Design of Interactions for Handheld Augmented Reality Devices Using Wearable Smart Textiles: Findings from a User Elicitation Study
Bibliographic record
Abstract
Advanced developments in handheld devices’ interactive 3D graphics capabilities, processing power, and cloud computing have provided great potential for handheld augmented reality (HAR) applications, which allow users to access digital information anytime, anywhere. Nevertheless, existing interaction methods are still confined to the touch display, device camera, and built-in sensors of these handheld devices, which suffer from obtrusive interactions with AR content. Wearable fabric-based interfaces promote subtle, natural, and eyes-free interactions which are needed when performing interactions in dynamic environments. Prior studies explored the possibilities of using fabric-based wearable interfaces for head-mounted AR display (HMD) devices. The interface metaphors of HMD AR devices are inadequate for handheld AR devices as a typical HAR application require users to use only one hand to perform interactions. In this paper, we aim to investigate the use of a fabric-based wearable device as an alternative interface option for performing interactions with HAR applications. We elicited user-preferred gestures which are socially acceptable and comfortable to use for HAR devices. We also derived an interaction vocabulary of the wrist and thumb-to-index touch gestures, and present broader design guidelines for fabric-based wearable interfaces for handheld augmented reality applications.
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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".