使用 Lipschitz-Regularized 神经网络进行基于肌电图的自动手势识别

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Ana NEACȘU, Jean-Christophe Pesquet, Corneliu Burileanu
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引用次数: 0

摘要

本文介绍了一种基于前臂水平采集的表面肌电图(sEMG)信号构建稳健的自动手势识别系统的新方法。我们的主要贡献在于提出了新的约束学习策略,通过控制分类器的 Lipschitz 常量来确保鲁棒性,从而抵御对抗性扰动。我们将重点放在非负神经网络上,可以为其推导出精确的 Lipschitz 定界,我们还提出了不同的谱规范约束,从理论上保证了鲁棒性。在四个公开数据集上的实验结果表明,我们在准确性和性能方面实现了很好的权衡。然后,我们展示了我们的模型与标准训练分类器在四种情况下的鲁棒性,同时考虑了白盒攻击和黑盒攻击。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.30
自引率
2.00%
发文量
131
期刊介绍: ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.
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