A neural network model for coordination of hand gesture during reach to grasp
In this paper a neural network model for spatio-temporal coordination of hand gesture during prehension is proposed. The model includes a simplified control strategy for whole hand shaping during grasping tasks, that provides a realistic coordination among fingers. This strategy uses the increasing evidence that supports the view of a synergistic control of whole fingers during prehension. In this control scheme, only two parameters are needed to define the evolution of hand shape during the task performance. The proposal involves the design and development of a Library of Hand Gestures consisting of motor primitives for finger pre-shaping of an anthropomorphic dextrous hand. Through computer simulations, we show how neural dynamics of the model leads to simulated grasping movements with human-like kinematic features. The model can provide clear-cut predictions for experimental evaluation at both the behavioural and neural levels as well as a neural control system for a dextrous robotic hand.
neural networks reach to grasp computer simulation dextrous robotic hands motor control motor and premotor cortex hand posture principal component analysis