Development of an Immersive Virtual Reality Application for Road Crossing Training for Older People
Bibliographic record
Abstract
Background: In old age, both physical and cognitive limitations can arise, impacting the mobili-ty of older adults in daily life. Virtual reality (VR) applications offer innovative opportunities for senior citizens to enhance their functional abilities. Everyday activities, such as crossing a street, can be simulated and practiced in a virtual environment. Objective: This study explores the use of virtual reality (VR) as an innovative training tool to help senior citizens navigate everyday challenges, such as crossing roads, more safely. VR pro-vides an immersive environment where users can simulate realistic traffic scenarios. This tech-nology allows participants to practice in a safe and controlled setting without exposure to the risks of real-world road traffic. Method: A VR training application called "Wegfest" was developed to facilitate targeted road-crossing practice. The application simulates various scenarios commonly encountered by older adults, such as crossing busy streets or waiting at traffic lights. Virtual reality enables users to train their reaction times and decision-making abilities through interactive exercises. Visual and auditory feedback mechanisms are integrated to help users identify potential hazards early and respond appropriately. Results: The development process of "Wegfest" demonstrates how a highly realistic street envi-ronment can be created for VR-based road-crossing training. In addition to replicating real-world road-crossing scenes, contemporary environmental factors (e.g., related to electromobility) and auditory stimuli were incorporated into the design. The Timed Up and Go (TUG) test revealed a significant improvement (p = 0.002) with a large effect size (Cohen's d = 0.784). The Falls Efficacy Scale-International Version (FES-I) indicated a significant increase in fall-related self-efficacy (p = 0.005). No significant change was ob-served in Montreal Cognitive Assessment (MoCA) scores (p = 0.56). Participants reported a sig-nificant improvement in their subjective perception of road-crossing safety following the inter-vention (p < 0.001). Discussion: The development of the VR training application "Wegfest" highlights the feasibility of creating realistic virtual environments for skill development. By leveraging immersive tech-nology, both physical and cognitive skills required for road-crossing can be effectively trained. The findings suggest that "Wegfest" has the potential to enhance the mobility and safety of older adults in road traffic through immersive experiences and targeted training interventions. Conclusion: As an innovative training tool, the VR application not only provides an engaging and enjoyable learning environment but also fosters self-confidence and independence among older adults in traffic settings. Regular training within the virtual world enables senior citizens to continuously refine their skills, ultimately improving their quality of life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".