Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches

TL;DR

This paper presents a smartwatch-based system for automatic upper-limb movement assessment in stroke rehabilitation, focusing on human activity recognition to detect key movements in real-world scenarios.

Contribution

It introduces a novel assessment pipeline, a dataset, and baseline methods for recognizing stroke-related movements using smartwatches in unconstrained environments.

Findings

Baseline detection and classification results provided.

Framework applicable to real-world rehabilitation scenarios.

Dataset supports further research in HAR for stroke assessment.

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.