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MINI REVIEW article

Front. Neurosci.

Sec. Neuroprosthetics

Volume 19 - 2025 | doi: 10.3389/fnins.2025.1655257

This article is part of the Research TopicNeuroengineering for health and disease: a multi-scale approachView all 8 articles

Advances in HD-EMG Interfaces and Spatial Algorithms for Upper Limb Prosthetic Control

Provisionally accepted
Debora QuadrelliDebora Quadrelli1,2*Michele CanepaMichele Canepa1,3Dario Di DomenicoDario Di Domenico1,4Nicolò BoccardoNicolò Boccardo1,3Michela ChiappaloneMichela Chiappalone1,2Matteo LaffranchiMatteo Laffranchi1
  • 1Italian Institute of Technology (IIT), Genova, Italy
  • 2Universita degli Studi di Genova Dipartimento di Informatica Bioingegneria Robotica e Ingegneria dei Sistemi, Genoa, Italy
  • 3Open University Affiliated Research Centre at Italian Institute of Technology (ARC@IIT), Genova, Italy
  • 4Politecnico di Torino Dipartimento di Elettronica e Telecomunicazioni, Turin, Italy

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Upper limb amputation significantly affects daily functioning and quality of life. Although myoelectric prostheses offer a promising avenue for restoring motor capabilities, high rates of device abandonment underscore challenges in control performance and user integration. Recent advances in high-density electromyography (HD-EMG) and machine learning (ML) algorithms have shown potential to enhance prosthetic dexterity. HD-EMG interfaces capture richer spatial and temporal muscle activation data, while ML algorithms exploit this information to improve intention detection and motion control. This mini-review explores advancements in HD-EMG acquisition systems, including both interface designs and recording technologies, as well as ML algorithms leveraging spatial information. In addition to summarizing the current state of the art, we discuss the challenges and the opportunities of embedding these technologies in prosthetic systems, with the objective of facilitating the translation of laboratory research into clinical applications.

Keywords: high-density EMG, EMG Recording Interfaces, myoelectric control, spatial information, machine learning, deep learning

Received: 27 Jun 2025; Accepted: 18 Aug 2025.

Copyright: © 2025 Quadrelli, Canepa, Di Domenico, Boccardo, Chiappalone and Laffranchi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Debora Quadrelli, Italian Institute of Technology (IIT), Genova, Italy

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Supplementary Material