Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-26T10:13:54.385Z Has data issue: false hasContentIssue false
  • English
  • Français

Using Simulation to Better Understand the Effects of Aging on Driver Visibility

Published online by Cambridge University Press:  12 April 2016

Tara Kajaks ,
Brenda Vrkljan ,
Joy MacDermid  and
Allison Godwin
Tara Kajaks*
Affiliation:
School of Kinesiology, McMaster University Toronto Rehabilitation Institute
Brenda Vrkljan
Affiliation:
School of Rehabilitation Science, McMaster University
Joy MacDermid
Affiliation:
School of Rehabilitation Science, McMaster University Hand and Upper Limb Centre Clinical Research Lab, St. Joseph’s Health Centre
Allison Godwin
Affiliation:
Human Kinetics, Laurentian University
*
†La correspondance et les demandes de tirés-à-part doivent être adressées à: / Correspondence and requests for offprints should be sent to: Tara Kajaks, M.Sc. Department of Kinesiology McMaster University 1280 Main Street West Hamilton, ON L8S 4L8 ([email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

This proof-of-concept pilot study explored virtual simulation methodology to quantify blind-spot line-of-sight using avatars derived from an older driver database (n = 100). Siemens Jack software simulated the blind spots of eight older driver avatars (four female). The male and female avatars were scaled to be small (25th percentile) and large (75th percentile) based on the height distribution for the older driver database, and had either “normal” (65 degrees) or “abnormal” (50 degrees) neck range of motion (ROM). A virtual model of a Volkswagen Beetle was used to illustrate left and right blind-spot line-of-sight for each avatar. Average line-of-sight between blind spots was 22.3 per cent and 10.4 per cent in the “normal” and “abnormal” rotational neck ROM conditions, respectively. Older drivers with functional impairments affecting neck ROM are more likely to have problems with left blind-spot line-of-sight. Findings are discussed with regard to vehicle design considerations for older adults.

Résumé

Cette étude de preuve de concept pilote a exploré une méthodologie utilisant la simulation virtuelle pour quantifier les angles non visibles des visibilités optiques, et en utilisant des avatars tirés d’une ancienne base de données (n = 100). Les logiciels Siemens Jack ont simulé les angles morts de huit avatars des conducteurs âgés (quatre femmes). Les avatars masculins et féminins ont été mis à l’échelle aux petites tailles (25e centile) et aux grandes tailles (75e centile), basé sur la distribution de la hauteur de la base de données des conducteurs âgés, et ils avaient l’amplitude de mouvement du cou “normal” (65 degrés) ou “anormal” 50 degrés (ROM). Un modèle virtuel d’une Volkswagen Beetle a été utilisé pour illustrer les angles morts lignes de visée à gauche et à droit pour chaque avatar. La moyenne ligne de visée entre les angles morts était de 22,3 pourcent et 10,4 pourcent dans les conditions «normales» et «anormales» de rotation du cou (ROM), respectivement. Les conducteurs âgés ayant des troubles fonctionnels affectant le cou (ROM) sont plus susceptibles d’avoir des problèmes avec l’angle mort / ligne de visée gauche. Les résultats sont discutés comme ils se rapportent à des considerations du dessein des véhicules pour les personnes agées.

