Pattern Classification and Its Applications to Control of Biomechatronic Systems

Abstract

This chapter presents a new approach to identify, classify, and control biomechatronic systems, which are controlled via superficial electromyographic signals generated by the upper limbs. Electromyographic data are 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 noninvasive 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 the data, with the purpose of addressing both the course of dimensionality and the issues related to the nonstationary behavior of electromyographic signals. A pattern recognition module is used to classify the data and to assign the extracted features to categories corresponding to each sphere grasped. A tracking generator is proposed using artificial neural networks, which are trained to learn the dynamics of finger motions. The performance of the methodology is evaluated in simulations and via real-time implementation with an embedded system.