Exploring the ’EarSwitch’ concept: a novel ear based control method for assistive technology
Bibliographic record
Abstract
BACKGROUND: Loss of communication with loved ones and carers is one of the most isolating and debilitating effects of many neurological disorders. Assistive technology (AT) supports individuals with communication, but the acceptability of AT solutions is highly variable. In this paper a novel ear based control method of AT, the concept of 'EarSwitch', is presented. This new approach is based on detecting ear rumbling, which is the voluntary contraction of the tensor tympani muscle (TTM), resulting in observable movement of the eardrum and a dull rumbling sound. 'EarSwitch' has the potential to be a discreet method that can complement existing AT control methods. However, only a subset of the population can ear rumble and little is known about the ability of rumbling in populations with neurological disorders. METHODS: To explore the viability of the 'EarSwitch' concept as an AT control method we conducted in-depth online surveys with (N=1853) respondents from the general population and (N=170) respondents with self-declared neurological disorders including Motor Neurone Disease (MND) and Multiple Sclerosis (MS).This is the largest ever study to explore ear rumbling and the first to explore whether rumbling is preserved among individuals with neurological disorders. In addition, we validated rumbling, and investigated usability of the 'EarSwitch' concept as a control input, using in-person otoscopic examination with a subset of participants. RESULTS: A significant proportion of the population with neurological disorders could benefit from 'EarSwitch' controllable AT. The upper bound prevalence of the ability to rumble without accompanying movements was 55% in the general population, 38% in the neurological population, and 20% of participants with MND (N=95) reported this ability. During the validation procedure, participants achieved high accuracy in self-reporting the ability to rumble (80%) and proved concept of using the 'EarSwitch' method to control a basic interface. DISCUSSION: 'EarSwitch' is a potential new AT control method control, either by itself or as a supplement to other existing methods. Results demonstrate self-reported ear rumbling is present among patients with different neurological disorders, including MND. Further research should explore how well the ability to rumble is preserved in different types and stages of neurological disorders.
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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 | 0.003 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".