Bayesian Estimation of Hand Kinematics from Spatially Tracked Landmarks
Bibliographic record
Abstract
A Bayesian framework for estimating finger joint kinematics from spatially tracked hand landmarks was introduced in this study.Three-dimensional landmark data were constructed by augmenting image-based two-dimensional hand landmarks with calibrated depth information.A hierarchy of coordinate frames was established, beginning with the palm as the root and extending to child frames assigned to each finger, thereby encoding the natural kinematic dependencies of the hand.This hierarchical representation provides the structural foundation for Bayesian estimation.Finger joint parameters were estimated within a maximum likelihood framework that is robust to tracking noise and signal occlusions, which are common in practical hand-tracking scenarios.Unlike data-driven methods, the proposed approach does not rely on pre-collected training datasets but instead leverages the kinematic model and intrinsic physical constraints of the human hand.The estimation problem was formalized as a Gaussian Bayesian Network (GBN), through which joint parameters were inferred using Maximum Likelihood Estimation (MLE).Robustness of the approach was qualitatively demonstrated through reconstructed graphical configurations that illustrate accurate recovery of finger postures under noisy conditions.This method provides a principled framework for hand motion reconstruction and establishes the foundation for future quantitative evaluations against benchmark datasets.The framework is expected to advance applications in human-computer interaction, prosthetic design, virtual reality (VR), and rehabilitation by enabling more reliable and anatomically consistent hand tracking.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".