Data Acquisition Through Participatory Design for Automated Rehabilitation Assessment

TL;DR

This study develops a participatory design-based system for semi-automated stroke rehabilitation assessment using multi-camera capture, segmentation, and rating interfaces, involving clinicians in data collection and validation.

Contribution

It introduces a novel participatory design approach for collecting and validating rehabilitation data with multi-camera capture and user-friendly interfaces.

Findings

Five clinicians captured 1800 videos with less than 5% errors.

Three segmentors segmented 760 videos, averaging 20 seconds per segment.

Clinicians preferred the recommended camera view over 90%.

Abstract

Through participatory design, we are developing a computational system for the semi-automated assessment of the Action Research Arm Test (ARAT) for stroke rehabilitation. During rehabilitation assessment, clinicians rate movement segments and components in the context of overall task performance. Clinicians change viewing angles to assess particular components. Through studies with clinicians, we develop a system that includes: a) unobtrusive multi-camera capture, b) a segmentation interface for non-expert segmentors, and c) a rating interface for expert clinicians. Five clinicians independently captured 1800 stroke survivor videos with <5$\%$ errors. Three segmentors have segmented 760 of these videos, averaging 20 seconds per segment. They favor the recommended camera view $>$ 90\%. Multiple clinicians have rated the segmented videos while reporting minimal problems. The complete data will be used for training an automated segmentation and rating system that empowers the clinicians as the ratings will be compatible with clinical practice and intuition.