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Med Sci Sports Exerc. 2005 Nov;37(11):1911-6.

Estimation of muscle forces and joint moments using a forward-inverse dynamics model.

Author information

1
Center for Biomedical Engineering Research, Department of Mechanical Engineering, University of Delaware, Newark, 19716, USA. buchanan@me.udel.edu

Abstract

PURPOSE:

This paper presents a forward dynamic neuromusculoskeletal model that can be used to estimate and predict joint moments and muscle forces. It uses EMG signals as inputs to the model, and joint moments predicted are verified through inverse dynamics. The aim of the model is to estimate or predict muscle forces about a joint, which can be used to estimate the corresponding joint compressive forces, and/or ligament forces in healthy and impaired subjects, based on the way they activate their muscles.

METHODS:

The estimation of joint moments requires three steps. In the first step, muscle activation dynamics govern the transformation from the EMG signal to a measure of muscle activation--a time-varying parameter between 0 and 1. In the second step, muscle contraction dynamics characterize how muscle activations are transformed into muscle forces. The final step requires a model of the musculoskeletal geometry to transform muscle forces to joint moments. Each of these steps involves complex, nonlinear relationships.

RESULTS:

An application is provided to demonstrate how this model can be used to study the forces in the healthy ankle during dynamometer trials and during gait. The model-predicted estimates of joint moment were found to match experimentally determined values closely.

CONCLUSION:

Neuromusculoskeletal models that use EMG as inputs can be employed to accurately estimate joint moments. The muscle forces predicted from these models can be used to better understand tissue loading in joints, and to provide in vivo estimates of tensile ligament forces and compressive cartilage loads during dynamic tasks. This tool has great potential for aiding in the study of injury mechanisms in sports.

PMID:
16286861
[Indexed for MEDLINE]

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