Active manipulation of users in haptic-enabled virtual environments
Bibliographic record
Abstract
The main goal of this research is to study the effect of sub-threshold forces on human performance in a haptic-enabled virtual reality system. A multi-modal task similar to Fitts is used to study the effects of the sub-threshold forces on user performance. Each user’s movement is manipulated using controlled forces such that the user is not aware of the forces. Subjects can see the position of the haptic probe in a virtual environment where they are manipulated using sub-threshold forces. The multi-modal task is used to mea-sure the accuracy of subjects in two experiments. During the experiments, the effects of force intensity and the rel-ative direction of applied forces to the direction of user’s hand motion in the presence of visual cues are investigated. A performance index is also introduced that can be used to evaluate human performance in the application of sub-threshold forces. A psychophysical method is utilized to ensure that the applied forces on the user’s hand are below the force threshold of the human haptic system. Results indicate that user performance is affected by both the in-tensity and direction of sub-threshold forces even when the users could control their actions through visual feedbacks.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".