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sEMG-Based Gesture Recognition with Spiking Neural Networks on Low-Power FPGA

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Design and Architectures for Signal and Image Processing (DASIP 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14622))

Abstract

Classification of surface electromyographic (sEMG) signals for the precise identification of hand gestures is a crucial area in the advancement of complex prosthetic devices and human-machine interfaces. This study presents a real-time sEMG classification system, exploiting a Spiking Neural Network (SNN) to distinguish among twelve distinct hand gestures. The system is implemented on a Lattice iCE40-UltraPlus FPGA, explicitly designed for low-power applications. Evaluation on the NinaPro DB5 dataset confirms an accuracy of 85.6%, demonstrating the model’s effectiveness. The power consumption for this architecture is approximately 1.7 mW, leveraging the inherent energy efficiency of SNNs for low-power classification.

This work was supported by Key Digital Technologies Joint Undertaking (KDT JU) in “EdgeAI Edge AI Technologies for Optimised Performance Embedded Processing” project, grant agreement No 101097300.

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Authors and Affiliations

  1. University of Cagliari, Cagliari, Italy

    Matteo Antonio Scrugli, Gianluca Leone, Paola Busia & Paolo Meloni

Authors
  1. Matteo Antonio Scrugli
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  2. Gianluca Leone
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  3. Paola Busia
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  4. Paolo Meloni
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Corresponding author

Correspondence to Matteo Antonio Scrugli .

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Editors and Affiliations

  1. Instituto Superior de Engenharia de Lisboa, Lisbon, Portugal

    Tiago Dias

  2. Università degli Studi di Cagliari, Cagliari, Italy

    Paola Busia

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Scrugli, M.A., Leone, G., Busia, P., Meloni, P. (2024). sEMG-Based Gesture Recognition with Spiking Neural Networks on Low-Power FPGA. In: Dias, T., Busia, P. (eds) Design and Architectures for Signal and Image Processing. DASIP 2024. Lecture Notes in Computer Science, vol 14622. Springer, Cham. https://doi.org/10.1007/978-3-031-62874-0_2

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  • DOI: https://doi.org/10.1007/978-3-031-62874-0_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-62873-3

  • Online ISBN: 978-3-031-62874-0

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