A Mobile Gesture Interaction Method for Augmented Reality Games Using Hybrid Filters
Bibliographic record
Abstract
In most existing games, the separation of gaming operation and feedback regions results in a lower immersion. Therefore, a mobile gesture interaction method is proposed to unify operation and feedback regions to provide users with more immersive interaction experiences. Specifically, the proposed method integrates a Leap Motion (LM) device with a HoloLens (HL) augmented reality (AR) glasses, which provides a natural mobile interaction interface between real bare hands and virtual objects in a real environment. To obtain an accurate mobile interaction between the real bare hand and virtual objects, the proposed method provides an effective registration method between LM and HL AR glasses. To ensure the accuracy and stability of the gesture data, the Kalman filter (KF) and the Particle filter (PF) are applied to estimate the position and the orientation of the hand. The proposed interaction method provides a gaming interaction method similar to real world interaction in daily life, which significantly improves the immersive experience of games. Game experiments and user subjective experience analysis are performed to evaluate the effectiveness of the proposed method. Results show that the proposed method yields a better accuracy and provide users with a more immersive interaction experience.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".