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Accurate identification of forearm muscle activity is an open question in the field of assistive and prosthetic technology. Neurorehabilitation therapies could benefit significantly from improved intention detection algorithms. Motor control models and high-density electrodes can be combined to enhance the effectiveness of such algorithms. In this paper, data from a linear, high-density electrode grid was analyzed through muscle synergy analysis, using an optimized criterion to determine the number of synergies. The results demonstrate that reducing electrode density also reduces the number of identified synergies, supporting the hypothesis of dense electrode grids are essential for comprehensive mapping of forearm muscle activity is needed.

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Optimal Extraction of Synergy-Like Structures from HD-EMG Signals of the Forearm

  • 会议论文

  • 发表日期：

    2025年

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  • S. Ranaldi,
  • F. Forconi,
  • G. Corvini,
  • I. De Meis,
  • M. Schmid,
  • S. Conforto

摘要

Accurate identification of forearm muscle activity is an open question in the field of assistive and prosthetic technology. Neurorehabilitation therapies could benefit significantly from improved intention detection algorithms. Motor control models and high-density electrodes can be combined to enhance the effectiveness of such algorithms. In this paper, data from a linear, high-density electrode grid was analyzed through muscle synergy analysis, using an optimized criterion to determine the number of synergies. The results demonstrate that reducing electrode density also reduces the number of identified synergies, supporting the hypothesis of dense electrode grids are essential for comprehensive mapping of forearm muscle activity is needed.