Video-Based Hand Pose Estimation for Remote Assessment of Bradykinesia in Parkinson's Disease

TL;DR

This study evaluates the accuracy of hand pose estimation models for remote assessment of Bradykinesia in Parkinson's Disease, finding that on-device recordings yield more reliable results than streaming videos during Telehealth.

Contribution

It systematically compares multiple pose estimation models on different recording conditions, highlighting their limitations in Telehealth video assessments.

Findings

Three models showed good accuracy on high-quality on-device videos.

Pose estimation accuracy significantly drops in streaming videos.

Most movement features are reliable in on-device recordings but not in streaming videos.

Abstract

There is a growing interest in using pose estimation algorithms for video-based assessment of Bradykinesia in Parkinson's Disease (PD) to facilitate remote disease assessment and monitoring. However, the accuracy of pose estimation algorithms in videos from video streaming services during Telehealth appointments has not been studied. In this study, we used seven off-the-shelf hand pose estimation models to estimate the movement of the thumb and index fingers in videos of the finger-tapping (FT) test recorded from Healthy Controls (HC) and participants with PD and under two different conditions: streaming (videos recorded during a live Zoom meeting) and on-device (videos recorded locally with high-quality cameras). The accuracy and reliability of the models were estimated by comparing the models' output with manual results. Three of the seven models demonstrated good accuracy for on-device recordings, and the accuracy decreased significantly for streaming recordings. We observed a negative correlation between movement speed and the model's accuracy for the streaming recordings. Additionally, we evaluated the reliability of ten movement features related to bradykinesia extracted from video recordings of PD patients performing the FT test. While most of the features demonstrated excellent reliability for on-device recordings, most of the features demonstrated poor to moderate reliability for streaming recordings. Our findings highlight the limitations of pose estimation algorithms when applied to video recordings obtained during Telehealth visits, and demonstrate that on-device recordings can be used for automatic video-assessment of bradykinesia in PD.