Ululate: A Non-Intrusive, Wearable Tongue Gesture Detection System for Human-Computer Interaction
Bibliographic record
Abstract
Human-computer interaction (HCI) focuses on improving the user's interaction with the computer.HCI enhances user experience in a wide range of applications, such as medical, security, autonomous vehicles, and wearable smart devices.While several systems have already developed tongue-based HCI that aim to be used as an input device, the majority require tongue piercing, dental retainers, and multiple electrodes on the chin, in the mouth or ears.These approaches are generally unhygienic, intrusive, and unsuitable to use in public areas esthetically.In this study, we designed Ululate, a hygienic, unobtrusive, and non-intrusive tongue gesture detection system that detects tongue movement by measuring vibration on the neck.The proposed system uses a sensing unit (accelerometer) that can be positioned below the lower jaw on the Genioglossus muscle.Hence, it does not require any in-mouth installation.Classification is conducted using four types of supervised machine learning algorithms, namely K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Tree, and Random Forest, then the accuracy of each algorithm using five different accuracy matrices is compared.The initial result of tongue gestures demonstrates that random forest shows the highest accuracy (97%).The overall designed system is lightweight, low profile, and low cost, which makes it efficient for everyday use.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".