-205 ◽  
Author(s):  
Zachary F. Lerner ◽  
Derek J. Haight ◽  
Matthew S. DeMers ◽  
Wayne J. Board ◽  
Raymond C. Browning
Keyword(s):  
Walking Speed ◽  
Inverse Dynamics ◽  
Knee Extension ◽  
Musculoskeletal Model ◽  
Contact Forces ◽  
Muscle Forces ◽  
Musculoskeletal Models ◽  
Peak Knee ◽  
Muscle Moments ◽  
Vector Sum

Net muscle moments (NMMs) have been used as proxy measures of joint loading, but musculoskeletal models can estimate contact forces within joints. The purpose of this study was to use a musculoskeletal model to estimate tibiofemoral forces and to examine the relationship between NMMs and tibiofemoral forces across walking speeds. We collected kinematic, kinetic, and electromyographic data as ten adult participants walked on a dual-belt force-measuring treadmill at 0.75, 1.25, and 1.50 m/s. We scaled a musculoskeletal model to each participant and used OpenSim to calculate the NMMs and muscle forces through inverse dynamics and weighted static optimization, respectively. We determined tibiofemoral forces from the vector sum of intersegmental and muscle forces crossing the knee. Estimated tibiofemoral forces increased with walking speed. Peak earlystance compressive tibiofemoral forces increased 52% as walking speed increased from 0.75 to 1.50 m/s, whereas peak knee extension NMMs increased by 168%. During late stance, peak compressive tibiofemoral forces increased by 18% as speed increased. Although compressive loads at the knee did not increase in direct proportion to NMMs, faster walking resulted in greater compressive forces during weight acceptance and increased compressive and anterior/posterior tibiofemoral loading rates in addition to a greater abduction NMM.

Comparison of Cylindrical Wrapping Geometries to Via Points for Modeling Muscle Paths in the Estimation of Sit-to-Stand Muscle Forces

2013 ◽  
Author(s):  
Valerie Norman-Gerum ◽  
John McPhee
Keyword(s):  
Muscle Force ◽  
Inverse Dynamics ◽  
Three Dimensional ◽  
Standing Position ◽  
Minimum Length ◽  
Muscle Forces ◽  
Joint Torques ◽  
Sit To Stand ◽  
Cylindrical Geometries ◽  
Anatomical Constraints

To better understand the complexities of rising from a seated to a standing position, a model of the human has been created. Sit-to-stand kinematics as well as ground reaction forces were measured experimentally and are used in an inverse dynamics analysis to estimate nine muscle forces during motion. Calculated muscle forces are sensitive to assumptions made when modeling muscle paths. Changes in the line of action of a muscle due to interaction with anatomical constraints are often accounted for by including fixed via points in a model. Here an alternate approach of representing anatomical constraints using three-dimensional cylindrical geometries is derived and presented. In this mathematical model the course of the muscle is determined as the minimum-length path where the muscle is allowed to wrap freely over the surface of the cylinder. Muscle forces are estimated for sit-to-stand by resolving net joint torques using an objective function giving preference to solutions minimizing both muscle stresses and abrupt changes in muscle forces. This is the first time muscle forces have been presented for sit-to-stand using a musculoskeletal model with included anatomical constraints represented using cylindrical wrapping geometries alone. A comparison of calculated muscle force patterns using fixed via points and wrapping points versus three-dimensional wrapping surfaces is made with reference to electromyographic phase data. For the sit-to-stand motion, the inclusion of anatomical constraints as three-dimensional cylindrical geometries results in calculation of muscle forces more true to the experimental data and more consistent with the belief that gradual motions are created by gradual changes in muscle force over time.

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.

Knee Muscle and Ligament Forces During Drop Landing Exercise

2017 ◽  
Author(s):  
Dumitru I. Caruntu ◽  
Ricardo Moreno ◽  
Robert Freeman
Keyword(s):  
Inverse Dynamics ◽  
Cruciate Ligament ◽  
Lateral Collateral Ligament ◽  
Inequality Constraints ◽  
Standing Position ◽  
The Body ◽  
Muscle Forces ◽  
Collateral Ligament ◽  
Drop Landing ◽  
D Model

This work investigates the human leg muscle and ligaments forces during a drop-landing exercise. An inverse dynamics 2-D model of human leg is used on this ballistic task in order to predict these forces. The model consists of three bony structures, namely femur, tibia, and patella. The joints of the model are the knee joint and the hip joint. The ligamentous structure of the knee includes the two cruciate ligaments, Anterior Cruciate Ligament (ACL) and the Posterior Cruciate Ligament (PCL), and the two collateral ligaments, Lateral Collateral Ligament (LCL) and Medial Collateral Ligament (MCL). The system of muscles of the system includes muscle such as quadriceps, hamstrings, gastrocnemius are included in the model. Experimental data used show a maximum of 100 degrees of flexion angle and ground reaction forces up to 4 times the body weight. The inverse dynamics 2-D model consists of an objective function to minimize the muscle forces, and a set of constraints consisting of equality constraints which are the dynamics equations of the bony structures, and inequality constraints in which all muscle forces must be positive. All muscle forces show a pattern in which they reach large magnitudes at the beginning of landing, decreasing as the subject end the exercise with a standing position.

