A 2D Hand Pose Estimation System Accuracy for Finger Tapping Test Monitoring: A Pilot Study

Accurate and accessible motor function quantification is important for monitoring the movement disorders’ progression. Manual muscle testing models and wearable sensors can be costly or reduce degrees of freedom. Artificial intelligence, especially human pose estimation (PE), offers promising alternatives. This work aims to compare the accuracy of a 2D PE tool for the Finger Tapping Test (FTT) with a 3D infrared motion capture system (MoCap). PE tracked three anatomical landmarks (wrist, thumb, index finger), while reflective markers were placed at corresponding locations on both tools to measure wrist-centered angles. Different trials of slow and rapid FTT sessions were statistically analyzed by rank correlation analysis, Friedman, Bland–Altman, and Kruskal–Wallis to assess agreement and repeatability. PE and MoCap measurements showed no significant differences (p > 0.05), with high reliability (ICC 0.87–0.91), low variability (CV 6–8.6%), and negligible effect size. Bland–Altman slopes indicated minor amplitude-dependent bias, while RMSE (2.92–4.48°) and MAPE (6.38–8.22%) errors occurred in slow and rapid conditions. These results demonstrate that 2D PE provides a reliable, accessible, and low-cost alternative for quantifying finger movement. The findings suggest that PE can serve as an assistive method for monitoring motor function. Future studies can be population-level studies with patients with neurological disorders.