From pose estimation accuracy to motor biomarker reliability: a reliability-centred evaluation framework for markerless movement analysis

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.

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