Audio effects on haptics perception during drilling simulation
Bibliographic record
Abstract
Virtual reality has provided immersion and interactions through computer generated environments attempting to reproduce real life experiences through sensorial stimuli. Realism can be achieved through multimodal interactions which can enhance the user’s presence within the computer generated world. The most notorious advances in virtual reality can be seen in computer graphics visuals, where photorealism is the norm thriving to overcome the uncanny valley. Other advances have followed related to sound, haptics, and in a lesser manner smell and taste feedback. Currently, virtual reality systems (multimodal immersion and interactions through visual-haptic-sound) are being massively used in entertainment (e.g., cinema, video games, art), and in non-entertainment scenarios (e.g., social inclusion, educational, training, therapy, and tourism). Moreover, the cost reduction of virtual reality technologies has resulted in the availability at a consumer-level of various haptic, headsets, and motion tracking devices. Current consumer-level devices offer low-fidelity experiences due to the properties of the sensors, displays, and other electro-mechanical devices, that may not be suitable for high-precision or realistic experiences requiring dexterity. However, research has been conducted on how toovercome or compensate the lack of high fidelity to provide an engaging user experience using storytelling, multimodal interactions and gaming elements. Our work focuses on analyzing the possible effects of auditory perception on haptic feedback within a drilling scenario. Drilling involves multimodal interactions and it is a task with multiple applications in medicine, crafting, and construction. We compare two drilling scenarios were two groups of participants had to drill through wood while listening to contextual and non-contextual audios. We gathered their perception using a survey after the task completion. From the results, we believe that sound does influence the haptic perception, but further experiments are required to better comprehend the implications and possible medical applications.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".