Spatial-geometric approach to physical mobile interaction based on accelerometer and IR sensory data fusion
Bibliographic record
Abstract
Interaction with the physical environment using mobile phones has become increasingly desirable and feasible. Nowadays mobile phones are being used to control different devices and access information/services related to those devices. To facilitate such interaction, devices are usually marked with RFID tags or visual markers, which are read by a mobile phone equipped with an integrated RFID reader or camera to fetch related information about those objects and initiate further actions. This article contributes in this domain of mobile physical interaction; however, using a spatial-geometric approach for interacting with indoor physical objects and artifacts instead of RFID based solutions. Using this approach, a mobile phone can point from a distance to an annotated object or a spatial subregion of that object for the purpose of interaction. The pointing direction and location is determined based on the fusion of IR camera and accelerometer data, where the IR cameras are used to calculate the 3D position of the mobile phone users and the accelerometer in the phone provides its tilting and orientation information. The annotation of objects and their subregions with which the mobile phone interacts is performed by specifying their geometric coordinates and associating related information or services with them. We perform experiment in a technology-augmented smart space and show the applicability and potential of the proposed approach.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".