Targeted Muscle Training with a Hybrid Body-Machine Interface

Dalia De Santis*, Ferdinando A. Mussa-Ivaldi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

Abstract

Studies have shown that motor recovery after neurological injuries is dependent on functional reorganization. In particular, engaging muscles in skilled activities triggers a process of remodeling that could lead to improving functional outcomes. Here, we propose a novel approach for engaging targeted muscles into skilled activities while operating assistive interfaces based on wearable sensors. To enforce contribution of specific muscles to the control output of a movement-based assistive interface, we introduced a signal dependent on muscle activation as replacement of a highly correlated signal dependent on limb kinematics, as measured by a set of inertial sensors. The latter were weighted against the EMG contribution and sent as input to a linear map projecting kinematic signals onto a 2D screen. Modulation of the weighting factor allows switching from a kinematic only (assistive) to a hybrid (rehabilitative) mode by increasing or decreasing EMG contribution to the operation of the interface.

Original languageEnglish (US)
Title of host publicationBiosystems and Biorobotics
PublisherSpringer Science and Business Media Deutschland GmbH
Pages457-461
Number of pages5
DOIs
StatePublished - 2022

Publication series

NameBiosystems and Biorobotics
Volume28
ISSN (Print)2195-3562
ISSN (Electronic)2195-3570

ASJC Scopus subject areas

  • Biomedical Engineering
  • Mechanical Engineering
  • Artificial Intelligence

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