Enhancing Box and Block Test with Computer Vision for Post-Stroke Upper Extremity Motor Evaluation
This work addresses the need for more sensitive, calibration-free motor assessment tools for stroke patients, though it is incremental as it builds on existing computer vision methods applied to a clinical test.
The researchers tackled the problem of evaluating upper-extremity motor function after stroke by developing a computer vision framework to analyze movement quality during the Box and Block Test using monocular video, showing that it can separate healthy and stroke-related patterns and distinguish patients with identical standard scores.
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.