Analyzing play experience sensitivity to input sensor noise in outdoor augmented reality smartphone games
Bibliographic record
Abstract
Augmented reality games overlay digital artifacts on a camera feed of the environment to create fantastic experiences in the real world. The act of overlaying digital artifacts on a real environment requires detailed information about the relative pose of the player and digital artifacts be accurately sensed and computed, which is often beyond the capacity of sensor systems deployed on commercial devices such as smartphones. Game developers are adept at creating compelling experiences from a limited or noisy palette of interactions, but have limited guidance in the case of augmented reality games. In this paper, we present a novel technique for evaluating the sensitivity of augmented reality games and game mechanics to input noise by modifying the sensor input stream of an open source operating system in a controlled manner. Any game, commercial or academic, that runs on that operating system can be systematically tested for the user experience impact of differing levels of sensor input noise. We perform such an experiment on two commercial and one academic game and determine that similar levels of input noise have very different impacts on user experience depending on the game design, input modality, and narrative. The differential impact of noise on user experience is important because it indicates that proper design decisions can be used ameliorate or mask sensor noise issues.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".