ClickSense: A Low-Cost Tangible Active User Input Method Using Passive Acoustic Sensing for Mobile Virtual Reality
Bibliographic record
Abstract
Mobile virtual reality (MVR) employs smartphone-integrated head-mounted displays to provide low-cost immersive experiences widely available to the public. Compared to higher-fidelity devices, MVR offers poor interaction support notably lacking a mechanism to produce click events necessary to indicate selection. We propose ClickSense: a no-cost active input method based on acoustic sensing. ClickSense uses the smartphone's built-in microphone to passively listen to environmental audio to identify the unique click sound emitted from a handheld clicker. Keeping with the spirit of the low-cost nature of MVR, the clicker itself is made of recycled household objects for no cost. We present a sound recognition system that can easily run on lower-powered mobile devices. Unlike vision-based gestures, our approach is not dependent on the smartphone's camera fidelity. Our evaluation of the system reveals that it offers a 97.33% recognition rate when used in isolation. When used to indicate selection in a standard selection task, it offered a 95.05% recognition.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".