X-MyoNET: Biometric Identification Using Deep Processing of Dynamic Surface Electromyography.

This paper investigates the potential of surface electromyography (sEMG) as a new biometric modality and proposes a deep neural network architecture as the backbone of a gesture-independent personal identification system. The paper focuses on the real-world translation of such a model through systematic optimization, which finds the minimum number of gestures and sensors needed for training. Focusing on “dynamic sEMG”, our proposed method can successfully identify 40 subjects with an average accuracy of 97%. This is achieved when gestures are the same in training, validation, and testing (the subjects need to repeat a particular gesture among a set of seven known gestures as a passcode). In a more complex scenario, when training gestures differ from those in validation and testing, our model can achieve an average accuracy of 90%, demonstrating that the proposed model can extract the unique patterns to identify a user regardless of gestures. Taking advantage of Gradient-weighted Class Activation Mapping (Grad-CAM), we explore the attention of the model on segments of the spectrotemporal space of the input signals. Grad-CAM not only sheds light on sEMG-based personal identification by decoding and visualizing the unique user-specific neurophysiological pattern but also generates a two-dimensional spectrotemporal mask used to reduce the model complexity significantly. As a result of the systematic optimization and Grad-CAM analysis, our proposed identification method needs only 4% of data for training, boosting practicality. This paper also reveals the robustness of the proposed model for cross-day evaluation. Finally, the comparative study shows the superiority of our proposed model over several state-of-the-art algorithms.