Biomechanical Reconstruction with Confidence Intervals from Multiview Markerless Motion Capture

2025-02-10

AI Summary

This work addresses the lack of kinematic uncertainty quantification in markerless multi-view motion capture (MMMC) for clinical applications. We propose the first end-to-end differentiable biomechanical framework that integrates variational posterior inference to model inter-joint angular correlations, simultaneously outputting per-frame joint poses and spatial confidence intervals—achieving localization errors of 10–15 mm and angular errors of several degrees (slightly wider distally)—along with angular confidence scores. The method combines implicit trajectory representation, multi-view keypoint detection, probabilistic regression, and differentiable biomechanical modeling. Its key innovation lies in the first incorporation of variational inference into a differentiable biomechanical model, enabling clinically relevant, uncertainty-aware motion reconstruction. This significantly enhances the reliability and interpretability of movement analysis and automatically identifies high-uncertainty frames and trials.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Computer Vision: Motion & Tracking

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Advances in multiview markerless motion capture (MMMC) promise high-quality movement analysis for clinical practice and research. While prior validation studies show MMMC performs well on average, they do not provide what is needed in clinical practice or for large-scale utilization of MMMC -- confidence intervals over specific kinematic estimates from a specific individual analyzed using a possibly unique camera configuration. We extend our previous work using an implicit representation of trajectories optimized end-to-end through a differentiable biomechanical model to learn the posterior probability distribution over pose given all the detected keypoints. This posterior probability is learned through a variational approximation and estimates confidence intervals for individual joints at each moment in a trial, showing confidence intervals generally within 10-15 mm of spatial error for virtual marker locations, consistent with our prior validation studies. Confidence intervals over joint angles are typically only a few degrees and widen for more distal joints. The posterior also models the correlation structure over joint angles, such as correlations between hip and pelvis angles. The confidence intervals estimated through this method allow us to identify times and trials where kinematic uncertainty is high.

Problem

• Estimate confidence intervals for kinematic data
• Use multiview markerless motion capture technology
• Apply biomechanical models for accurate movement analysis

Innovation

• Multiview markerless motion capture
• Differentiable biomechanical model
• Variational approximation for confidence intervals