Enhancing Box and Block Test with Computer Vision for Post-Stroke Upper Extremity Motor Evaluation

TL;DR

This paper introduces a camera-based, calibration-free computer vision framework to analyze upper-extremity movement during the Box and Block Test, capturing detailed movement quality beyond traditional scores using monocular video recordings.

Contribution

The work presents a novel, unsupervised analysis method using joint angles from monocular videos to distinguish healthy and stroke-related movement patterns without expert labels.

Findings

Joint angles effectively differentiate healthy and stroke movement patterns.

Patients with similar scores can have different movement quality profiles.

The framework requires only simple monocular video recordings.

Abstract

Standard clinical assessments of upper-extremity motor function after stroke either rely on ordinal scoring, which lacks sensitivity, or time-based task metrics, which do not capture movement quality. In this work, we present a computer vision-based framework for analysis of upper-extremity movement during the Box and Block Test (BBT) through world-aligned joint angles of fingers, arm, and trunk without depth sensors or calibration objects. We apply this framework to a dataset of 136 BBT recordings collected from 48 healthy individuals and 7 individuals post stroke. Using unsupervised dimensionality reduction of joint-angle features, we analyze movement patterns without relying on expert clinical labels. The resulting embeddings show separation between healthy movement patterns and stroke-related movement deviations. Importantly, some patients with the same BBT scores can be separated with different postural patterns. These results show that world-aligned joint angles can capture meaningful information of upper-extremity functions beyond standard time-based BBT scores, with no effort from the clinician other than monocular video recordings of the patient using a phone or camera. This work highlights the potential of a camera-based, calibration-free framework to measure movement quality in clinical assessments without changing the widely adopted clinical routine.