Impact of Video Quality on Video-based Digital Assessment of Parkinson’s Disease

Background: Vision-based digital biomarkers have emerged as promising tools for objectively (and possibly remotely) assessing Parkinson’s disease (PD) motor signs, addressing inherent limitations of traditional clinical scales like the Unified Parkinson’s Disease Rating Scale (UPDRS). However, real-world deployment is hindered by variability in video quality, particularly in uncontrolled home environments. Objective: To quantify the impact of video quality parameters on the accuracy of human pose estimation (HPE) and downstream clinical assessments, including finger-tapping event detection and UPDRS scoring. Methods: We analyzed videos of PD patients performing finger-tapping tasks across two settings: high-resolution recordings collected in controlled clinical environments (n=227: "clinical dataset") and patient-recorded videos from home settings (n=88: "home-recorded dataset"). To evaluate the effect of video quality on assessment accuracy, we introduced systematic degradations and assessed key parameters, including resolution, frame rate, lighting conditions, and hand visibility. Performance was measured using Mean Per Joint Position Error (MPJPE), Percentage of Correct Keypoints (PCK), and Estimated Landmark Failed Frames (ELFF). Results: Low frame rates and inadequate hand coverage within the frame significantly reduced the accuracy of HPE-based assessments. In contrast, video resolution had a less pronounced effect than expected. Conclusion: Frame rate and proper visibility of body parts are more critical than resolution for reliable at-home motor signs evaluation in Parkinson’s disease. We establish practical thresholds for video quality in remote PD assessments and provide actionable guidelines for optimizing remote monitoring systems.