Multi-Finger Movement Decoding using EMG

K.-J. You and H.-C. Shin (Korea)


Surface EMG, finger motions, neural signal processing, HCI


We provide a novel method to infer finger flexing motions using a four-channel surface electromyogram (EMG). Sur face EMG signals can be recorded from the human body non-invasively and easily. Surface EMG signals in this study were obtained from four channel electrodes placed around the forearm. The motions consist of the flexion of five single fingers (thumb, index finger, middle finger, ring finger, and little finger) and three multi-finger motions. The maximum likelihood estimation was used to infer the finger motions. Experimental results have shown that this method can successfully infer the finger flexing motions. The aver age accuracy was as high as 97.75%

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