A Quality of Experience Model for Haptic Virtual Environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Haptic-based Virtual Reality (VR) applications have many merits. What is still obscure, from the designer's perspective of these applications, is the experience the users will undergo when they use the VR system. Quality of Experience (QoE) is an evaluation metric from the user's perspective that unfortunately has received limited attention from the research community. Assessing the QoE of VR applications reflects the amount of overall satisfaction and benefits gained from the application in addition to laying the foundation for ideal user-centric design in the future. In this article, we propose a taxonomy for the evaluation of QoE for multimedia applications and in particular VR applications. We model this taxonomy using a Fuzzy logic Inference System (FIS) to quantitatively measure the QoE of haptic virtual environments. We build and test our FIS by conducting a users' study analysis to evaluate the QoE of a haptic game application. Our results demonstrate that the proposed FIS model reflects the user's estimation of the application's quality significantly with low error and hence is suited for QoE evaluation.
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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