Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/120400
Título: Easing automatic neurorehabilitation via classification and smoothness analysis
Autores/as: Bensalah, Asma
Fornés, Alicia
Carmona-Duarte, Cristina 
Lladós, Josep
Clasificación UNESCO: 1203 Ciencia de los ordenadores
120304 Inteligencia artificial
Palabras clave: Deep Learning
Jerk
Movement Classification
Movement Smoothness
Neurorehabilitation, et al.
Fecha de publicación: 2022
Editor/a: Springer 
Publicación seriada: Lecture Notes in Computer Science 
Conferencia: IGS2021: The 20th Conference of the International Graphonomics Society
Resumen: Assessing the quality of movements for post-stroke patients during the rehabilitation phase is vital given that there is no standard stroke rehabilitation plan for all the patients. In fact, it depends basically on the patient’s functional independence and its progress along the rehabilitation sessions. To tackle this challenge and make neurorehabilitation more agile, we propose an automatic assessment pipeline that starts by recognising patients’ movements by means of a shallow deep learning architecture, then measuring the movement quality using jerk measure and related measures. A particularity of this work is that the dataset used is clinically relevant, since it represents movements inspired from Fugl-Meyer a well common upper-limb clinical stroke assessment scale for stroke patients. We show that it is possible to detect the contrast between healthy and patients movements in terms of smoothness, besides achieving conclusions about the patients’ progress during the rehabilitation sessions that correspond to the clinicians’ findings about each case.
URI: http://hdl.handle.net/10553/120400
ISBN: 978-3-031-19744-4
ISSN: 0302-9743
DOI: 10.1007/978-3-031-19745-1_25
Fuente: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [ISSN 0302-9743], v. 13424 LNCS, p. 336-348, (Enero 2022)
Colección:Actas de congresos
Vista completa

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.