Segmentation of Dual-Axis Swallowing Accelerometry Signals in Healthy Subjects With Analysis of Anthropometric Effects on Duration of Swallowing Activities
Bibliographic record
Abstract
Dysphagia (swallowing difficulty) is a serious and debilitating condition that often accompanies stroke, acquired brain injury, and neurodegenerative illnesses. Individuals with dysphagia are prone to aspiration (the entry of foreign material into the airway), which directly increases the risk of serious respiratory consequences such as pneumonia. Swallowing accelerometry is a promising noninvasive tool for the detection of aspiration and the evaluation of swallowing. In this paper, dual-axis accelerometry was implemented since the motion of the hyolaryngeal complex occurs in both anterior-posterior and superior-inferior directions during swallowing. Dual-axis cervical accelerometry signals were acquired from 408 healthy subjects during dry, wet, and wet chin tuck swallowing tasks. The proposed segmentation algorithm is based on the idea of sequential fuzzy partitioning of the signal and is well suited for long signals with nonstationary variance. The algorithm was validated with simulated signals with known swallowing locations and a subset of 295 real swallows manually segmented by an experienced speech language pathologist. In both cases, the algorithm extracted individual swallows with over 90% accuracy. The time duration analysis was carried out with respect to gender, body mass index (BMI), and age. Demographic and anthropometric variables influenced the duration of these segmented signals. Male participants exhibited longer swallows than female participants (p=0.05). Older participants and participants with higher BMIs exhibited swallows with significantly longer (p=0.05) duration than younger participants and those with lower BMIs, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.000 | 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 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".