A Classification Method of Hand Movements Using Multi Channel Electrode

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Abstract
In this study, we describe the classification method of hand movements using 96 channels matrix-type(16times6) of multi channel surface electrode. Today, there are many systems that use the EMG as a control signal. As for those ordinary systems, it has some problem like most of them require the definition of measuring position. We design the new system with multi channel electrode to solve some of those conventional problems. Our system that has 96 channels electrode does not need to select a particular electrode position. Only attaching this electrode, we can obtain correct EMG and this way means providing with a simple and easy way. The purpose of this study is development of the EMG pattern recognition method using multi channel electrode. From measured 96 channels EMG data, we chose one line (16channels) of this electrode with the smallest noise. The EMG signal is recognized by canonical discriminant analysis. In order to recognize the EMG signal, the first three eigenvectors are chosen to form a discriminant space. And Euclidean distance is applied to classify the EMG. From the experiment in this method, we can discriminate 12 movements of the hand including four finger movements. And the recognition rate that can be done in real-time was measured at 80 percent on the average

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