From pose estimation accuracy to motor biomarker reliability: a reliability-centred evaluation framework for...

Markerless pose estimation has become an essential tool for quantitative movement analysis in neuroscience. However, current evaluation frameworks primarily rely on frame-wise spatial accuracy metrics, which provide only limited information about the reliability of the downstream kinematic biomarkers derived from reconstructed trajectories. In this study, we propose a reliability-centred evaluation framework designed to assess the preservation of downstream kinematic information in pose estimation pipelines. The framework was evaluated exclusively using DeepLabCut-based architectures trained and tested under identical experimental conditions. Forelimb trajectories of mice performing the Montoya staircase task were analysed using nine DeepLabCut-based convolutional neural network architectures. We introduce the Motor Biomarker Reliability Composite Score (MBRCS), which integrates four complementary dimensions of trajectory reliability: spatial accuracy, temporal stability, occlusion robustness, and kinematic fidelity. Each component was normalized to a 0-1 scale, where higher values indicate greater trajectory reliability. Although several architectures achieved comparable frame-wise localisation performance, larger differences were observed in temporal stability, occlusion robustness and downstream kinematic fidelity. Across the evaluated architectures, MBRCS values spanned almost the entire normalized range (0.00-0.99). These differences were reflected in the Normalized Jerk Score (NJS), which was used as the downstream kinematic biomarker in this study. Overall, our findings suggest that frame-wise localisation accuracy alone may not fully capture differences in trajectory reliability that become apparent when temporal stability, occlusion robustness and kinematic fidelity are jointly considered. The proposed framework provides a structured and reproducible methodology for benchmarking pose estimation pipelines according to the reliability of downstream motor biomarkers.