HeadTrack: Real-Time Human–Computer Interaction via Wireless Earphones
Bibliographic record
Abstract
Accurate head movement tracking is crucial for virtual reality and Metaverse in ubiquitous human-computer interaction (HCI) applications. Existing works for head tracking with wearable VR kits and wireless signals require expensive devices and heavy algorithmic processing. To resolve this problem, we propose HeadTrack, a low-cost, high-precision head motion tracking system that uses commercially available wireless earphones to capture the user’s head motion in real-time. HeadTrack uses smartphones as ‘sound anchors’ and emits inaudible chirps picked up by the user’s wireless earphones. By measuring the time-of-flight of these signals from the smartphone to each microphone on the earphone, we can deduce the user’s face orientation and distance relative to the smartphone, enabling us to accurately track the user’s head movement. To realize HeadTrack, we use the cross-correlation method to optimize the Frequency Modulated Continuous Wave (FMCW) based acoustic ranging method, which solves the problem of insufficient wireless earphone bandwidth. Moreover, we solve the problems of asynchronous startup time between devices and the existence of sampling frequency offset. We conduct excessive experiments in real scenarios, and the results prove that HeadTrack can continuously track the direction of the user’s head, with an average error under 6.3° in pitch and 4.9° in yaw.
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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) | 0.000 | 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) | 0.008 | 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".