Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches
This work addresses the challenge of motor assessment for stroke patients in rehabilitation, but it is incremental as it introduces a framework and dataset with baseline methods rather than a breakthrough.
The paper tackled the problem of automatically assessing upper-limb movements in stroke patients using smartwatches by focusing on Human Activity Recognition to detect four key movements from the Fugl-Meyer scale, achieving baseline results for both constrained and unconstrained scenarios.
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.