Daohang Sha

Daohang Sha, James L. Patton, Ferdinando A. Mussa-Ivaldi

Moving our hands smoothly is essential to execute ordinary tasks, such as carrying a glass of water without spilling. Past studies have revealed a natural tendency to generate smooth trajectories when moving the hand from one point to another in free space. Here we provide a new perspective on movement smoothness by showing that smoothness is also enforced when the hand maintains contact with a curved surface. Maximally smooth motions over curved surfaces occur along geodesic lines that depend on fundamental features of the surface, such as its radius and center of curvature. Subjects were requested to execute movements of the hand while in contact with a virtual sphere that they could not see. We found that with practice, subjects tended to move their hand along smooth trajectories, near geodesic pathways joining start to end positions, to reduce contact forces with constrained boundary, variance of contact force, tangential velocity profile error and sum of square jerk along the time span of movement. Furthermore, after practicing movements in a region of the sphere, subjects executed near-geodesic movements, less contact forces, less contact force variance, less tangential velocity profile error and less sum of square jerk in a different region. These findings suggest that the execution of smooth movements while the hand is in contact with a surface is a means for extracting information about the surface's geometrical features.

Daohang Sha, James S Thomas

Background The development of a simulation model of full body reaching tasks that can predict endeffector trajectories and joint excursions consistent with experimental data is a non-trivial task. Because of the kinematic redundancy inherent in these multi-joint tasks there are an infinite number of postures that could be adopted to complete them. By developing models to simulate full-body reaching movements in 3D space we can begin to explore cost functions that may be used by the central nervous system to plan and execute these movements. Methods A robust simulation model was developed using 1) graphic-based modeling tools to generate an inverse dynamics controller (SimMechanics), 2) controller parameterization methods, and 3) cost function criteria. An adaptive weight coefficient based on the final motor task error (i.e. distance between end-effector and target at the end of movement) was proposed to balance motor task error and physiological cost terms (e.g. joint power). The output of the simulation models using different cost controller functions based on motor task error or motor task error and various physiological cost terms (e.g. joint power, center of mass displacement) were compared to experimental data from 15 healthy participants performing full body reaching movements. Results In sum, the best fit to the experimental data was obtained by minimizing motor task error, joint power, and center of mass displacement. Simulation and experimental results demonstrated that the proposed method is effective for the simulation of large-scale human skeletal systems. Conclusions This method can reasonably predict the whole body reaching movements including final postures, joint power and movement of COM using simple algebraic calculations of inverse dynamics and forward kinematics.