Sami ARICA, Rouhollah KIAN ARA, Kerem Tuncay ÖZGÜNEN

ON THE CLASSIFICATION OF HAND MOVEMENTS WITH ELECTROMYOGRAM SIGNALS OBTAINED FROM ARM MUSCLES FOR CONTROLLING HAND PROSTHESIS

Electromyography (EMG) signals are outcomes of skeletal muscle activities. In this study EMG signal is read non-invasively from the skin surface by placing electrodes on the skin of specified muscle (surface EMG - SEMG). The aim of the study is to generate control signals from SEMGs measured from four hand muscles; Extensor carpi radialis, Palmaris longus, Pronator quadratus and Flexor digitorum superficialis to navigate a prosthetic hand. The SEMGs for five hand movements; finger flexion, wrist flexion, wrist extension, pronation, supination have been acquired. The features have been computed from the windowed EMG of a 0.512 second interval. From each muscle (channel), root mean square value, mean frequency and peak frequency are employed as features. The mean frequency is computed from the discrete Fourier transform, by counting number of zero crossings and using minimum norm subspace frequency estimation technique. The peak frequency is also obtained by employing the discrete Fourier transform. These features and their pairwise combinations have been classified with support vector machine. The classifications have been done for two scenarios: 1. For each subject the right (left) hand movement is classified from the right (left) arm EMG data. 2. The left (right) hand movement of a subject is classified from the right (left) arm EMG data of the same subject. The right hand and left hand data recorded from two males and two females. The average right-hand success of the classification was 82.0%, while the left-hand categorization was 83.5%. Interestingly, the left-hand versus right-hand and the right-hand versus left-hand classification success was obtained 65.7%.

Keywords:

Electromyography, hand prosthesis, support vector machine,

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