Movement curvature planning through force field internal models

Abstract

Human motion studies have focused primarily on modeling straight point-to-point reaching movements. However, many goal-directed reaching movements, such as movements directed towards oneself, are not straight but rather follow highly curved trajectories. These movements are particularly interesting to study since they are essential in our everyday life, appear early in development and are routinely used to assess movement deficits following brain lesions. We argue that curved and straight-line reaching movements are generated by a unique neural controller and that the observed curvature of the movement is the result of an active control strategy that follows the geometry of one's body, for instance to avoid trajectories that would hit the body or yield postures close to the joint limits. We present a mathematical model that accounts for such an active control strategy and show that the model reproduces with high accuracy the kinematic features of human data during unconstrained reaching movements directed toward the head. The model consists of a nonlinear dynamical system with a single stable attractor at the target. Embodiment-related task constraints are expressed as a force field that acts on the dynamical system. Finally, we discuss the biological plausibility and neural correlates of the model's parameters and suggest that embodiment should be considered as a main cause for movement trajectory curvature.

A Robotics-Based Approach to Modeling of Choice Reaching Experiments on Visual Attention

“…In fact, there are several mathematical models for velocity profiles such as the Minimum Hand Jerk model, the Minimum Commanded Torque Change model, etc. A recent review by Petreska and Billard (2009) suggested that a modified vector integration to endpoint (VITE) model (Bullock and Grossberg, 1988) yields the best fit to human movement trajectories. In the current paper we used the VITE-model as the reference model for our model.…”

Tunnel-shaped potential force fields for improved hand-guiding of robotic arms

“…Another possibility would be the use of path planning algorithms. The approach of the TSPFF was inspired by the idea of Petreska and Billard that the human brain creates and uses potential fields to improve the movements of human extremities, for example, to avoid hitting the joint limits [7].…”

Revisiting a study of callosal apraxia: The right hemisphere can imitate the orientation but not the position of the hand

“…Finally, neural activation is consistently observed in a motor brain area contralateral to the imitating hand, namely the dorsolateral premotor cortex (BA 6) (Decety et al, 1997;Hermsdörfer et al, 2001;Mühlau et al, 2005;Peigneux et al, 2004). This brain area may implement the motor command of the hand in the form of a motion attractor which guides imitation by a representation of the desired hand posture (Petreska & Billard, 2009). …”