EMG-Based Automatic Gesture Recognition Using Lipschitz-Regularized Neural Networks

This paper introduces a novel approach for building a robust Automatic Gesture Recognition system based on Surface Electromyographic (sEMG) signals, acquired at the forearm level. Our main contribution is to propose new constrained learning strategies that ensure robustness against adversarial perturbations by controlling the Lipschitz constant of the classifier. We focus on nonnegative neural networks for which accurate Lipschitz bounds can be derived, and we propose different spectral norm constraints offering robustness guarantees from a theoretical viewpoint. Experimental results on four publicly available datasets highlight that a good trade-off in terms of accuracy and performance is achieved. We then demonstrate the robustness of our models, compared to standard trained classifiers in four scenarios, considering both white-box and black-box attacks.