A High Accuracy and Real-Time sEMG-Based Hand Gesture Classifier Using LDA-Based Template Matching With Adaptive Majority Vote and Online Data Augmentation

Surface electromyography (sEMG)-based gesture classification is promising in many emerging applications such as prosthetic control and augmented reality (AR)/virtual reality (VR). While many existing sEMG-based hand gesture classifiers achieve high intrasession accuracy, they show very low intersession and intersubject accuracy which are more important in practical use. Another issue of the existing works is the usage of deep neural networks increases the accuracy but also brings up the complexity, making real-time classification difficult especially on embedded systems. In this work, an sEMG-based hand gesture classifier with high intersession, intersubject accuracy, and real-time performance is proposed. A linear discriminant analysis (LDA)-based template matching method is proposed to improve the intersession and intersubject accuracy while maintaining low complexity. An adaptive window-size majority vote method and a calibration-oriented online data augmentation method are proposed to further improve the accuracy. The proposed classifier has been implemented on both GPU and FPGA-based embedded system demonstrating real-time performance. Compared to the state-of-the-art works, the proposed classifier achieves higher intersession and intersubject accuracy of 78.0% and 78.9% on Ninapro DB6 (95.2% and 96.6% on CapgMyo DB-b) with shorter classification time of 6.5 ms on Ninapro DB6 (4.9 ms on CapgMyo DB-b).