# EMG Based Gesture Recognition Using the Unbiased Difference Power
## 4 Result  
We generated a noise signal using raw signal information. To add noise to the raw signal, which is known as noisy signal, we added additive Gaussian noise with zero mean and \(\sigma^{2}_{v_{noise}}\) variance to the raw signal. The variance of noise signal, \(\sigma^{2}_{v_{noise}}\) is calculated as  
\[\sigma^{2}_{v_{noise}}= \begin{cases}\frac{\sigma^{2}_{v}}{10}-\sigma^{2}_{v_{ raw}}&,\ \ for\ SNR_{raw}\geq SNR\\ e&,\ \ otherwise\end{cases}  \tag{9}\]  
where \(\sigma^{2}_{s}\) is variance of activated signal, and \(\sigma^{2}_{v_{raw}}\) is the resting period variance of raw signal. \(\epsilon\) is a small value, which is set \(10^{-5}\) in this study. \(SNR_{raw}\) is a SNR of the raw signal, which is calculated as \(SNR_{raw}=10\log_{10}(\sigma^{2}_{s}/\sigma^{2}_{v_{raw}})\). We set five different SNR levels between 0 and to 20 dB. First, we confirm the classification rate of the raw data. Figure 5 shows the classification accuracy of the raw data. In this case, both the training and test data are selected from the raw data. Because these results confirm the accuracy of the raw data, we only used the difference power as a feature. All subjects showed an average classification accuracy of more than 60%. For the performance evaluation, we compare the results using the proposed unbiased difference power with two different conventional cases: applying no denoising algorithm or unbiased feature (None) and conventional denoising algorithm based on wavelet reconstruction (denoising) [17]. The proposed unbiased difference power is divided into two categories based on the method used to obtain the noise power. Leave-one-out validation is used for the performance evaluation. Moreover, we repeated the validation for 100 times to ensure the evaluation result. For the classification using the proposed unbiased difference power, noiseless feature, \(E[\overline{\Delta}_{\infty}x[n]]=E[\Delta x[n]]-2\sigma^{2}_{v_{max}}\), is used for training set, whereas calibrated noisy features, \(E[\overline{\Delta}x[n]]=E[\Delta x[n]]-2\sigma^{2}_{v_{noise}}\), is used as test data. For the \(\sigma^{2}_{v_{noise}}\), we use the ideal noise power and estimated noise power. Figure 4: Feature variation based on SNR. Black solid line shows unbiased difference power obtained using ideal noise power. Gray dashed line shows unbiased difference power obtained using estimated noise power. Figure 6 shows the confusion matrices of subject 1 which shows the best performance in classification of raw data. For the case none shown in Figure 6a, the diagonal, which represents the accuracy, decreases with the SNR. This phenomenon, however, is improved by applying denoising or using the unbiased difference power. In particular, the confusion matrices of the cases using the unbiased difference power in Figure 6c,d show outstanding improvement, i.e., low SNRs, compared with those of the denoising case in Figure 6b. Figure 5: Classification result of raw data. (**a**) Confusion matrix of subject two. (**b**) Average accuracy of 14 subjects. Then, to expand the confirmed results in single subject, we confirm the classification results of all subjects used for evaluation. Figure 7 shows the changes in the recognition accuracy of 14 subjects in four different situations. For the none case, the accuracy decreases rapidly with the SNR regardless of the subject, as shown in Figure 7a. The accuracy decrease associated with the SNR occurs even in the denoising case, as shown in Figure 7b. However, using the proposed unbiased feature, the accuracy is maintained even when the SNR decreases. As shown in Figure 7c,d, even when the SNR is 5 dB, the accuracy of all subjects is maintained. When the SNR is 0 dB, a slight decrease in accuracy appears in most of the subjects, but the result remained the highest. These results indicate that the proposed unbiased difference power enables robust gesture recognition against noise. Figure 6: Confusion matrix of subject 1. (**a**) None. (**b**) Denoising. [17] (**c**) Proposed method using ideal noise power.