ThermoGrasp: Enabling Localized Thermal Feedback on Fingers for Precision Grasps in Virtual Reality
Bibliographic record
Abstract
The increasing interest in thermal haptic feedback devices, particularly for virtual reality (VR) applications, highlights the need for more immersive user experiences. However, replicating precise thermal sensations on the fingers remains challenging due to the complexity of finger joints and movements. In this paper, we introduce ThermoGrasp, a novel thermal display designed to enhance VR experiences by providing realistic thermal feedback during precision object grasping. ThermoGrasp is a modular wearable device that targets controlled thermal feedback on the distal phalanges. The implications of designing its VR application were assessed through two experimental studies. The first study focused on the device's ability to accurately convey thermal sensations across different fingers during various precision grasps. The second study investigated the overall haptic experience in VR, examining the impact of thermal feedback on user immersion and realism during interactions with objects of varying temperatures. Participants' subjective responses were analyzed based on factors such as autotelicity, expressiveness, immersion, realism, and harmony. The findings indicate that precise, localized thermal feedback significantly enhances the VR experience, offering a marked improvement over traditional haptic feedback methods.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".