A neuromusculoskeletal tracking (NMT) method was developed to estimate muscle forces from observed motion data. The NMT method combines skeletal motion tracking and optimal neuromuscular tracking to produce forward simulations of human movement quickly and accurately. The skeletal motion tracker calculates the joint torques needed to actuate a skeletal model and track observed segment angles and ground forces in a forward simulation of the motor task. The optimal neuromuscular tracker resolves the muscle redundancy problem dynamically and finds the muscle excitations (and muscle forces) needed to produce the joint torques calculated by the skeletal motion tracker. To evaluate the accuracy of the NMT method, kinematics and ground forces obtained from an optimal control (parameter optimization) solution for maximum-height jumping were contaminated with both random and systematic noise. These data served as input observations to the NMT method as well as an inverse dynamics analysis. The NMT solution was compared to the input observations, the original optimal solution, and a simulation driven by the inverse dynamics torques. The results show that, in contrast to inverse dynamics, the NMT method is able to produce an accurate forward simulation consistent with the optimal control solution. The NMT method also requires 3 orders-of-magnitude less CPU time than parameter optimization. The speed and accuracy of the NMT method make it a promising new tool for estimating muscle forces using experimentally obtained kinematics and ground force data.

Determining individual muscles forces from human performance has greatly depended on the quality of inverse dynamics solutions, as muscle force decomposition remains the only feasible approach for determining muscle forces non-invasively in human movement. However, legitimate questions about the accuracy of inverse dynamics arise, with resultant torques/forces failing to drive a forward model through the observations from which they were derived. While optimization of forward dynamics to match experimental data is considered more accurate, the simplicity and low computational costs of inverse methods are favored over the large computing requirements of optimization. In this paper, an evolution in the inverse methods for computing accurate and reliable torques is presented, whereby the relative speed of inverse dynamics is combined with the desired accuracy of forward dynamics. This method is based on developing a nonlinear tracker that determines the net muscle torques which accurately follow clinically observed kinematics and ground reaction forces. The results show that the method is robust and can produce accurate estimates of the joint torques during movement. The method outlined here is a necessary first step to solving the muscle force indeterminancy problem more efficiently.

The innovations made in the design, implementation and assessment of an How People Learn (HPL)-inspired undergraduate course on biomechanics was discussed. Major focus of the current activities in the biomechanics domain that are based on the HPL model draws all the latest learning materials developed within the biomechanics domain. The goals of the course were: to teach students about relationships between musculoskeletal structure and function in the context of human movement, provide real life examples of biomechanical situations that are familiar and interesting and teach students to think about complex problems. The results show that the HPL model scored better on the questions designed to test transfer.