EMG Based Gesture Recognition Using the Unbiased Difference Power

Abstract

In this paper, we propose an unbiased difference power that is robust against noise as a feature for electromyography (EMG)-based gesture recognition. The proposed unbiased difference power is obtained by subtracting the noise-biased part from the difference power. We derive the difference power equation and discover that the difference power is biased by twice the noise power. For noise power estimation, we utilized the characteristics of the EMG signal and estimated the noise power from the resting period. For performance evaluation, we used EMG signals provided by the open source Ninapro project database. We used the recognition accuracy as an evaluation index. We compare the recognition accuracy of the case using the proposed unbiased feature with those of two conventional cases. Experimental results show that the proposed unbiased difference power improves the accuracy compared with conventional ones. As the noise level increases, cases where the proposed unbiased difference power is used show a clear improvement in accuracy compared with the two conventional cases. For the signal-to-noise ratio (SNR) of 0 dB, the proposed unbiased difference power improves the average accuracy by more than 12%.