Precise Embodied Data Selection with Haptic Feedback while Retaining Room-Scale Visualisation Context
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Room-scale immersive data visualisations provide viewers a wide-scale overview of a large dataset, but to interact precisely with individual data points they typically have to navigate to change their point of view. In traditional screen-based visualisations, focus-and-context techniques allow visualisation users to keep a full dataset in view while making detailed selections. Such techniques have been studied extensively on desktop to allow precise selection within large data sets, but they have not been explored in immersive 3D modalities. In this paper we develop a novel immersive focus-and-context technique based on a "magic portal" metaphor adapted specifically for data visualisation scenarios. An extendable-hand interaction technique is used to place a portal close to the region of interest. The other end of the portal then opens comfortably within the user's physical reach such that they can reach through to precisely select individual data points. Through a controlled study with 12 participants, we find strong evidence that portals reduce overshoots in selection and overall hand trajectory length, reducing arm and shoulder fatigue compared to ranged interaction without the portal. The portals also enable us to use a robot arm to provide haptic feedback for data within the limited volume of the portal region. In a second study with another 12 participants we found that haptics provided a positive experience (qualitative feedback) but did not significantly reduce fatigue. We demonstrate applications for portal-based selection through two use-case scenarios.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it