The Comparison of Muscle Forces Derived From Different Muscle Models

2005 ◽  
Author(s):  
Miloslav Vilimek
Keyword(s):  
Elbow Joint ◽  
Inverse Dynamics ◽  
Optimization Techniques ◽  
Muscle Forces ◽  
Bone Implant ◽  
Optimization Criteria ◽  
Functional Activities ◽  
Flexion Extension ◽  
Activation Dynamics ◽  
Muscle Models

This study, investigated the accuracy, practicality, and sensitivity of several different methods of calculating muscle forces during functional activities in humans. The upper extremity dynamic system was chosen, where the movement flexion / extension elbow joint was studied. The redundant mechanisms were solved using optimization criteria with and without models of individual muscles according to their active and passive properties. Exploration of the control problem for the redundant elbow system was performed using muscle models with and without tendon and activation dynamics. Comparisons with known movements solved by inverse dynamics approach and optimization techniques, provided similar results across to all optimization criteria. Moreover, if muscle models with active and passive properties are included in these analyses, it is relatively easy to calculate muscle forces of both agonists and antagonists. These approaches may be used to provide input data for dynamic FEM stress analysis of bones and bone-implant systems.

Methodology to Customize Maximal Isometric Forces for Hill-Type Muscle Models

2017 ◽  
Vol 33 (1) ◽  
pp. 80-86 ◽  
Author(s):  
Fabien Dal Maso ◽  
Mickaël Begon ◽  
Maxime Raison
Keyword(s):  
Muscle Force ◽  
Biceps Brachii ◽  
Isometric Force ◽  
Force Estimation ◽  
Triceps Brachii ◽  
Joint Torques ◽  
Musculoskeletal Models ◽  
Flexion Extension ◽  
Isometric Forces ◽  
Maximal Isometric Force

One approach to increasing the confidence of muscle force estimation via musculoskeletal models is to minimize the root mean square error (RMSE) between joint torques estimated from electromyographic-driven musculoskeletal models and those computed using inverse dynamics. We propose a method that reduces RMSE by selecting subsets of combinations of maximal voluntary isometric contraction (MVIC) trials that minimize RMSE. Twelve participants performed 3 elbow MVIC in flexion and in extension. An upper-limb electromyographic-driven musculoskeletal model was created to optimize maximum muscle stress and estimate the maximal isometric force of the biceps brachii, brachialis, brachioradialis, and triceps brachii. Maximal isometric forces were computed from all possible combinations of flexion-extension trials. The combinations producing the smallest RMSE significantly reduced the normalized RMSE to 7.4% compared with the combination containing all trials (9.0%). Maximal isometric forces ranged between 114–806 N, 64–409 N, 236–1511 N, and 556–3434 N for the brachii, brachialis, brachioradialis, and triceps brachii, respectively. These large variations suggest that customization is required to reduce the difference between models and actual participants’ maximal isometric force. While the smallest previously reported RMSE was 10.3%, the proposed method reduced the RMSE to 7.4%, which may increase the confidence of muscle force estimation.

scholarly journals THE INFLUENCE OF ACTUATION MODELING ON THE ASSESSED JOINT REACTIONS IN BIOMECHANICAL SYSTEMS

2013 ◽  
Vol 20 (3) ◽  
pp. 183-187
Author(s):  
Krzysztof Drapała ◽  
Kazimierz Pulaski ◽  
Wojciech Blajer
Keyword(s):  
Human Body ◽  
Lower Limb ◽  
Inverse Dynamics ◽  
Dynamics Simulation ◽  
Muscle Forces ◽  
Biomechanical Models ◽  
Musculoskeletal Models ◽  
Human Movements ◽  
Time Variations

Abstract Introduction. Human body biomechanical models are actuated either by net torques at the joints or individual muscle forces whose action around the joints results, by principle, in the net torques. In the model-based inverse dynamics simulation of human movements the assessed joint reactions depend substantially on the choice of the actuation model, which is discussed in the paper. Material and methods. Using the two actuation models, variant biomechanical models of the lower limb, decomposed from the whole human body, were developed. They were then used for the inverse dynamics simulation of a recorded one-leg jump on the force platform to assess time variations of controls (either net torques or muscle forces) and joint reactions. Results. The assessed joint reactions obtained using the model actuated by net torques are substantially different from those obtained by means of the model actuated by muscle forces. Conclusion. The joint reactions computed using the model actuated by net torques do not involve contribution of the tensile muscle forces to the internal loads, and they are therefore underestimated. Determination of joint reactions should thus be based on musculoskeletal models actuated by the 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.

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