Design of a Tactile Instrument to Measure Human Roughness Perception in a Virtual Environment
Bibliographic record
Abstract
This paper presents the experimental results on the measurement of human texture perception in virtual environments. The experiment is conducted with a haptic tactile instrument that provides sensations of rough textures directly to the fingertip of the users. It consists of a brush and a DC motor. The brush rubs directly against the user's fingertip. Simulated texture is felt through an aperture on the tactile actuator where the users place their fingertip. The speed and direction of the brush are varied to control the roughness of the virtual surface and to determine the effect of either variable on perceived roughness. The actuator is designed to be attached to an existing force feedback device in order to create an interface that can provide force feedback and tactile feedback. The magnitudes of rough textures are measured through this device by comparing the virtual textures with real sandpapers of different grit sizes. Through human factor testing, it is found that the direction of rotation has negligible effects on roughness perception when the time gap between two consecutive stimuli is as large as 10 s. However, when the time gap is reduced to 0.5 s, the effects of direction become prominent. The just noticeable difference with respect to speed is found to decrease as the base speed of the brush increases. The results also show that although each subject's perception of roughness is biased using various sandpapers, the measured data is divided between two trends. One group of users perceives the roughness to increase with increasing speed, while the other group perceives the roughness to decrease.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".