The proximity toolkit
Bibliographic record
Abstract
People naturally understand and use proxemic relationships (e.g., their distance and orientation towards others) in everyday situations. However, only few ubiquitous computing (ubicomp) systems interpret such proxemic relationships to mediate interaction (proxemic interaction). A technical problem is that developers find it challenging and tedious to access proxemic information from sensors. Our Proximity Toolkit solves this problem. It simplifies the exploration of interaction techniques by supplying fine-grained proxemic information between people, portable devices, large interactive surfaces, and other non-digital objects in a room-sized environment. The toolkit offers three key features. 1) It facilitates rapid prototyping of proxemic-aware systems by supplying developers with the orientation, distance, motion, identity, and location information between entities. 2) It includes various tools, such as a visual monitoring tool, that allows developers to visually observe, record and explore proxemic relationships in 3D space. (3) Its flexible architecture separates sensing hardware from the proxemic data model derived from these sensors, which means that a variety of sensing technologies can be substituted or combined to derive proxemic information. We illustrate the versatility of the toolkit with proxemic-aware systems built by students.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.073 | 0.039 |
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".