# Study on Noise Reduction and Data Generation for sEMG Spectrogram Based User Recognition  
Jae-Myeong Kim  
IT Research Institute, Chosun University, Gwang-Ju 61452, Korea; [EMAIL_ADDRESS] (J.-M.K. ); [EMAIL_ADDRESS] (M.-G.K.) (c) (d) (e  
Biosignals are electrical signals generated inside the body and contain features that are unique to an individual. Representative biosignals include electromyogram (EMG), electrocardiogram (ECG), and electroencephalogram (EEG) [5]. Among such biosignals, various studies are being conducted to improve the performance of user identification technology using ECG signals that contain individual attributes based on the electrophysiological factors of the heart, the location and the size of the heart, and physical conditions. However, ECG signals cannot be changed when they are exposed externally because of problems including hacking. Furthermore, in ECG signals, the heart rate and waveform can change because of an individual's physical activity, the measurement time, or psychological effects. EMG signals, which can mitigate these drawbacks of ECG signals, are biosignals that are uniquely generated for each individual and generated by the muscle from behavioral characteristics, such as activities based on the development degree of each muscle and the muscle development degree between individuals. Additionally, as shown in Figure 2, surface electromyography (sEMG) signals can be measured on the skin surface of the desired muscle region to be acquired, rendering it easier to acquire signals compared with electrocardiography and electroencephalography. Moreover, sEMG signals can generate different signal patterns based on the muscles analyzed [6]. However, the databases used in existing user identification studies involving sEMG signals do not contain a sufficient number of subjects or repetitions for each motion. Because sEMG signals identify users using signals generated by muscles based on behavioral  
Figure 1: Block harmful genre content through user recognition. Figure 2: sEMG signal acquisition using various muscles. characteristics, they can be applied to user identification only when each motion is acquired using a sufficient number of repetitions. If the number of repetitions is insufficient, each motion may not be recognized, thereby hindering the application of the signals to user identification. In addition, most existing sEMG-based user identification studies applied temporal domain feature extraction methods to one-dimensional (1D) sEMG signals and applied signals to identification algorithms. However, sEMG signals are continuous signals with features that change constantly over time; furthermore, it is difficult to derive a clear cycle from them as it is difficult to repeat the same motion while maintaining a constant muscle strength based on time change. In other words, analyzing 1D sEMG signals acquired without a constant muscle strength and time in the temporal domain results in degraded user identification performance [7]. Hence, a preprocessing method was used in this study to address the problems of irregular signals and insufficient data to apply 1D sEMG signals acquired based on behavioral characteristics to the user identification system; furthermore, a user identification method using multidimensional feature sEMG spectrograms, including time-frequency information, is presented. First, irregular resting state signals and noise contained in sEMG signals were removed and sEMG signals were generated using matching pursuit, which enables data generation using a small amount of available data. Subsequently, the preprocessed 1D sEMG signals were applied to short-time Fourier transform (STFT), which is a method of extracting multidimensional features including time-frequency information. After adjusting the sEMG signals to a resolution that can be efficiently analyzed, they were transformed into sEMG spectrograms, and a convolutional neural network (CNN) was used to identify users in the final step.