The Study of Road Conditions that Affect Tilt-based Text Input for Mobile Devices
Bibliographic record
Abstract
With the rapid advances in technology of mobile devices, their application areas expand continuously as well. Given the growth of the market share for mobile devices, one of the main issues that arise is the need for new and convenient techniques for data input and output suitable for various settings. Tilt-based text input is one of the alternatives that attract attention of current researchers. This study investigates the potential of using tilt-based interaction to enter text while sitting as a passenger in a moving vehicle. In addition, the investigation focused on the characteristics of the road as well as driving conditions that affect tilt-based text input. The study manipulated the following factors: keyboard size 10×4 and road unevenness (small, moderate, and large). 14 participants, aged 25 to 50, took part in the user study. The best performance and the shortest task completion time equal to 24.5 seconds was observed when the vehicle was moving at a constant speed on a straight road with small unevenness. The task completion time increased by 15% in moderate unevenness road and by almost 20 % in large unevenness road. Error rate and movement efficiency were investigated additionally in order to find out the cause of such times.
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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".