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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 dynamics govern 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 dynamics characterize how muscle activations are transformed into muscle forces. The third step requires a model of the musculoskeletal geometry to transform muscle forces to joint moments. Finally, the equations of motion allow 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.

Thirty experienced male swimmers with body types ± 1 SD of the mean of selected body form parameters reported for elite male swimmers were recruited for the study. During three freestyle flip turns, selected kinetic, hydrodynamic, and kinematic variables of the push-off following a flip turn were recorded. Kinetics were recorded via a 2D vertically mounted forceplate that recorded peak push-off force and total impulse. The acceleration of each swimmer’s center of gravity and wall exit velocity were calculated from underwater videography. Hydrodynamic peak drag force and drag impulse were calculated from the kinetic and kinematic data using a derivative of Newton’s second law. A stepwise regression yielded peak drag force, peak propulsive force, and push-off time in the final regression equation (R = 0.80; R ² = 0.64). Beta values indicated that the peak drag force carried the highest weighting of the three variables. The results of the stepwise regression indicated that a combination of a low peak drag force high peak propulsive force, and increased wall push-off time produced the fastest final push-off velocity.

The measurement of lumbar spine motion is an important step for injury prevention research during complex and high impact activities, such as cricket fast bowling or javelin throwing. This study examined the performance of two designs of a lumbar rig, previously used in gait research, during a controlled high impact bench jump task. An 8-camera retro-reflective motion analysis system was used to track the lumbar rig. Eleven athletes completed the task wearing the two different lumbar rig designs. Flexion extension data were analyzed using a fast Fourier transformation to assess the signal power of these data during the impact phase of the jump. The lumbar rig featuring an increased and pliable base of support recorded moderately less signal power through the 0–60 Hz spectrum, with statistically less magnitudes at the 0–5 Hz (p = .039), 5–10 Hz (p = .005) and 10–20 Hz (p = .006) frequency bins. A lumbar rig of this design would seem likely to provide less noisy lumbar motion data during high impact tasks.

The purpose of this study was to quantify the effect of soft tissue artifact during three-dimensional motion capture and assess the effectiveness of an optimization method to reduce this effect. Four subjects were captured performing upper-arm internal-external rotation with retro-reflective marker sets attached to their upper extremities. A mechanical arm, with the same marker set attached, replicated the tasks human subjects performed. Artificial sinusoidal noise was then added to the recorded mechanical arm data to simulate soft tissue artifact. All data were processed by an optimization model. The result from both human and mechanical arm kinematic data demonstrates that soft tissue artifact can be reduced by an optimization model, although this error cannot be successfully eliminated. The soft tissue artifact from human subjects and the simulated soft tissue artifact from artificial sinusoidal noise were demonstrated to be considerably different. It was therefore concluded that the kinematic noise caused by skin movement artifact during upper-arm internal-external rotation does not follow a sinusoidal pattern and cannot be effectively eliminated by an optimization model.

Anterior cruciate ligament (ACL) injuries are costly. Sidestep technique training reduces knee moments that load the ACL. This study examined whether landing technique training alters knee moments. Nineteen team sport athletes completed the study. Motion analysis and ground reaction forces were recorded before and after 6 weeks of technique modification. An inverse dynamic model was used to calculate three-dimensional knee loading. Pre- and postintervention scores were compared using paired t tests. Maximal knee flexion angle during landing was increased following training. There was no change in valgus or flexion moments, but an increase in peak internal rotation moment. This increase in internal rotation moment may increase the risk of ACL injury. However, the increased angle at which the peak internal rotation moment occurred at follow up may mitigate any increase in injury risk by reducing load transmission.

Gait analysis together with musculoskeletal modeling is widely used for research. In the absence of medical images, surface marker locations are used to scale a generic model to the individual’s anthropometry. Studies evaluating the accuracy and reliability of different scaling approaches in a pediatric and/or clinical population have not yet been conducted and, therefore, formed the aim of this study. Magnetic resonance images (MRI) and motion capture data were collected from 12 participants with cerebral palsy and 6 typically developed participants. Accuracy was assessed by comparing the scaled model’s segment measures to the corresponding MRI measures, whereas reliability was assessed by comparing the model’s segments scaled with the experimental marker locations from the first and second motion capture session. The inclusion of joint centers into the scaling process significantly increased the accuracy of thigh and shank segment length estimates compared to scaling with markers alone. Pelvis scaling approaches which included the pelvis depth measure led to the highest errors compared to the MRI measures. Reliability was similar between scaling approaches with mean ICC of 0.97. The pelvis should be scaled using pelvic width and height and the thigh and shank segment should be scaled using the proximal and distal joint centers.