Evaluation of Older Driver Functional Range of Motion using Virtual Reality
Bibliographic record
Abstract
The number of drivers over 65 years of age is increasing (Siren & Haustein, 2013; Sivak & Schoettle, 2012). Safe operation of a motor vehicle requires good vision, cognition, and motor function. Changes in these are part of the normal aging process (Anstey, Wood, Lord, & Walker, 2005; Desapriya et al., 2011). Reduced neck rotation range of motion (ROM) is associated with doubling crash risk (Isler, Parsonson, & Hansson, 1997; Marottoli et al., 1998). Trunk movement in driving is another consideration (Ashman, Bishu, Foster, & McCoy, 1994; Caragata, Tuokko, & Damini, 2009; Marottoli et al., 2007; Ostrow, Shaffron, & McPherson, 1992). This study used cost-effective immersive virtual reality (VR) technology to examine driver performance. The objective was to explore the functional rotation movement (e.g. overall rotation) of younger and older drivers during a blind spot checking task in VR containing moving virtual cars to represent a dynamic driving situation. The VR system included a steering wheel and pedal set (Logitech, CA), and a head-mounted display (Oculus VR, CA) for visual feedback and head rotational movement tracking. An active-marker infrared motion tracking system (Optotrak Certus System, NDI, Canada) tracked trunk motion for evaluating the contribution of truck movements to the overall ROM. Fourteen younger (8 female) and 12 older (7 female) healthy drivers with a valid driver license and normal vision were recruited from the local community under informed consent approved by the New England Institutional Review Board. The task was to perform normal blind spot checking movements while driving. The average functional ROM and baseline neck ROM were 101.6° and 78.1° for younger drivers (age 18 to 35 years), and 71.9° and 63.5° for older drivers (age >65 years), respectively. Drivers on average turned 15.6° more when checking the blind spot than the baseline neck rotation ( F(1,24)=41.68, p<.001). Younger drivers on average turned 16.3° more than the older drivers ( F(1,24)=51.61, p<.001). There was a statistically significant interaction between situation (baseline and blind spot checking) and driver group ( F(1,24)=9.99, p=.004). Both driver groups engaged neck and trunk movements while checked blind spots, which differed from simple neck or trunk axial rotation. Since we found that the functional ROM was considerably greater than baseline neck ROM it suggests that drivers during a blind spot checking task move beyond typical neck ROM. This study demonstrated the potential of using readily available off-the- shelf VR for driver performance assessment. The results suggest that functional ROM, in addition to baseline ROM, should be considered when evaluating individual driving performance. The next step is to study if functional ROM measured using VR can be utilized for screening driving risks in aging.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".