In-Ear System for Monitoring Lower Jaw Motion: A Novel Approach to Speech Intention Detection in Laryngectomy Patients
Bibliographic record
Abstract
OBJECTIVE: Restoring speech following laryngectomy presents a significant clinical challenge due to the reliance of existing artificial voice systems on manual operation. This limitation reduces their practicality and social acceptability, emphasizing the critical need for hands-free solutions. In this proof-of-concept study, we investigate an untapped biomarker: deformation of the outer ear canal induced by lower jaw motion, as a discreet and reliable indicator of speech intention. METHODS: We propose a novel in-ear device designed to monitor these deformations and enable intuitive, hands-free control of artificial voice systems. The prototype device is equipped with four infrared proximity sensors, housed within a 3D-printed enclosure. Testing protocol assessed the device's ability to detect fundamental jaw movements (protrusion, retraction, depression/elevation, and lateral shifting), integrated movements (chewing and coughing), and speech patterns (isolated vowels, single words, and complete sentences). RESULTS: Device performance was validated through comparison with surface electromyography and audio recordings, confirming its accuracy in detecting both activity onset and termination across all tested movements and speech patterns. Reproducibility of recorded signals was established across independent trials, with the device removed and repositioned within the ear canal between trials. CONCLUSION: We demonstrated that outer ear canal deformation reliably captures mandibular movements, including those associated with speech, establishing its potential as a discreet and reproducible biomarker for intuitive, hands-free control of artificial voice systems. SIGNIFICANCE: By offering a non-invasive, socially acceptable solution, this approach holds significant promise for enhancing the quality of life for laryngectomy patients and facilitating their reintegration into social and professional environments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".