# Study on Noise Reduction and Data Generation for sEMG Spectrogram Based User Recognition
## 5 Conclusions  
In existing user identification studies using sEMG signals, experiments with sEMG signals were conducted after removing noise using only simple filters. sEMG signals are time series data acquired over time and are generated based on different activity degrees of each muscle when performing a motion; hence, they can be applied to user identification once a sufficient amount of data is acquired. However, most sEMG databases contain a minimal amount of data and signals are acquired irregularly as it is difficult to acquire constant signals based on the conditions involved when acquiring data by repeating motions. Furthermore, because sEMG signals, which are time series data, cannot be repeated while maintaining a constant muscle strength over time, it is difficult to obtain a clear periodicity; hence, user identification performance is degraded when analyzed as 1D features. In this study, a preprocessing method was employed for solving problems of irregular signals and insufficient data for a user identification system using 1D sEMG signals obtained based on behavioral characteristics; furthermore, a user identification method using multidimensional feature sEMG spectrograms containing time-frequency information was proposed. After removing irregular resting state signals and noise included in the sEMG signals, the sEMG signal data were generated using matching pursuit, which enables signals to be generated using a small amount of data and quick data generation using a relatively simple formula compared with other data generation techniques. The similarity of the generated sEMG signals was verified using cross-correlation similarity, which yielded 90% to 99% similarity with the raw data. The preprocessed 1D sEMG signals were applied with STFT, a multidimensional feature extract method containing time-frequency information, and the resolution was changed to enable efficient analysis of the sEMG signals. Subsequently, after transforming the signals into sEMG spectrograms, a CNN model was used to perform final user identification. The proposed system comprised processes of sEMG data composition, sEMG data preprocessing and normalization, transformation of 1D sEMG signals into spectrograms, and final classification. Based on experiments, the user identification accuracy obtained using the 1D sEMG signals was 59.3% before performing preprocessing and 66.7% after performing preprocessing, indicating only a slight performance increase. Furthermore, the user identification performance of 40 subjects using the proposed method was 78.4% before preprocessing and 85.4% after preprocessing when the sEMG signals were transformed into spectrograms by applying a window length of 256 in 12 channels; these results indicated 19.4% and 18.7% accuracy improvements, respectively, compared with the case of using 1D sEMG signals. When the insufficient amount of data was increased by 10 times by applying matching pursuit, the user identification performance was 96.1%, a 10% increase compared with before data augmentation. By conducting user identification using DenseNet201 and MobileNet-v2 networks in addition to the CNN employed in this study, it was demonstrated that the user identification performance improved when applying the method proposed. Accordingly, the possibility of performing user identification was verified based on the use of multidimensional feature sEMG spectrograms transformed through the STFT after the removal of noise and unnecessary resting state signals in the 1D sEMG signals, as well as training data augmentation. In the future, we plan to acquire sEMG signals directly from the wearable device environment, build a database, and conduct sEMG signal-based user identification research that can be applied in real life. **Author Contributions:** Conceptualization, J.-M.K. and S.-B.P. ; methodology, J.-M.K. and S.-B.P. ; software, J.-M.K. ; validation, J.-M.K., M.-G.K., and S.-B.P. ; formal analysis, J.-M.K. ; investigation, J.-M.K. ; writing--original draft preparation, J.-M.K.