PT Unknown
AU Asma Bensalah
   Alicia Fornes
   Cristina Carmona_Duarte
   Josep Llados
TI Easing Automatic Neurorehabilitation via Classification and Smoothness Analysis
BT Intertwining Graphonomics with Human Movements. 20th International Conference of the International Graphonomics Society, IGS 2022
PY 2022
BP 336
EP 348
VL 13424
DI 10.1007/978-3-031-19745-1_25
DE Neurorehabilitation; Upper-lim; Movement classification; Movement smoothness; Deep learning; Jerk
AB 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.
ER