Research on Construction Method of Neural Network Model with Spatiotemporal Coordination during Arm Transport and Hand Preshape

Abstract

Based on the VITE (Vector Integration to Endpoint) point-to-point trajectory formation model, a new neural network model which has biological meaning is proposed in this paper to explain the spatiotemporal coordination among arm transport component, hand preshaping component and palm orientation component during reach-to-grasp task. In addition to adjusting the overall speed of movement components, the basal ganglia thalamus cortex loop is exploited and coupled neurons are set up to ensure the exchange of status information with one another. In this way, the spatiotemporal coordination of reach-to-grasp movement is achieved, laying a solid foundation for robust 3-D gesture tracking. Model changes the gating input signal, optimizes the updating method of maximum grip aperture, and also considers experimental perturbation conditions. Simulation results demonstrate that the proposed model robustly achieves spatiotemporal coordination during prehension and effectively extracts the important kinematic characteristics of human prehension, meanwhile the setting time is reduced by 13 percent.