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Upper limb amputation significantly challenges independence, as the human hand is essential for sensing and interacting with the environment. As a consequence, researchers developed complex prosthetic devices, equipped with multiple degrees of freedom, capable of restoring lost functionalities. To enhance prostheses control, current focus is on high-density electromyography (HD-EMG) interfaces, which are susceptible to electrode shift, thus posing reliability issues. To overcome this problem, this study proposes an algorithm to detect and measure the electrode displacement in both lateral and longitudinal directions within an HD-EMG setting. The proposed algorithm achieved a mean accuracy of 79.06% in predicting electrodes shifts, showing potential for real-world application to empower data-driven models over multi-day prostheses usage.

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Algorithm for Detecting Shifts in High-Density EMG Sensors

  • 会议论文

  • 发表日期：

    2025年

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  • Debora Quadrelli,
  • Dario Di Domenico,
  • Michele Canepa,
  • Andrea Marinelli,
  • Marta Gandolla,
  • Carlo Albino Frigo,
  • Michela Chiappalone,
  • Nicolò Boccardo,
  • Matteo Laffranchi

摘要

Upper limb amputation significantly challenges independence, as the human hand is essential for sensing and interacting with the environment. As a consequence, researchers developed complex prosthetic devices, equipped with multiple degrees of freedom, capable of restoring lost functionalities. To enhance prostheses control, current focus is on high-density electromyography (HD-EMG) interfaces, which are susceptible to electrode shift, thus posing reliability issues. To overcome this problem, this study proposes an algorithm to detect and measure the electrode displacement in both lateral and longitudinal directions within an HD-EMG setting. The proposed algorithm achieved a mean accuracy of 79.06% in predicting electrodes shifts, showing potential for real-world application to empower data-driven models over multi-day prostheses usage.