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EMG-Based Automatic Gesture Recognition Using Lipschitz-Regularized Neural Networks
Open accessAbstract
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.
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“| Zero Crossing Rate (ZCR) | Measure signal waveform zero-crossing | Rapid computation of muscle | Time-domain feature extraction for sEMG-based |”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [135]
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References 28 · Cited by 15
Research works printed but not linked: 31
- Cristina Andronache, Marian Negru, Ioana Bădiţoiu, George Cioroiu, Ana Neacsu, and Corneliu Burileanu. 2022. Automatic gesture recognition framework based on forearm EMG activity. In Proc. IEEE Int. Conf. Telecomm. Signal Porcess.284–288.
- Cristina Andronache, Marian Negru, Ana Neacşu, George Cioroiu, Anamaria Rădoi, and Corneliu Burileanu. 2020. Towards extending real-time EMG-based gesture recognition system. In Proc. IEEE Int. Conf. Telecomm. Signal Porcess.Milan, 301–304.
- Cem Anil, James Lucas, and Roger Grosse. 2019. Sorting out Lipschitz function approximation. In Proc. Int. Conf. Mach. Learn.Long Beach, California, 291–301.
- Nicholas Carlini, Pratyush Mishra, Tavish Vaidya, Yuankai Zhang, Micah Sherr, Clay Shields, David Wagner, and Wenchao Zhou. 2016. Hidden voice commands. In USENIX Security Symp.Austin, TX, 513–530.
- Nicholas Carlini and David Wagner. 2017. Towards evaluating the robustness of neural networks. In IEEE Symp. Security Privacy. San Jose, CA, 39–57.
- Tong Chen Jean-Bernard Lasserre Victor Magron and Edouard Pauwels. 2020. Semialgebraic optimization for Lipschitz constants of ReLU networks. In Proc. Int. Conf. Neural Info. Process. Syst. Curran Associates Inc. 12.
- Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier. 2017. Parseval networks: Improving robustness to adversarial examples. In Proc. Int. Conf. Mach. Learn.Sydney, NSW, 854–863.
- Mahyar Fazlyab, Alexander Robey, Hamed Hassani, Manfred Morari, and George Pappas. 2019. Efficient and accurate estimation of lipschitz constants for deep neural networks. In Adv. Neural Info. Process. Syst.Vancouver, Canada, 11423–11434.
- Xiaojie Gao, Yueming Jin, Qi Dou, and Pheng-Ann Heng. 2020. Automatic gesture recognition in robot-assisted surgery with reinforcement learning and tree search. In Proc. IEEE Int. Conf. Robot. Autom.Paris, France, 8440–8446.
- Jonas Geiping, Liam H. Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, and Tom Goldstein. 2020. Witches’ Brew: Industrial scale data poisoning via gradient matching. In Int. Conf. Learn. Represent.
- Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015. Explaining and harnessing adversarial examples. In Int. Conf. Learn. Represent.San Diego, CA.
- Kavya Gupta, Beatrice Pesquet-Popescu, Fateh Kaakai, and Jean-Christophe Pesquet. 2021. A quantitative analysis of the robustness of neural networks for tabular data. In Proc. IEEE Int. Conf. Acoust. Speech Signal Process.8057–8061.
- Rami N. Khushaba and Sarath Kodagoda. 2012. Electromyogram (EMG) feature reduction using mutual components analysis for multifunction prosthetic fingers control. In Proc. Int. Conf. Control Autom. Robotics & Vision. Guangzhou, China, 1534–1539.
- Jonghwa Kim, Stephan Mastnik, and Elisabeth André. 2008. EMG-based hand gesture recognition for realtime biosignal interfacing. In Proc. Int. Conf. Intell. User Interfac.Canaria, Spain, 30–39.
- Okan Kopuklu, Neslihan Kose, and Gerhard Rigoll. 2018. Motion fused frames: Data level fusion strategy for hand gesture recognition. In Proc. IEEE Conf. Comput. Vis. Pattern Recogn.Utah, 2103–2111.
- Alexey Kurakin, Ian Goodfellow, and Samy Bengio. 2017. Adversarial machine learning at scale. In Int. Conf. on Learn. Represent.Toulon, France.
- Alexey Kurakin, Zhengyou Zhang, and Zicheng Liu. 2012. A real time system for dynamic hand gesture recognition with a depth sensor. In Proc. IEEE European Signal Processing Conf.Bucharest, Romania, 1975–1979.
