EMBC Special Issue: Calibrated Uncertainty for Trustworthy Clinical Gait Analysis Using Probabilistic Multiview Markerless Motion Capture

• URL: http://arxiv.org/abs/2601.22412v1
• Date: Thu, 29 Jan 2026 23:50:19 GMT
• Title: EMBC Special Issue: Calibrated Uncertainty for Trustworthy Clinical Gait Analysis Using Probabilistic Multiview Markerless Motion Capture
• Authors: Seth Donahue, Irina Djuraskovic, Kunal Shah, Fabian Sinz, Ross Chafetz, R. James Cotton,
• Abstract summary: Video-based human movement analysis holds potential for movement assessment in clinical practice and research.<n>This study evaluates the calibration and reliability of a probabilistic multi-view markerless motion capture method.<n>Probability model reconstruction quantifies uncertainty, allowing it to identify unreliable outputs without the need for concurrent ground-truth instrumentation.
• Score: 1.260797434681533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video-based human movement analysis holds potential for movement assessment in clinical practice and research. However, the clinical implementation and trust of multi-view markerless motion capture (MMMC) require that, in addition to being accurate, these systems produce reliable confidence intervals to indicate how accurate they are for any individual. Building on our prior work utilizing variational inference to estimate joint angle posterior distributions, this study evaluates the calibration and reliability of a probabilistic MMMC method. We analyzed data from 68 participants across two institutions, validating the model against an instrumented walkway and standard marker-based motion capture. We measured the calibration of the confidence intervals using the Expected Calibration Error (ECE). The model demonstrated reliable calibration, yielding ECE values generally < 0.1 for both step and stride length and bias-corrected gait kinematics. We observed a median step and stride length error of ~16 mm and ~12 mm respectively, with median bias-corrected kinematic errors ranging from 1.5 to 3.8 degrees across lower extremity joints. Consistent with the calibrated ECE, the magnitude of the model's predicted uncertainty correlated strongly with observed error measures. These findings indicate that, as designed, the probabilistic model reconstruction quantifies epistemic uncertainty, allowing it to identify unreliable outputs without the need for concurrent ground-truth instrumentation.

Related papers

• Biomechanical Reconstruction with Confidence Intervals from Multiview Markerless Motion Capture [2.07180164747172]
  We show confidence intervals over specific kinematic estimates from a specific individual analyzed using a possibly unique camera configuration.<n>We extend our previous work using an implicit representation of trajectories optimized end-to-end through a differentiable biomechanical model.<n>This posterior probability is learned through a variational approximation and estimates confidence intervals for individual joints at each moment in a trial.
  arXiv  Detail & Related papers  (2025-02-10T14:04:57Z)
• On the good reliability of an interval-based metric to validate prediction uncertainty for machine learning regression tasks [0.0]
  This study presents an opportunistic approach to a (more) reliable validation method for prediction uncertainty average calibration. Considering that variance-based calibration metrics are quite sensitive to the presence of heavy tails in the uncertainty and error distributions, a shift is proposed to an interval-based metric, the Prediction Interval Coverage Probability (PICP) The resulting PICPs are more quickly and reliably tested than variance-based calibration metrics.
  arXiv  Detail & Related papers  (2024-08-23T14:16:10Z)
• A Confidence Interval for the $\ell_2$ Expected Calibration Error [35.88784957918326]
  We develop confidence intervals $ell$ Expected the Error (ECE)<n>We consider top-1-to-$k$ calibration, which includes both the popular notion of confidence calibration as well as calibration.<n>For a debiased estimator of the ECE, we show normality, but with different convergence rates and variances for calibrated and misd models.
  arXiv  Detail & Related papers  (2024-08-16T20:00:08Z)
• Assessing the Probabilistic Fit of Neural Regressors via Conditional Congruence [2.13382635602206]
  Existing approaches for measuring this misalignment are primarily developed under the framework of calibration.<n>We introduce a metric, Congruence Error (CCE), that uses conditional kernel mean embeddings to estimate the distance, at any point, between the learned predictive distribution and the empirical, conditional distribution in a dataset.<n>We perform several high dimensional regression tasks and show that CCE exhibits four critical properties: $textitcorrectness$, $textitmonotonicity$, $textitreliability$, and $textitrobustness$.
  arXiv  Detail & Related papers  (2024-05-20T23:30:07Z)
• Calibrating Large Language Models with Sample Consistency [76.23956851098598]
  We explore the potential of deriving confidence from the distribution of multiple randomly sampled model generations, via three measures of consistency. Results show that consistency-based calibration methods outperform existing post-hoc approaches. We offer practical guidance on choosing suitable consistency metrics for calibration, tailored to the characteristics of various LMs.
  arXiv  Detail & Related papers  (2024-02-21T16:15:20Z)
• Selective Learning: Towards Robust Calibration with Dynamic Regularization [79.92633587914659]
  Miscalibration in deep learning refers to there is a discrepancy between the predicted confidence and performance. We introduce Dynamic Regularization (DReg) which aims to learn what should be learned during training thereby circumventing the confidence adjusting trade-off.
  arXiv  Detail & Related papers  (2024-02-13T11:25:20Z)
• Calibration by Distribution Matching: Trainable Kernel Calibration Metrics [56.629245030893685]
  We introduce kernel-based calibration metrics that unify and generalize popular forms of calibration for both classification and regression. These metrics admit differentiable sample estimates, making it easy to incorporate a calibration objective into empirical risk minimization. We provide intuitive mechanisms to tailor calibration metrics to a decision task, and enforce accurate loss estimation and no regret decisions.
  arXiv  Detail & Related papers  (2023-10-31T06:19:40Z)
• Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation [50.920911532133154]
  The intrinsic ill-posedness and ordinal-sensitive nature of monocular depth estimation (MDE) models pose major challenges to the estimation of uncertainty degree. We propose to model the uncertainty of MDE models from the perspective of the inherent probability distributions. By simply introducing additional training regularization terms, our model, with surprisingly simple formations and without requiring extra modules or multiple inferences, can provide uncertainty estimations with state-of-the-art reliability.
  arXiv  Detail & Related papers  (2023-07-19T12:11:15Z)
• Conservative Prediction via Data-Driven Confidence Minimization [70.93946578046003]
  In safety-critical applications of machine learning, it is often desirable for a model to be conservative. We propose the Data-Driven Confidence Minimization framework, which minimizes confidence on an uncertainty dataset.
  arXiv  Detail & Related papers  (2023-06-08T07:05:36Z)
• Towards Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertainty [57.023423137202485]
  Concerns regarding the reliability of medical image segmentation persist among clinicians.<n>We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks.<n>By leveraging subjective logic theory, we explicitly model probability and uncertainty for medical image segmentation.
  arXiv  Detail & Related papers  (2023-01-01T05:02:46Z)
• Calibrate: Interactive Analysis of Probabilistic Model Output [5.444048397001003]
  We present Calibrate, an interactive reliability diagram that is resistant to drawbacks in traditional approaches. We demonstrate the utility of Calibrate through use cases on both real-world and synthetic data.
  arXiv  Detail & Related papers  (2022-07-27T20:01:27Z)
• Machine learning for causal inference: on the use of cross-fit estimators [77.34726150561087]
  Doubly-robust cross-fit estimators have been proposed to yield better statistical properties. We conducted a simulation study to assess the performance of several estimators for the average causal effect (ACE) When used with machine learning, the doubly-robust cross-fit estimators substantially outperformed all of the other estimators in terms of bias, variance, and confidence interval coverage.
  arXiv  Detail & Related papers  (2020-04-21T23:09:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.