Investigating Upper Limb Movement Classification on Users with Tetraplegia as a Possible Neuroprosthesis Interface

Lucas Fonseca 1, 2 Antonio Padilha Lanari Bo 1 David Guiraud 2 Benjamin Navarro 3 Anthony Gélis 4 Christine Azevedo Coste 2
2 CAMIN - Control of Artificial Movement and Intuitive Neuroprosthesis
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
3 IDH - Interactive Digital Humans
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Spinal cord injury (SCI), stroke and other nervous system conditions can result in partial or total paralysis of individual's limbs. Numerous technologies have been proposed to assist neurorehabilitation or movement restoration, e.g. robotics or neuroprosthesis. However, individuals with tetraplegia often find difficult to pilot these devices. We developed a system based on a single inertial measurement unit located on the upper limb that is able to classify performed movements using principal component analysis. We analyzed three calibration algorithms: unsupervised learning, supervised learning and adaptive learning. Eight participants with tetraplegia (C4-C7) piloted three different postures in a robotic hand. We achieved 89% accuracy using the supervised learning algorithm. Through offline simulation, we found accuracies of 76% on the unsupervised learning, and 88% on the adaptive one.
Keywords : SCI Tetraplegia
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01900330
Contributor : Christine Azevedo Coste <>
Submitted on : Wednesday, October 24, 2018 - 11:13:30 AM
Last modification on : Wednesday, October 24, 2018 - 11:15:03 AM
Long-term archiving on : Friday, January 25, 2019 - 2:08:28 PM

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Lucas Fonseca, Antonio Padilha Lanari Bo, David Guiraud, Benjamin Navarro, Anthony Gélis, et al.. Investigating Upper Limb Movement Classification on Users with Tetraplegia as a Possible Neuroprosthesis Interface. EMBS: Engineering in Medicine and Biology Society, Jul 2018, Honolulu, United States. ⟨lirmm-01900330⟩

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