Towards Stroke Patients’ Upper-Limb Automatic Motor Assessment Using Smartwatches

Assessing the physical condition in rehabilitation scenarios is a challenging problem, since it involves Human Activity Recognition (HAR) and kinematic analysis methods. In addition, difficulties increase unconstrained scenarios, which are much closer to real use cases. particular, our aim design an upper-limb assessment pipeline for stroke patients using smartwatches. We focus on HAR task, as first part of assessing pipeline. Our main target automatically detect recognize four key movements inspired by Fugl-Meyer scale, performed both constrained scenarios. addition application protocol dataset, we propose two detection classification baseline believe that proposed framework, dataset results will serve foster this research field.