PT Unknown
AU Asma Bensalah
   Jialuo Chen
   Alicia Fornes
   Cristina Carmona_Duarte
   Josep Llados
   Miguel A. Ferrer
TI Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches.
BT International Workshop on Artificial Intelligence for Healthcare Applications
PY 2020
BP 476
EP 489
VL 12661
AB Assessing the physical condition in rehabilitation scenarios is a challenging problem, since it involves Human Activity Recognition (HAR) and kinematic analysis methods. In addition, the difficulties increase in unconstrained rehabilitation scenarios, which are much closer to the real use cases. In particular, our aim is to design an upper-limb assessment pipeline for stroke patients using smartwatches. We focus on the HAR task, as it is the first part of the assessing pipeline. Our main target is to automatically detect and recognize four key movements inspired by the Fugl-Meyer assessment scale, which are performed in both constrained and unconstrained scenarios. In addition to the application protocol and dataset, we propose two detection and classification baseline methods. We believe that the proposed framework, dataset and baseline results will serve to foster this research field.
ER