Obálka

Investigating upper limb movement classification on users with tetraplegia as a possible neuroprosthesis interface

Uloženo v:
Podrobná bibliografie
Hlavní autor: Fonseca, Lucas
Datum vydání: 2018
Další autoři: Bó, Antônio Padilha Lanari, Guiraud, David, Navarro, Benjamin, Gélis, Anthony, Coste, Christine Azevedo
Jazyk: eng
Zdroj: Repositório Institucional da UnB
Download full: http://repositorio.unb.br/handle/10482/34442
Shrnutí: 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.

Podobné jednotky: Investigating upper limb movement classification on users with tetraplegia as a possible neuroprosthesis interface

Podobné jednotky