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Jure Zakotnik

Publications and source records attributed to Jure Zakotnik.

2 recordsLinked to original sources

Co-contraction and passive forces facilitate load compensation of aimed limb movements.

Vertebrates and arthropods are both capable of load compensation during aimed limb movements, such as reaching and grooming. We measured the kinematics and activity of individual motoneurons in loaded and unloaded leg movements in an insect. To evaluate the role of active and passive musculoskeletal properties in aiming and load compensation, we used a neuromechanical model of the femur-tibia joint that transformed measured extensor and flexor motoneuron spikes into joint kinematics. The model comprises three steps: first, an activation dynamics module that determines the time course of isometric force; second, a pair of antagonistic muscle models that determine the joint torque; and third, a forward dynamics simulation that calculates the movement of the limb. The muscles were modeled in five variants, differing in the presence or absence of force-length-velocity characteristics of the contractile element, a parallel passive elastic element, and passive joint damping. Each variant was optimized to yield the best simulation of measured behavior. Passive muscle force and viscous joint damping were sufficient and necessary to simulate the observed movements. Elastic or damping properties of the active contractile element could not replace passive elements. Passive elastic forces were similar in magnitude to active forces caused by muscle contraction, generating substantial joint stiffness. Antagonistic muscles co-contract, although there was no motoneuronal coactivation, because of slow dynamics of muscle activation. We quantified how co-contraction simplified load compensation by demonstrating that a small variation of the motoneuronal input caused a large change in joint torque.

Animals↗

A posture optimization algorithm for model-based motion capture of movement sequences.

We have developed and evaluated a new optical motion capture approach that is suitable for a wide range of studies in neuroethology and motor control. Based on the stochastic search algorithm of Simulated Annealing (SA), it utilizes a kinematic body model that includes joint angle constraints to reconstruct posture from an arbitrary number of views. Rather than tracking marker trajectories in time, the algorithm minimizes an error function that compares predicted model projections to the recorded views. Thus, each video-frame is analyzed independently from other frames, enabling the system to recover from incorrectly analyzed postures. The system works with standard computer and video equipment. Its accuracy is evaluated using videos of animated locust leg movements, recorded by two orthogonal views. The resulting joint angle RMS errors range between 0.7 degrees and 4.9 degrees, limited by the pixel resolution of the digital video. 3D-movement reconstruction is possible even from a single view. In a real experimental application, stick insect walking sequences are analyzed with leg joint angle deviations between 0.5 degrees and 3.0 degrees. This robust and accurate performance is reached in spite of marker fusions and occlusions, simply by exploiting the natural constraints imposed by a kinematic chain and a known experimental setup.

Algorithms↗