Exploring Fictional Scenarios of Pervasive Computing to Identify Meaningful Gestures for Intuitive Interactions
Bibliographic record
Abstract
Technological advances in pervasive computing have expanded the possibilities of seamless interactions between people and technology through the use of gestures. However, gesture-based interactions challenge designers who need to understand people's preferences when dealing with technology-enabled environments. Limitations of current technologies complicate the exploration of gesture-based interactions. This research examines fictional scenarios of possible futures for pervasive computing intending to identify opportunities for gestures that can replace traditional inputs. The examination of future technologies through fiction and centred on users' preferences contributes to understanding the potential of interactions focused on body movement and grounded in users' context. Applying gestures that reflect people's preferences may lead to the design of intuitive interactions. This study reveals a correlation between gestures observed in fiction and those generated by users. It also reveals issues and opportunities for improving gesture-based interactions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".