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 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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".