Título:	Investigating upper limb movement classification on users with tetraplegia as a possible neuroprosthesis interface
Autor(es):	Fonseca, Lucas Bó, Antônio Padilha Lanari Guiraud, David Navarro, Benjamin Gélis, Anthony Coste, Christine Azevedo
Assunto:	Medula espinhal - ferimentos e lesões Acidentes vasculares cerebrais Lesão cerebral Medicina de reabilitação
Data de publicação:	Jul-2018
Editora:	IEEE
Referência:	FONSECA, Lucas et al. Investigating upper limb movement classification on users with tetraplegia as a possible neuroprosthesis interface. In: ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, 40., 2018, Honolulu.
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.
Unidade Acadêmica:	Instituto de Ciências Biológicas (IB) Departamento de Ecologia (IB ECL)
Licença:	Autorização concedida ao Repositório Institucional da Universidade de Brasília pelo Professor Antônio Padilha Lanari Bó para disponibilizar o trabalho, em 23 de abril de 2019, no site repositorio.unb.br, de acordo com a licença conforme permissões assinaladas, para fins de leitura, impressão e/ou download, a título de divulgação da obra, a partir desta data.
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