Sensing and decoding the neural drive to paralyzed muscles during attempted movements of a person with tetraplegia using a sleeve array
Abstract
A wearable electrode array and machine learning methods were used to record and decode myoelectric signals and motor unit firing in paralyzed muscles of a person with motor complete tetraplegia. The myoelectric activity and motor unit firing rates were task specific, even in the absence of visible motion, enabling accurate classification of attempted single-digit movements. This wearable system has the potential to enable people with tetraplegia to control assistive devices through movement intent.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 01, 2021
- Source ID
- 10.1152/jn.00220.2021
Entities
People
- Alessandro Del Vecchio
- Dario Farina
- Devapratim Sarma
- Douglas J Weber
- Jennifer L. Collinger
- Jordyn E. Ting
- Nicholas V. Annetta
- Nikhil Verma
- Samuel C. Colachis
Organizations
- Battelle Memorial Institute
- Carnegie Mellon University
- European Research Council
- Friedrich-Alexander-Universität Erlangen-Nürnberg
- Imperial College London
- National Institute of Neurological Disorders and Stroke
- National Science Foundation
- University of Pittsburgh