Work

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

Year: 2023

Type: article

Abstract: This article 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 tradeoff in terms of accuracy and performance is achieved. We then demonstrate the robustness of our models, compared with standard trained classifiers in four scenarios, considering both white-box and black-box attacks.

Source: ACM Transactions on Intelligent Systems and Technology

DOI: 10.1145/3635159

Authors: Ana Neacşu, Jean-Christophe Pesquet, Corneliu Burileanu

Institutions: Institut national de recherche en sciences et technologies du numérique, CentraleSupélec, Universitatea Națională de Știință și Tehnologie Politehnica București, Centre de Vision Numérique

Language: English

Topic: Adversarial Robustness in Machine Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Authority for Scientific Research and Innovation, Agence Nationale de la Recherche, Autoritatea Natională pentru Cercetare Stiintifică, Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii

Awards: G4823047899, BRIDinG thE gAp Between iterative proximaL methods and nEural networks, G6703262589