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.

To study abnormal spatial patterns of muscle activation in hemiparetic stroke, we compared EMG activity in paretic and contralateral elbow and shoulder muscles of 10 hemiparetic subjects during 1.5-s voluntary isometric contractions, against five to eight different loads. Isometric forces were generated in eight directions, referenced to a plane orthogonal to the long axis of the forearm, and were recorded by a three degrees of freedom load cell, mounted at the wrist. Surface and intramuscular EMGs of six elbow and six shoulder muscles were recorded from both impaired and contralateral upper extremities of each subject. The spatial characteristics of EMG activation of individual muscles were summarized using two measures. The first, called the ‘net resultant EMG vector’ is a new measure which calculated the vector sum of EMG magnitudes for each of the eight directions, and the second, index of EMG focus, is a measure of the range of EMG activation recorded for each load level. Use of these measures permitted us to describe spatial EMG characteristics quantitatively, which has not been done previously. We observed consistent and statistically significant shifts in the resultant EMG vector directions in the impaired limb, especially in shoulder and other proximal muscles. Significant increases in the angular range of EMG activity were also identified and were most evident at the elbow. Correlation analysis techniques were used to assess the degree of coactivation of different muscle pairs. There were consistent EMG coactivation patterns observed across all subjects (both normal and hemiparetic). However, in spastic-paretic limbs, additional novel coactivational relationships were also recorded, especially between elbow flexors/shoulder abductors and elbow extensors/shoulder adductors. These novel coactivation patterns represent a reduction in the number of possible muscle combinations, or in the number of possible ‘synergies’ in the paretic limb of the stroke subject. This reduction in number of ‘synergies’ could result from a loss of descending command options; from an increased reliance on residual, descending brainstem pathways (such as the reticulospinal and vestibulospinal projections); from changes in spinal interneuronal excitability; or from a combination of several of these factors. The relative merits of these hypotheses are addressed.