###### Abstract  
In the task of upper-limb pattern recognition, effective feature extraction, channel selection, and classification methods are crucial for the construction of an efficient surface electromyography (sEMG) signal classification framework. However, existing deep learning models often face limitations due to improper channel selection methods and overly specific designs, leading to high computational complexity and limited scalability. To address this challenge, this study introduces a deep learning network based on channel feature compression--partial channel selection sEMG net (PCS-EMGNet). This network combines channel feature compression (channel selection) and feature extraction (partial block), aiming to reduce the model's parameter count while maintaining recognition accuracy. PCS-EMGNet extracts high-dimensional feature vectors from sEMG signals through the partial block, decoding spatial and temporal feature information. Subsequently, channel selection compresses and filters these high-dimensional feature vectors, accurately selecting channel features to reduce the model's parameter count, thereby decreasing computational complexity and enhancing the model's processing speed. Moreover, the proposed method ensures the stability of classification, further improving the model's capability of recognizing features in sEMG signal data. Experimental validation was conducted on five benchmark databases, namely the NinaPro DB4, NinaPro DB5, BioPatRec DB1, BioPatRec DB2, and BioPatRec DB3 datasets. Compared to traditional gesture recognition methods, PCS-EMGNet significantly enhanced recognition accuracy and computational efficiency, broadening its application prospects in real-world settings. The experimental results showed that our model achieved the highest average accuracy of 88.34% across these databases, marking a 9.96% increase in average accuracy compared to models with similar parameter counts. Simultaneously, our model's parameter size was reduced by an average of 80% compared to previous gesture recognition models, demonstrating the effectiveness of channel feature compression in maintaining recognition accuracy while significantly reducing the parameter count. sEMG; hand gestures classification; channel feature compression; channel feature selection +
Footnote †: journal: Applied  
0000-0001-9993-3227][PERSON] 0000-0001-9993-3276][PERSON] 0000-0002-0002-0002-3338][PERSON] 0000-0002-0002-3338-0000][PERSON] 0000-0002-0002-3338-0000][PERSON] 0000-0002-3338-0000][PERSON] 0000-0002-  
advantages, including non-invasiveness, ease of acquisition, and suitability for dynamic gesture recognition. Consequently, sEMG has garnered significant attention from scholars and is widely applied in contexts where high-precision gesture recognition is demanded. Gesture recognition based on surface electromyography features high accuracy, ease of wear, and non-invasiveness, making it a focal point of research in the field. Traditional gesture recognition frameworks based on sEMG consist of data preprocessing, feature extraction, feature selection, and gesture classification. Among these stages, feature extraction and gesture classification are two critical phases within the framework of sEMG-based gesture recognition. Common feature extraction methods encompass time-domain features [7], frequency-domain features [8], and time-frequency-domain features [9]. For instance, time-domain features often include metrics such as Mean Absolute Value (MAV), Root Mean Square (RMS), Mean Absolute Value Slope (MAV Slope), Waveform Length (WL), Slope Sign Changes (SSCs), Zero Crossing (ZC), and EMG Histogram (HIST), with EMG histogram being an extension of zero crossing. Following the feature extraction methods, traditional classification techniques are employed for gesture classification. Different classifiers have been introduced, such as k-Nearest Neighbors (KNN) [10], random forest [11], Linear Discriminant Analysis (LDA) [12], Support Vector Machine (SVM) [13], and Hidden Markov Models (HMMs) [14].