Usability Evaluation and Satisfaction S urvey of a Visual A ssistive D evice for Preventing Pedal Confusion in Middle-Aged and Older Drivers
Bibliographic record
Abstract
Objective : This study aims to evaluate the usability and satisfaction of a visual driving assistive device developed to prevent pedal confusion among middle-aged and older drivers and to verify its effectiveness in promoting a safer driving environment. Methods : From July to October 2024, usability and satisfaction evaluations were conducted with 15 middle- aged and older drivers and 15 occupational therapists. Participants assessed the device's usefulness, convenience, and intuitiveness through surveys, and provided suggestions for improvement. The System Usability Scale (SUS) was used for usability evaluation, while satisfaction was assessed using the Quebec User Evaluation of Satisfaction with assistive Technology (QUEST). Results : The results showed that the visual driving assistive device effectively reduced pedal confusion and improved drivers’ psychological stability and confidence. The usability evaluation revealed a high average SUS score of 85, indicating strong usability. The satisfaction survey also showed positive evaluations regarding the device's usefulness and effectiveness in the QUEST items. Conclusion : This study confirmed that the visual driving assistive device enhances the safety of older drivers and effectively prevents pedal confusion during driving. Future studies should focus on incorporating user feedback for functional improvements and verifying the device’s long-term effectiveness through real-world driving scenarios.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".