# EMG Based Gesture Recognition Using the Unbiased Difference Power
## 5 Conclusions  
In this study, we propose an effective unbiased feature for a robust gesture recognition of noise. We extract an unbiased difference power by subtracting twice the noise power, which is a noise bias for the difference power. The validity of the proposed unbiased feature is confirmed by deriving an equation using the feature. For the noise power, we use the ideal and calculated values to confirm the difference between the real and ideal situations. The noise power estimation was simply obtained by calculating the power of resting period. Due to the characteristics of EMG and background noise, it is possible to simplify the noise estimation process. For performance evaluation, the recognition accuracy is used as an index. Moreover, we compared the results using the proposed unbiased feature with the results of the other two cases. None of the denoising algorithms were used in the first case, whereas a denoising algorithm based on wavelet reconstruction was used in the second case. The recognition accuracy by not applying any denoising algorithms decreases significantly as the SNR decreased. By applying the denoising algorithm, the rapid decrease in the previous case is alleviated. However, the results obtained using the proposed unbiased difference power indicate that the decoding accuracy is maintained, although the SNR decreases. The reason why the decoding accuracy is maintained although the SNR decreases is that the proposed unbiased feature maintains a similar value even under different noise situations, whereas difference power varies greatly. The results indicate that the proposed unbiased difference power effectively overcomes the interference caused by noise in the gesture recognition.  
For future work, it is necessary to apply the proposed unbiased difference power to a recorded noisy signal. Recording the EMG signal from various noisy situations and confirming the performance of the unbiased difference power should be done for practical uses. The evaluation with various classifiers is also needed for more rigorous performance evaluation. Moreover, more unbiased features should be derived. Since we only introduced an unbiased difference power, more unbiased features could be introduced  
Figure 8: Subject-averaged accuracy comparison. Colored bar represents average accuracy using each method explained in legend; white bar above colored bar represents corresponding standard deviation.  
in similar methods. These unbiased features would help EMG-based gesture recognition to be more robust to noise.  
Conceptualization, H.-C.S.; methodology, K.K.; software, K.K.; validation, K.K. H.-C.S.; writing--original draft preparation, K.K.; writing--review and editing, K.K., H.-C.S.; visualization, K.K.; supervision, project administration, and funding acquisition, H.-C.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MEST) (No. 2020R1A2C1101978).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} EMG & Electromyography \\ SNR & Signal-to-noise ratio \\ LDA & Linear discriminant analysis \\ \end{tabular}