# 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%.  
EMG; gesture recognition; feature extraction; difference power; unbiased difference power; linear discriminant analysis +
Footnote †: journal:  
Most EMG features are deteriorated by inevitable noise added during the recording. To handle the problem, various studies have been conducted to reduce the effect of noise [14; 15; 16; 17]. Several studies have been conducted to identify features that are robust to noise problems [14; 15]. These studies mainly compare the accuracies using different features in various noise levels, and confirm which features are relatively robust to noise. In some studies, denoising algorithms are introduced to reduce noise contamination in EMG signals [16; 17]. These studies remove the noise by thresholding wavelet coefficients. Denoising algorithms may induce the information loss [18]. The feature selection is not a fundamental solution for making the gesture recognition robust against the noise.  
The difference power is a feature frequently used in EMG-based gesture recognition [19; 20; 21; 22]. However, the difference power is biased by the noise contaminated in the EMG signal. Because of the noise bias, the difference power varies greatly depending on noise levels. Therefore, the noise bias must be removed from the extracted difference power for the robust gesture recognition against noise.  
In this paper, we propose a feature known as \"unbiased difference power\" for the robust gesture recognition of noise. The unbiased difference power is extracted by excluding the noise-biased part from the difference power. To determine the noise-biased part, we derive the difference power mathematically. For the performance evaluation, we use the recognition accuracy. In addition, the recognition accuracy based on the proposed unbiased difference power is compared with the accuracy of the two conventional cases. Only when the proposed unbiased difference power is used as a feature was the recognition accuracy maintained, even when the signal-to-noise ratio (SNR) decreased.