Videographic-Free Tracking of Hyoid Bone Displacement During Swallowing Using Accelerometer Signals and Transformers
Bibliographic record
Abstract
Objective: Accurate tracking of anatomical landmarks during swallowing is critical for early diagnosis and treatment of dysphagia. Hyoid bone displacement plays a pivotal role in upper esophageal sphincter opening and airway protection, traditionally assessed via a videofluoroscopic swallow study (VFSS). However, VFSSs are subjective, expose patients to radiation, and are not universally accessible. High-resolution cervical auscultation (HRCA) offers a noninvasive alternative, utilizing acoustic and vibratory signals. Prior studies have validated HRCA’s efficacy in analyzing swallowing kinematics and correlating with hyoid bone displacement, typically employing transform domain characteristics and recurrent neural networks to achieve 50% overlap in predicted displacements Methods: We introduce a transformer-based architecture for tracking hyoid bone displacement directly from raw HRCA signals, leveraging advanced temporal and spatial feature extraction methods using attention mechanism. The proposed pipeline preprocesses HRCA signals, segments individual swallows, and tracks the hyoid bone. Results: Our approach significantly improves upon existing methods, achieving over 70% relative overlap in predicted hyoid bone displacements across validation folds, surpassing state-of-the-art baseline models by a margin of at least 20%. Comprehensive statistical analysis confirms the robustness and accuracy of our predictions, demonstrating strong generalization capabilities on an independent dataset. Conclusion: This novel approach underscores the potential of transformer models in promoting noninvasive dysphagia assessment, offering a precise tracking of hyoid bone without VFSS images, and providing clinicians with insights about its movement trends, potentially aiding in clinical decision-making and bringing us one step closer to automated noninvasive swallowing assessment protocols. Clinical impact: This study highlights the potential of automated hyoid bone tracking using HRCA signals to enhance dysphagia assessment by providing objective, noninvasive measurements that potentially support earlier detection and monitoring of swallowing impairments in both clinical and home healthcare settings, ultimately improving patient management and treatment outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".