sEMG Gesture Recognition with a Simple Model of Attention

Myoelectric control is one of the leading brain-machine-interfaces in the field of robotic prosthetics. We present our research in real-time surface electromyography (sEMG) signal classification, where our simple and novel attention-based approach now leads the industry, universally beating more complex, state-of-the-art models. Our model achieved an accuracy of 87\% (class-balanced accuracy: 69\%) using sEMG data and 91\% (balanced accuracy: 74\%) using both sEMG and accelerometer (IMU) data on NinaPro DB5, as well as 73\% overall on NinaPro DB4, an improvement on both highly sophisticated deep learning and signal processing approaches. Notably, the representation of the data learned by the attention mechanism alone is powerful enough to yield an accuracy of 79\% on DB5. NinaPro DB5 is a standard benchmark for sEMG gesture recognition and consists of 53 unique gestures, including finger gestures, wrist gestures, and functional grasping gestures. Our proposed methodology's model simplicity represents a compelling alternative to the convolutional neural network (CNN) approaches utilized in recent research.