Bibliographic record
Abstract
The theory of around device interaction (ADI) has recently gained a lot of attention in the field of human computer interaction (HCI). As an alternative to the classic data entry methods, such as keypads and touch screens interaction, ADI proposes a touchless user interface that extends beyond the peripheral area of a device. In this paper, the authors propose a new approach for around mobile device interaction based on magnetic field. Our new approach, which we call it “MagiThings”, takes the advantage of digital compass (a magnetometer) embedded in new generation of mobile devices such as Apple’s iPhone 3GS/4G, and Google’s Nexus. The user movements of a properly shaped magnet around the device deform the original magnetic field. The magnet is taken or worn around the fingers. The changes made in the magnetic field pattern around the device constitute a new way of interacting with the device. Thus, the magnetic field encompassing the device plays the role of a communication channel and encodes the hand/finger movement patterns into temporal changes sensed by the compass sensor. The mobile device samples momentary status of the field. The field changes, caused by hand (finger) gesture, is used as a basis for sending interaction commands to the device. The pattern of change is matched against pre-recorded templates or trained models to recognize a gesture. The proposed methodology has been successfully tested for a variety of applications such as interaction with user interface of a mobile device, character (digit) entry, user authentication, gaming, and touchless mobile music synthesis. The experimental results show high accuracy in recognizing simple or complex gestures in a wide range of applications. The proposed method provides a practical and simple framework for touchless interaction with mobile devices relying only on an internally embedded sensor and a magnet.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.017 |
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".