Exploring Tangible Interactions with Radar Sensing
Bibliographic record
Abstract
Research has explored miniature radar as a promising sensing technique for the recognition of gestures, objects, users' presence and activity. However, within Human-Computer Interaction (HCI), its use remains underexplored, in particular in Tangible User Interface (TUI). In this paper, we explore two research questions with radar as a platform for sensing tangible interaction with the counting, ordering, identification of objects and tracking the orientation, movement and distance of these objects. We detail the design space and practical use-cases for such interaction which allows us to identify a series of design patterns, beyond static interaction, which are continuous and dynamic. With a focus on planar objects, we report on a series of studies which demonstrate the suitability of this approach. This exploration is grounded in both a characterization of the radar sensing and our rigorous experiments which show that such sensing is accurate with minimal training. With these techniques, we envision both realistic and future applications and scenarios. The motivation for what we refer to as Solinteraction, is to demonstrate the potential for radar-based interaction with objects in HCI and TUI.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".