A model-based motion capture marker location refinement approach using inverse kinematics from dynamic trials.

Marker-based motion capture techniques are commonly used to measure human body kinematics. These techniques require an accurate mapping from physical marker position to model marker position. Traditional methods utilize a manual process to achieve marker positions that result in accurate tracking. In this work, we present an optimization algorithm for model marker placement to minimize marker tracking error during inverse kinematics analysis of dynamic human motion. The algorithm sequentially adjusts model marker locations in 3-D relative to the underlying rigid segment. Inverse kinematics is performed for a dynamic motion capture trial to calculate the tracking error each time a marker position is changed. The increase or decrease of the tracking error determines the search direction and number of increments for each marker coordinate. A final marker placement for the model is reached when the total search interval size for every coordinate falls below a user-defined threshold. Individual marker coordinates can be locked in place to prevent the algorithm from overcorrecting for data artifacts such as soft tissue artifact. This approach was used to refine model marker placements for eight able-bodied subjects performing walking trials at three stride frequencies. Across all subjects and stride frequencies, root mean square (RMS) tracking error decreased by 38.4% and RMS tracking error variance decreased by 53.7% on average. The resulting joint kinematics were in agreement with expected values from the literature. This approach results in realistic kinematics with marker tracking errors well below accepted thresholds while removing variance in the model-building procedure introduced by individual human tendencies.

Extracting low-velocity concentric and eccentric dynamic muscle properties from isometric contraction experiments.

Determining dynamic properties of mammalian muscles, such as activation characteristics or the force-velocity relation, challenges the experimentalist. Tracking system, apparatus stiffness, load oscillation, force transducer, other sensors, and additional measuring devices may be incorporated, integrated and evaluated in an experimental set-up. In contrast, isometric contraction experiments (ICEs) are less challenging, but are generally not considered to reveal dynamic muscle properties. Yet, a sensitivity analysis of our muscle model discloses the influence of concentric, eccentric and activation parameters on isometric force development. Accordingly, we used solely experimental ICE data to identify muscle model parameters that generally describe concentric as well as eccentric muscle performance. In addition, we compared two different activation dynamics in regards to their physiological relevance to improve model-fits to ICE data. To this end, we optimized different combinations of such dynamic parameter subsets with respect to their influence on contraction solutions. Depending on muscle length in our optimized model, the contractile element reached shortening peaks during activation in the range 9-39% of its maximum contraction velocity, and about 8-25% during lengthening when deactivated. As a first result, we suggest one formulation of activation dynamics to be superior. Second, the step in slope of the force-velocity relation at isometric force was found to be the least influential among all dynamic parameters. Third, we suggest a specially designed isometric experimental set-up to estimate this transition parameter. Fourth, because of an inconsistency in literature, we developed a simple method to determine switching times of the neural stimulation and thus electro-mechanical delay (EMD) values from measuring muscle force in ICEs only.