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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".