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

Abstract

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.

Activity recognition of stroke‐affected people using wearable sensor

“…In this direction, Bensalah and others developed an upper‐limb assessment system specifically for stroke‐affected patients using data collected from digital watches. Their assessment system automatically detects and recognizes a variety of constrained activities [13]. Braakhuis and others developed a day‐to‐day activity stroke monitoring system for routine physical therapy.…”