Tactile Feedback Within Virtual Reality Training: An Immersive Study for Motorcycle Training
Bibliographic record
Abstract
Over 750 million motorcycles are estimated to be on roads, of which 380,000 annual deaths are of motorcyclists.Motorcyclists are affiliated with higher fatality rates than drivers as motorcycles are more physically demanding due to their innate design.Unlike most cars today, motorcycles are manual and require its rider to manage its controls intuitively.This is where training can be seen as vital to the safety of the rider.Although motorcycle training programs (MTPs) exist, these programs are seen to be short and do not allow their trainees enough time to practice.As virtual reality (VR) is being used to train professionals in numerous industries, it may be a potential training tool for novice trainees.The research investigates VR as a possible supplementary tool for learning the operations of operating a motorcycle.Specific levels of tactile feedback have been seen to increase performance of specific skill in VR, although research on the effects of tactile feedback within the domain of motorcycle training and VR have not yet been investigated.To investigate the addition of tactile feedback on operator performance in VR, three groups of participants were trained on three different VR simulators with each differing in levels of tactile feedback.The results show that low levels of tactile feedback contribute to better performance in motorcycle training, while higher levels of tactile feedback increase immersiveness, encouraging participants to treat the virtual reality simulation more as a training tool and less as a game.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".