Toward a Mathematical Model for Quality of Experience Evaluation of Haptic Applications
Why this work is in the frame
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Bibliographic record
Abstract
There is rapid progress in the advancement of user interfaces. One such advancement is enabling the sense of touch, or haptics, as part of the interface. Haptic devices are seeing growth in many types of applications such as gaming and medical simulation. Assessing the quality of experience (QoE) of the user is necessary to evaluate how the user perceives such interfaces. The QoE is a user-centric parameter that shifts the paradigm of evaluation from the technology itself to the user. This paper proposes a mathematical-based QoE evaluation of haptic-based applications. A mathematical model that is able to quantify the QoE of the user is described. By conducting a user study in which users evaluate a haptic-based game application, we were able to test and validate the mathematical model. There are several approaches in determining the weights to be used with the mathematical model. This paper presents and compares different approaches for weight determination, namely even weight distribution, correlation-based weights, even weights–correlation combination, linear regression analysis, and principal component analysis (PCA). Our results show that PCA weight determination performs slightly better than the rest of the approaches.
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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 | 0.000 |
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 it