- Fabian Latorre, Paul Rolland, and Volkan Cevher. 2020. Lipschitz constant estimation of neural networks via sparse polynomial optimization. In Int. Conf. on Learning Representations.
- Peiliang Li, Xiaozhi Chen, and Shaojie Shen. 2019. Stereo R-CNN based 3D object detection for autonomous driving. In Proc. IEEE Conf. Comput. Vis. Pattern Recogn.Long Beach, CA, 7644–7652.
- Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018. Towards deep learning models resistant to adversarial attacks. In Proc. Int. Conf. Learn. Represent.Vancouver, BC, Canada.
- Ana Neacşu, Jean-Christophe Pesquet, and Corneliu Burileanu. 2020. Accuracy-robustness trade-off for positively weighted neural networks. In Proc. IEEE Int. Conf. Acoust. Speech Signal Process.Barcelona, Spain, 8389–8393.
- Ana Antonia Neacsu, George Cioroiu, Anamaria Rădoi, and Corneliu Burileanu. 2019. Automatic EMG-based hand gesture recognition system using time-domain descriptors and fully-connected neural networks. In Proc. Int. Conf. Telecommunications Signal Process.Budapest, Hungary, 232–235.
- Alvaro David Orjuela-Cañón, Andrés F. Ruíz-Olaya, and Leonardo Forero. 2017. Deep neural network for EMG signal classification of wrist position: Preliminary results. In IEEE Latin American Conf. Comput. Intell.Arequipa, Peru, 1–5.
- Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Ananthram Swami. 2016. The limitations of deep learning in adversarial settings. In IEEE Symp. Security Privacy. Saarbrücken, Germany.
- Prajit Ramachandran, Barret Zoph, and Quoc V Le. 2018. Searching for activation functions. Proc. Int. Conf. Learn. Represent. (30 Apr.–03 May2018), 1–13.
- Kevin Scaman and Aladin Virmaux. 2018. Lipschitz regularity of deep neural networks: Analysis and efficient estimation. In Proc. Ann. Conf. Neur. Inform. Proc. Syst.Montreal, Canada, 3839–3848.
- Mathieu Serrurier, Franck Mamalet, Alberto González-Sanz, Thibaut Boissin, Jean-Michel Loubes, and Eustasio del Barrio. 2021. Achieving robustness in classification using optimal transport with hinge regularization. In Proc. IEEE Conf. Comput. Vis. Pattern Recogn.Nashville, 505–514.
- Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2014. Intriguing properties of neural networks. In Int. Conf. Learn. Represent.Banff, Canada.
- Miyato Takeru, Kataoka Toshiki, Koyama Masanori, and Yoshida Yuichi. 2018. Spectral normalization for generative adversarial networks. In Int. Conf. Learn. Represent.Vancouver, Canada.
- Dimitris Tsipras Shibani Santurkar Logan Engstrom Alexander Turner and Aleksander Madry. 2019. Robustness may be at odds with accuracy. Int. Conf. Learn. Represent. (2019) 1–20.
- Nelly Indriani Widiastuti. 2019. Convolution neural network for text mining and natural language processing. In IOP Conf. Mater. Sci. Eng., Vol. 662. Kazimierz Dolny, Poland.
Metadata
- Authors
- Neacşu, A., Pesquet, J.-C., & Burileanu, C.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- Universitatea Națională de Știință și Tehnologie Politehnica București (RO)
- Venue
- ACM Transactions on Intelligent Systems and Technology
- Published
- 2024-02-22
- Volume
- 15
- Issue
- 2
- Pages
- 1-25
- ISSN
- 2157-6904 (ISSN-L)
- 2157-6912
- OpenAlex
- W4389487309
- Cited by
- 15
- References
- 28
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 5
- FWCI
- 1.33
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Transactions on Intelligent Systems and Technology (publishedVersion, cc-by)
- HAL (submittedVersion, other-oa)
- Funders
- BRIDGEABLE ANR Research and Teaching Chair in AI
- Romanian National Authority for Scientific Research and Innovation, UEFISCDI (425PED/2020, and PN-III-P2-2.1-PED-2019-2392)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Keywords
- surface electromyography, Lipschitz regularization, adversarial robustness, black-box attacks, white-box attacks
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- Neacşu, A., Pesquet, J.-C., & Burileanu, C. (2024). EMG-Based Automatic Gesture Recognition Using Lipschitz-Regularized Neural Networks. ACM Transactions on Intelligent Systems and Technology. https://doi.org/10.1145/3635159
