Running with Data: A Survey of the Current Research and a Design Exploration of Future Immersive Visualisations
Bibliographic record
Abstract
This work investigates the current research on in-situ visualisations for running: visualisations about data that are referred to during the running activity. We analyse 47 papers from 33 Human-Computer Interaction and Visualisation venues and identify six dimensions of a design space of in-situ running visualisations. Our analysis of this design space highlights an emerging trend: a shift from on-body, peripersonal visualisations (i.e., in the space within direct reach, such as visualisations on a smartwatch or a mobile phone display) towards extrapersonal displays (i.e., in the space beyond immediate reach, such as visualisations in immersive augmented reality displays) that integrate data in the runner's surrounding environment. We explore this opportunity by conducting a series of workshops with 10 active runners in total, eliciting design concepts for running visualisations and interactions beyond conventional 2D displays. We find that runners show a strong interest for visualisation designs that favour more context-aware, interactive, and unobtrusive experiences that seamlessly integrate with their run. These findings inform a set of design considerations for future immersive running visualisations and highlight directions for further research.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".