GraspUI: Seamlessly Integrating Object-Centric Gestures within the Seven Phases of Grasping
Bibliographic record
Abstract
Objects are indispensable tools in our daily lives. Recent research has demonstrated their potential to act as conduits for digital interactions with microgestures, however, the primary focus was on situations where the hand firmly grasps an object. We introduce GraspUI, an exploratory design space of object-centric gestures within the seven distinct phases of the grasping process, spanning pre-, during, and post-grasp movements. We conducted ideation sessions with mixed-reality designers from industry and academia to explore gesture integration throughout the entire grasping process. The outcome was 38 storyboards envisioning practical applications. To evaluate the design space’s utility, we performed a video-based assessment with end-users. We then implemented an interactive prototype and quantified the overhead cost of performing proposed gestures through a secondary study. Participants reacted positively to gestures and could integrate them into existing usage of objects. To conclude, we highlight technical and usability guidelines for implementing and extending GraspUI systems.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".