HaloTouch: Using IR Multi-Path Interference to Support Touch Interactions with General Surfaces
Bibliographic record
Abstract
Sensing touch on arbitrary surfaces has long been a goal of ubiquitous computing, but often requires instrumenting the surface.Depth camera-based systems have emerged as a promising solution for minimizing instrumentation, but at the cost of high touch-down detection error rates, high touch latency, and high minimum hover distance, limiting them to basic tasks.We developed HaloTouch, a vision-based system which exploits a multipath interference effect from an off-the-shelf time-of-flight depth camera to enable fast, accurate touch interactions on general surfaces.HaloTouch achieves a 99.2% touch-down detection accuracy across various materials, with a motion-to-photon latency of 150 ms.With a brief (20s) userspecific calibration, HaloTouch supports millimeter-accurate hover sensing as well as continuous pressure sensing.We conducted a user study with 12 participants, including a typing task demonstrating text input at 26.3 AWPM.HaloTouch shows promise for more robust, dynamic touch interactions without instrumenting surfaces or adding hardware to users.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".