Intramuscular EMG-Driven Musculoskeletal Modelling: Towards Implanted Muscle Interfacing in Spinal Cord Injury Patients

Moon Ki Jung, Silvia Muceli, Camila Rodrigues, Alvaro Megia-Garcia, Alejandro Pascual-Valdunciel, Antonio J. Del-Ama, Angel Gil-Agudo, Juan C. Moreno, Filipe Oliveira Barroso, Jose L. Pons, Dario Farina*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Objective: Surface EMG-driven modelling has been proposed as a means to control assistive devices by estimating joint torques. Implanted EMG sensors have several advantages over wearable sensors but provide a more localized information on muscle activity, which may impact torque estimates. Here, we tested and compared the use of surface and intramuscular EMG measurements for the estimation of required assistive joint torques using EMG driven modelling. Methods: Four healthy subjects and three incomplete spinal cord injury (SCI) patients performed walking trials at varying speeds. Motion capture marker trajectories, surface and intramuscular EMG, and ground reaction forces were measured concurrently. Subject-specific musculoskeletal models were developed for all subjects, and inverse dynamics analysis was performed for all individual trials. EMG-driven modelling based joint torque estimates were obtained from surface and intramuscular EMG. Results: The correlation between the experimental and predicted joint torques was similar when using intramuscular or surface EMG as input to the EMG-driven modelling estimator in both healthy individuals and patients. Conclusion: We have provided the first comparison of non-invasive and implanted EMG sensors as input signals for torque estimates in healthy individuals and SCI patients. Significance: Implanted EMG sensors have the potential to be used as a reliable input for assistive exoskeleton joint torque actuation.

Original languageEnglish (US)
Pages (from-to)63-74
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Volume69
Issue number1
DOIs
StatePublished - Jan 1 2022

Funding

Keywords

  • EMG driven modelling
  • assistive technology
  • electromyography
  • human-machine interface
  • musculoskeletal model
  • spinal cord injury

ASJC Scopus subject areas

  • Biomedical Engineering

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