Effects of the Alignment Between a Haptic Device and Visual Display on the Perception of Object Softness
Bibliographic record
Abstract
Virtual reality (VR) has been gaining popularity in surgical planning and simulation. Most VR surgical simulation systems provide haptic (pertinent to the sense of touch) and visual information simultaneously using certain alignments between a haptic device and visual display. A critical aspect of such VR surgical systems is to represent both haptic and visual information accurately to avoid perceptual illusions (e.g., to distinguish the softness of organs/tissues). This study compared three different alignments (same-location alignment, vertical alignment, and horizontal alignment) between a haptic device and visual display that are widely used in VR systems. We conducted three experiments to study the influence of each alignment on the perception of object softness. In each experiment, we tested 15 different human subjects with varying availability of haptic and visual information. During each trial, the task of the subject was to discriminate object softness between two deformable balls in different viewing angles. We analyzed the following dependent measurements: subject perception of object softness and objective measurements of maximum force and maximum pressing depth. The analysis results reveal that all three alignments (independent variables) have similar effect on subjective perception of object softness within the interval of viewing angles from -7.5°to +7.5°. The viewing angle does not affect objective measurements. The same-location alignment requires less physical effort compared with the other two alignments. These observations have implications in creating accurate simulation and interaction for VR surgical systems.
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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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".