The Influence of Uncertainty in Body Segment Mass on Calculated Joint Moments and Muscle Forces

2016 ◽  
pp. 349-359
Author(s):  
Magdalena Żuk ◽  
Celina Pezowicz
Keyword(s):  
Muscle Forces ◽  
Body Segment ◽  
Joint Moments

The effect of perturbing body segment parameters on calculated joint moments and muscle forces during gait

2014 ◽  
Vol 47 (2) ◽  
pp. 596-601 ◽  
Author(s):  
Mariska Wesseling ◽  
Friedl de Groote ◽  
Ilse Jonkers
Keyword(s):  
Muscle Forces ◽  
Body Segment ◽  
Joint Moments ◽  
Body Segment Parameters

Estimation of Muscle Forces About the Ankle During Gait in Healthy and Neurologically Impaired Subjects

2011 ◽  
pp. 320-347
Author(s):  
Daniel N. Bassett ◽  
Joseph D. Gardinier ◽  
Kurt T. Manal ◽  
Thomas S. Buchanan
Keyword(s):  
Muscle Activation ◽  
Inverse Dynamics ◽  
Biomechanical Model ◽  
Forward Dynamics ◽  
Muscle Forces ◽  
Dynamics Model ◽  
Joint Moments ◽  
Neurologically Impaired ◽  
Model Predictions ◽  
Contraction Dynamics

This chapter describes a biomechanical model of the forces about the ankle joint applicable to both unimpaired and neurologically impaired subjects. EMGs and joint kinematics are used as inputs and muscle forces are the outputs. A hybrid modeling approach that uses both forward and inverse dynamics is employed and physiological parameters for the model are tuned for each subject using optimization procedures. The forward dynamics part of the model takes muscle activation and uses Hill-type models of muscle contraction dynamics to estimate muscle forces and the corresponding joint moments. Inverse dynamics is used to calibrate the forward dynamics model predictions of joint moments. In this chapter we will describe how to implement an EMG-driven hybrid forward and inverse dynamics model of the ankle that can be used in healthy and neurologically impaired people.

scholarly journals Neuromusculoskeletal Modeling: Estimation of Muscle Forces and Joint Moments and Movements from Measurements of Neural Command

2004 ◽  
Vol 20 (4) ◽  
pp. 367-395 ◽  
Author(s):  
Thomas S. Buchanan ◽  
David G. Lloyd ◽  
Kurt Manal ◽  
Thor F. Besier
Keyword(s):  
Muscle Activation ◽  
Equations Of Motion ◽  
Muscle Forces ◽  
Joint Moments ◽  
Neural Signals ◽  
Knee Kinetics ◽  
Time Varying Parameter ◽  
Activation Dynamics ◽  
Muscle Activations ◽  
Neuromusculoskeletal Modeling

This paper provides an overview of forward dynamic neuromusculoskeletal modeling. The aim of such models is to estimate or predict muscle forces, joint moments, and/or joint kinematics from neural signals. This is a four-step process. In the first step,muscle activation dynamicsgovern the transformation from the neural signal to a measure of muscle activation—a time varying parameter between 0 and 1. In the second step,muscle contraction dynamicscharacterize how muscle activations are transformed into muscle forces. The third step requires a model of themusculoskeletal geometryto transform muscle forces to joint moments. Finally, theequations of motionallow joint moments to be transformed into joint movements. Each step involves complex nonlinear relationships. The focus of this paper is on the details involved in the first two steps, since these are the most challenging to the biomechanician. The global process is then explained through applications to the study of predicting isometric elbow moments and dynamic knee kinetics.

Prediction of Muscle Forces and Joint Moments in Stroke Patients Using an EMG-Driven Model

2008 ◽  
Author(s):  
Qi Shao ◽  
Daniel N. Bassett ◽  
Kurt Manal ◽  
Thomas S. Buchanan
Keyword(s):  
Healthy People ◽  
Muscle Forces ◽  
Stroke Patients ◽  
Joint Moments ◽  
Post Stroke ◽  
Internal Forces ◽  
Kinetics Of

Abnormal kinematic and kinetic patterns are associated with disability following stroke. The estimation of internal forces and moments during movements is important for developing better rehabilitation regimens for this population. In this study, we used an EMG-driven model to estimate muscle forces and joint moments for stroke patients, and analyzed the kinetics of these patients. Although such models have been used in healthy people, this is the first study to model post-stroke patients.

