Trajectory Tracking Methodology using sEMG Signals for Tracking Finger Motions

This paper presents the research advances in the development of a novel methodology for tracking finger motions using superficial electromyographic signals captured from the forearm of healthy subjects. Electromyographic data is recorded while the hand of subjects is constricted to grasps a set of spheres with a small variation in diameter. Five muscles are monitored with non-invasive electrodes placed on the skin of volunteers while a set of grasp-hold-relax tasks are carried out randomly. A preprocess stage is performed to extract time domain features from data, with the purpose of address both the curse of dimensionality and the issues related to the nonstationary behavior of electromyographic signals. A pattern recognition module is used to classify data and to assign the position of the fingers with each sphere grasped. Finally, a neuronal model predictive controller is designed which is able to control the position of the fingers using predefined trajectories. The applicability of the methodology is presented via simulations of a servo system that models one joint angle motion of the thumb.