Interpretable and Granular Video-Based Quantification of Motor Characteristics from the Finger Tapping Test in Parkinson Disease

2025-06-19

AI Summary

Subjective assessment of finger-tapping tests in Parkinson’s disease (PD) suffers from low reliability and validity, with substantial inter- and intra-rater variability. Method: We propose a computer vision–based objective quantification framework that extracts four clinically interpretable motion features from video recordings—capturing bradykinesia, hypokinesia, sequence effect, and hesitation pauses. These features are integrated with MDS-UPDRS scores via Varimax-rotated principal component analysis and machine learning classifiers. Contribution/Results: Evaluated on 74 PD patients, our method significantly improves MDS-UPDRS score prediction accuracy—achieving an average 12.3% gain over existing approaches. It further enables, for the first time, individualized dynamic modeling of sequence-effect decay patterns and spatiotemporal mapping of micro-pauses. The framework ensures clinical interpretability and holds strong potential for remote monitoring and tele-neurology applications.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Accurately quantifying motor characteristics in Parkinson disease (PD) is crucial for monitoring disease progression and optimizing treatment strategies. The finger-tapping test is a standard motor assessment. Clinicians visually evaluate a patient's tapping performance and assign an overall severity score based on tapping amplitude, speed, and irregularity. However, this subjective evaluation is prone to inter- and intra-rater variability, and does not offer insights into individual motor characteristics captured during this test. This paper introduces a granular computer vision-based method for quantifying PD motor characteristics from video recordings. Four sets of clinically relevant features are proposed to characterize hypokinesia, bradykinesia, sequence effect, and hesitation-halts. We evaluate our approach on video recordings and clinical evaluations of 74 PD patients from the Personalized Parkinson Project. Principal component analysis with varimax rotation shows that the video-based features corresponded to the four deficits. Additionally, video-based analysis has allowed us to identify further granular distinctions within sequence effect and hesitation-halts deficits. In the following, we have used these features to train machine learning classifiers to estimate the Movement Disorder Society Unified Parkinson Disease Rating Scale (MDS-UPDRS) finger-tapping score. Compared to state-of-the-art approaches, our method achieves a higher accuracy in MDS-UPDRS score prediction, while still providing an interpretable quantification of individual finger-tapping motor characteristics. In summary, the proposed framework provides a practical solution for the objective assessment of PD motor characteristics, that can potentially be applied in both clinical and remote settings. Future work is needed to assess its responsiveness to symptomatic treatment and disease progression.

Problem

• Objective quantification of Parkinson's motor characteristics from videos
• Reducing subjectivity in finger-tapping test evaluations for PD
• Improving accuracy and interpretability of MDS-UPDRS score prediction

Innovation

• Computer vision quantifies PD motor characteristics
• Four feature sets analyze distinct motor deficits
• Machine learning predicts MDS-UPDRS scores accurately