INTELLIGENT CONTROL OF A PROSTHETIC ARM BY EMG PATTERN RECOGNITION
Abstract
An electromyographic (EMG) signal pattern recognition system is developed for the precise identification of a motion and speed command from the EMG signals in order to establish the intelligent control of a prosthetic arm. A probabilistic model of the EMG patterns formulated in the feature space of integral absolute value (IAV) enables the derivation of the sample probability density function (SPDF) of a command in the feature space of IAV. A nonlinear transformation from the feature space of IAV to the feature space of variance and zero crossings is provided to establish the SPDF of a command in the feature space of variance and zero crossings where classification is carried out. Classification is carried out through a multiclass sequential decision procedure the decision rule and the stopping rule of which are designed by the simple mathematical formulas related with the upper bound of the probability of error. Speed and motion prediction incorporate with decision procedure to enhance the decision speed and reliability. The result of classification is fed into the decomposition scheme in which speed of each primitive motion of a combined motion is directly assigned by the decomposition rule, so as to establish the precise and direct control. A learning procedure provides the adaptation of the decision processor to the long-term pattern variation. The overall procedure is explained as an application of the hierchically intelligent control system theory. The effectiveness of the theories and procedures developed is experimentally verified by the analysis of collected data and the computer simulation of the developed procedures.
Degree
Ph.D.
Subject Area
Electrical engineering
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