Exploring Body-Anchored Augmented Reality Interfaces Across Different Mobility and Social Contexts
Bibliographic record
Abstract
Researchers explore various on-body locations for anchoring user interfaces in Augmented Reality (AR), primarily to leverage onbody haptic feedback and enhance usability. However, most studies are conducted in controlled laboratory settings, limiting their applicability to real-world use. Consequently, there remains a lack of research on identifying suitable on-body AR UI placements across diverse social and mobility contexts. To address this gap, we conduct a user study investigating user preferences for anchoring UIs on six on-body locations-palm, back of the palm, inner and outer forearm, and right and left lap-across different mobility conditions (standing, walking, sitting) and social contexts (private, semiprivate, and public settings). Results show that users prefer anchoring AR interfaces on the outer forearm and palm across all conditions. However, participants noted the limited interaction space on hand surfaces. To address this, a follow-up study evaluated six UI layouts anchored to the palm and forearm, comparing body-aligned vs. vertically oriented placements and small vs. enlarged interfaces. Results show that vertically oriented, enlarged layouts yield superior performance, offering context-sensitive guidance for designing AR interfaces that balance comfort, preference, and usability.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".