Keywords

vieillissmentdétection des angles mortsmodèles humains numériquesligne de viséeconducteurs âgéssimulationagingblind spot detectiondigital human modelsline-of-sightolder driverssimulation
Type
Articles
Information
Canadian Journal on Aging / La Revue canadienne du vieillissement , Volume 35 , Special Issue S1: Older Driver Research: Evidence to Guide Change / Recherche sur la conduite automobile des personnes âgées : des preuves afin de guider le changement , June 2016 , pp. 110 - 116
Check for updates
Copyright
Copyright © Canadian Association on Gerontology 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anstey, K. J., & Wood, J. M. (2011). Chronological age and age-related cognitive deficits are associated with an increase in multiple types of driving errors in late life. Neuropsychology, 25(5), 613–621.Google Scholar
Butler-Jones, D. (2010). The Chief Public Health Officer’s report on the state of public health in Canada: Growing older – Adding life to years. Ottawa, ON: Public Health Agency of Canada.Google Scholar
Canadian Automotive Association (2012). Seniors Driving. Retrieved from http://seniorsdriving.caa.ca (Accessed on January 15, 2014.)Google Scholar
Charlton, J., Fildes, B. N., & Andrea, D. (2002). Vehicle safety and older occupants. Gerontechnology, 1(4), 274–286.CrossRefGoogle Scholar
Eby, D., & Molnar, L. (2012). Has the time come for an older driver vehicle?. Ann Arbor, MI: The University of Michigan Transportation Research Institute. Report No. UMTRI-2012-5.Google Scholar
Eger, T. R., Godwin, A. A., Henry, D. J., Grenier, S. G., Callaghan, J., & Demerchant, A. (2010). Why vehicle design matters: Exploring the link between line-of-sight, driving posture and risk factors for injury. Work, 35, 27–37.Google Scholar
Friedman, C., McGwin, G., Ball, K. K., & Owsley, C. (2013). Association between higher order visual processing abilities and a history of motor vehicle collision involvement by drivers ages 70 and over. Investigative Ophthalmology & Visual Science, 54(1), 778–782.Google Scholar
Godwin, A. A., & Eger, T. R. (2009). Using virtual computer analysis to evaluate the potential use of camera intervention on industrial machines with line-of-sight impairments. International Journal of Industrial Ergonomics, 39, 146–151.Google Scholar
Godwin, A. A., Eger, T. R., Salmoni, A. W., & Dunn, P. G. (2008). Virtual design modifications yield line-of-sight improvements for LHD operators. International Journal of Industrial Ergonomics, 38(2), 202–210.CrossRefGoogle Scholar
Godwin, A. A., Eger, T. R., Corrigan, L., & Grenier, S. G. (2010). Classic JACK modelling of driver posture and line-of-sight for operators of lift-trucks. International Journal of Human Factors Modelling and Simulation, 1(3), 259–270.CrossRefGoogle Scholar
Herriotts, P. (2005). Identification of vehicle design requirements for older drivers. Applied Ergonomics, 36, 255–262.CrossRefGoogle ScholarPubMed
Jeffkins, A., Eger, T., Salmoni, A., & Whissell, R. (2004). Virtual JACK in a virtual machine: Computer simulation is beneficial for investigating visibility during mobile mining equipment operation. Ergonomics in Design, 12(2), 12–17.CrossRefGoogle Scholar
Kajaks, T., Stephens, A., & Potvin, J. (2011). The effect of manikin anthropometrics and posture guidelines on proactive ergonomic assessments using digital human models. International Journal of Human Factors Modelling and Simulation, 2(3), 236.CrossRefGoogle Scholar
Marshall, S. C., Man-Son-Hing, M., Bédard, M., Charlton, J., Gagnon, S., Gélinas, I., et al. (2013). Protocol for Candrive II/Ozcandrive, a multicentre prospective older driver cohort study. Accident Analysis and Prevention, 61, 245–252. doi: 10.1016/j.aap.2013.02.009 Google Scholar
Park, J., Choi, Y., Lee, B., Jung, K., Sah, S., You, H. (2014). A classification of sitting strategies based on driving posture analysis. Journal of the Ergonomics Society of Korea, 33(2), 87–96.Google Scholar
Reed, M. P., Manary, M. A., Flannagan, C. A. C., & Schneider, L. W. (2002). A statistical method for predicting automobile driving posture. Human Factors, 44(4), 557–568.Google Scholar
Sabbah, A., Zaindl, A., & Bubb, H. (2009). Design of a mock-up for supported ingress/egress using a DHM, SAE Technical Paper 2009-01-2268. doi: 10.4271/2009-01-2268 Google Scholar
Stutts, J., Martell, C., & Staplin, L. (2009). Identifying behaviours and situations associated with increased crash risk for older drivers (DOT HS 811 093). Retrieved from www.nhtsa.gov/DOT/NHTSA/.../Associated%20Files/811093.pdf. (Accessed on January 15, 2014.)Google Scholar
Turcotte, M. (2006). Seniors’ access to transportation. In Canadian Social Trends (Catalogue number 11–008). Ottawa: ON: Statistics Canada. Retrieved from http://www.statcan.gc.ca/pub/11-008-x/2006005/pdf/9528-eng.pdf Google Scholar
Turcotte, M. (2012). Profile of seniors’ transportation habits. In Canadian Social Trends (Catalogue number 11–008). Ottawa, ON: Statistics Canada. Retrieved from http://www.statcan.gc.ca/pub/11-008-x/2012001/article/11619-eng.pdf Google Scholar
Tyson, J. (1997, October). To see or not to see … that is the question! Ergonomics Australia, 11(5). Retrieved from http://ergonomics.uq.edu.au/eaol/oct97tyson.pdf (Accessed on January 15, 2014.)Google Scholar
West, S. K., Hahn, D. V., Baldwin, K. C., Duncan, D. D., Munoz, B. E., Turano, K. A., et al. (2010). Older drivers and failure to stop at red lights. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 65(2), 179–183.Google Scholar
Youdas, J. W., Garrett, T. R., Suman, V. J., Bogard, C. L., Hallman, H. O., & Carey, J. R. (1992). Normal range of motion of the cervical spine: An initial goniometric study. Physical Therapy, 72(11), 770–780.Google Scholar
Zhan, J., Porter, M. M., Polgar, J., & Vrkljan, B. (2013). Older drivers’ opinions of criteria that inform the cars they buy: a focus group study. Accident Analysis & Prevention, 61, 281–287.Google Scholar