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Related Experiment Video

Updated: Jan 17, 2026

Therapy Interventions for Upper Limb Amputees Undergoing Selective Nerve Transfers
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Advances in HD-EMG interfaces and spatial algorithms for upper limb prosthetic control.

Debora Quadrelli1,2, Michele Canepa1,3, Dario Di Domenico1,4

  • 1Rehab Technologies Lab, Italian Institute of Technology, Genoa, Italy.

Frontiers in Neuroscience
|September 22, 2025
PubMed
Summary
This summary is machine-generated.

High-density electromyography (HD-EMG) and machine learning (ML) enhance prosthetic control for upper limb amputees. These technologies improve device performance and user integration, aiming to reduce prosthetic abandonment.

Keywords:
EMG recording Interfacesdeep learninghigh-density EMGmachine learningmyoelectric controlspatial information

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Neuroprosthetics

Background:

  • Upper limb amputation severely impacts daily life and prosthesis use.
  • Current myoelectric prostheses face challenges in control and user acceptance, leading to high abandonment rates.
  • Advances in sensing and AI offer potential solutions for improved prosthetic functionality.

Purpose of the Study:

  • To review recent advancements in high-density electromyography (HD-EMG) acquisition systems and machine learning (ML) algorithms for prosthetic control.
  • To explore the integration of HD-EMG and ML in prosthetic systems.
  • To identify challenges and opportunities for translating research into clinical applications.

Main Methods:

  • Review of current literature on HD-EMG interfaces, recording technologies, and ML algorithms.
  • Analysis of spatial information processing in ML for prosthetic control.
  • Discussion of technological integration and clinical translation barriers.

Main Results:

  • HD-EMG provides richer muscle activation data compared to traditional methods.
  • ML algorithms effectively utilize spatial information from HD-EMG for improved intention detection and motion control.
  • Significant progress has been made in both HD-EMG hardware and ML software for prosthetics.

Conclusions:

  • Combining HD-EMG and ML shows great promise for enhancing prosthetic dexterity and user experience.
  • Addressing challenges in system integration and clinical validation is crucial for widespread adoption.
  • Further research can lead to more intuitive and functional upper limb prostheses, reducing abandonment rates.