Toward Haptic Perception of Objects in a Visual and Depth Guided Navigation
Bibliographic record
Abstract
This paper investigates the limits of vibrato tactile haptic feedback when interacting with 3D virtual scenes. In this study, the spatial locations of the objects are mapped to the work-volume of the user using a Kinect sensor. In addition, the location of the hand of the user is determined using marker-based visual processing. The depth information is used to build a vibrotactile map on a finger of a haptic glove enhanced with vibrating actuators. The users can perceive the locations and dimensions of remote objects by moving their hand inside a scanning region. A marker detection imaging can provide the location and orientation of the user hand (glove). In order to map the corresponding tactile message. A user study was conducted to explore how different users can perceive such haptic experience. Factors like total number of detected objects, object separation spatial resolution, dimension-based and shape-based discrimination were evaluated. The preliminary results in a group of untrained users of different ages and backgrounds showed that the localization and counting of objects can be attained with a high degree of success. All users were able to classify groups of objects based on different dimensions (height, width, deep) and the perception of total volume. However, shape recognition proved to be a challenge for the majority of the users using the current configuration of the haptic glove.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".