Predicting Muscle Forces and Joint Moments Using Single Joint and Multi Joint EMG-Driven Models

2007 ◽  
Author(s):  
Daniel N. Bassett ◽  
Qi Shao ◽  
Kurt Manal ◽  
Thomas S. Buchanan
Keyword(s):  
Physical Therapists ◽  
Muscle Forces ◽  
Reasonable Accuracy ◽  
Joint Models ◽  
Joint Moments ◽  
Biomedical Field

The biomedical field thrives on computational devices. Clinicians, physical therapists, and researchers frequently use models as tools. The key to proper implementation of these tools is a good understanding of the limitations, advantages, and options available. Previous research on EMG-driven models demonstrated the ability of single joint models to predict joint moments with reasonable accuracy [1]. The advantage provided is the possibility of studying muscle and intersegmental forces in vivo.

scholarly journals Estimation of Muscle Forces and Joint Moments Using a Forward-Inverse Dynamics Model

2005 ◽  
Vol 37 (11) ◽  
pp. 1911-1916 ◽  
Author(s):  
THOMAS S. BUCHANAN ◽  
DAVID G. LLOYD ◽  
KURT MANAL ◽  
THOR F. BESIER
Keyword(s):  
Inverse Dynamics ◽  
Muscle Forces ◽  
Dynamics Model ◽  
Joint Moments

INFLUENCE OF BODY SEGMENT PARAMETERS ON INVERSE DYNAMICS AND ESTIMATED MUSCLE FORCES

2012 ◽  
Vol 45 ◽  
pp. S241
Author(s):  
Mariska Wesseling ◽  
Friedl De Groote ◽  
Christophe Meyer ◽  
Ilse Jonkers
Keyword(s):  
Inverse Dynamics ◽  
Muscle Forces ◽  
Body Segment ◽  
Body Segment Parameters

Using Hill-Type Muscle Models and EMG Data in a Forward Dynamic Analysis of Joint Moment

2003 ◽  
Vol 03 (02) ◽  
pp. 169-186 ◽  
Author(s):  
Richard Heine ◽  
Kurt Manal ◽  
Thomas S. Buchanan
Keyword(s):  
Muscle Activation ◽  
Joint Moment ◽  
Model Parameters ◽  
Time Varying ◽  
Muscle Forces ◽  
Joint Moments ◽  
Delay Term ◽  
Activation Dynamics ◽  
Moment Models ◽  
Muscle Models

There has been considerable interest in estimating muscle forces and joint moments from EMG signals, but most approaches have not been very successful. This is largely because robust models of muscle activation dynamics, Hill-type muscle contraction dynamics, and musculoskeletal geometry are generally not included. Here we present a model which includes these sub-models and we determine which model parameters are most important. The models abilities to predict joint moments about the human elbow during time-varying isometric tasks were examined. Inputs to the models were EMGs from eight muscles. Joint moment was the output, which was compared with the measured moment. Models varied in complexity, having up to 59 adjustable parameters. It was found that a seven adjustable parameter model could adequately estimate time-varying joint moments without substantial sacrifice in performance. The key parameters that were fit for each subject were two global gain factors, a time delay term, a non-linear EMG-force term, two muscle activation terms, and a term for skewing the length-tension curve with muscle activation. This approach offers advantages over optimization-based methods for estimating individual muscle forces. Most importantly, it accounts for the way muscles are activated, which makes it potentially powerful to evaluate patients with pathologies.

Comparison of mechanical energy expenditure of joint moments and muscle forces during human locomotion

1996 ◽  
Vol 29 (4) ◽  
pp. 405-415 ◽  
Author(s):  
Boris I. Prilutsky ◽  
Ludmila N. Petrova ◽  
Leonid M. Raitsin
Keyword(s):  
Energy Expenditure ◽  
Mechanical Energy ◽  
Human Locomotion ◽  
Muscle Forces ◽  
Joint Moments
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