Analysis of Hand Movement and Head Orientation in Hierarchical Menu Selection in Immersive AR
Bibliographic record
Abstract
Immersive Augmented Reality (AR) has become pervasive across multiple domains, ranging from medicine and education to interior design, and other fields. Leading technology giants like Apple, Meta, and Microsoft have introduced their proprietary Head Mounted Displays (HMD), joining the competitive market. This technology offers users an immersive experience by superimposing virtual 3D content onto real-world objects, allowing for various interactions and input modalities. Numerous Graphical User Interfaces (GUIs) have also been devised for immersive AR, integrating with input modalities such as hand gesture, head pointing, and voice commands. Since the most favored interaction method in immersive AR involves the interplay between hand gesture and head pointing, our research aims to analyze the workload exerted on users during hand gesture-head pointing interactions. For instance, a hierarchical menu selection task relies on both hand gesture (for item selection or click) and head pointing (to navigate the cursor). The degree of reliance on each input modality affects the user’s workload. We applied both objective and subjective means to assess the physical workload. Two user studies were conducted, from which we derived a unified metric to evaluate the interdependence of hand and head in immersive AR. A significant correlation was discovered between the metric and both objective and subjective workload assessments. Our findings provide insights for AR designers, suggesting the combined use of head pointing and hand gesture when creating hierarchical menu selection interfaces in immersive AR.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".