Journal of the Ergonomics Society of Korea (대한인간공학회지)

Ergonomics Society of Korea (대한인간공학회)
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Driving Performance Evaluation Using Bio-signals from the Prefrontal Lobe in the Driving Simulator

  • Kim, Young-Hyun (Department of Rehabilitation Science and Technology, Daegu University) ;
  • Kim, Yong-Chul (Department of Rehabilitation Science and Technology, Daegu University)
  • Received : 2011.11.09
  • Accepted : 2012.02.28
  • Published : 2012.04.30
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Abstract

Objective: The aim of this study was to develop the assistive device for accelerator and brake pedals using bio-signals from the prefrontal lobe in the driving simulator and evaluate its performance. Background: There is lack of assistive devices for the driving in peoples with disabilities in Korea. However, if bio-signals and/or brain waves are used at driving a car, the people with serious physical limitations can improve their community mobility. Method: 15 subjects with driver's license participated in this study for experiment of driving performance evaluation in the simulator. Each subject drove the simulator the same course 10 times in three separated groups which use different interface controllers to accelerate and brake: (1) conventional pedal group, (2) joystick group and (3) bio-signal group(horizontal quick glance of the eyes and clench teeth). All experiments were recorded and the driving performances were evaluated by three inspectors. Results: Average score of bio-signal group for the driving in the simulator was increased 3% compared with the pedal group and was increased 9% compared with the joystick group(p<0.01). The subjects using bio-signals was decreased 44% in number of deduction compared with others because the device had the built-in modified cruise control. Conclusion: The assistive device for accelerator and brake pedals using bio-signals showed significantly better performance than using general pedal and a joystick interface(p<0.01). Application: This study can be used to design adaptive vehicle for driving in people with disabilities.

Keywords

References

  1. Barea, R., Boquete, L. and Mazo, M., Lopez, E., System for assisted mobility using eye movements based on electrooculography, IEEE Transactions on Neural System and Rehabilitation Engineering, 10(4), 209-218, 2002. https://doi.org/10.1109/TNSRE.2002.806829
  2. Employment Development Institute, Survey on the Employment Status of the Disabled in Business, 2010, http://edi.kead.or.kr.
  3. Eoh, H. J., Chung, M. K. and Kim, S-H., Electroencephalographic study of drowsiness in simulated driving with sleep deprivation, International Journal of Industrial Ergonomics, 35(4), 307-320, 2005. https://doi.org/10.1016/j.ergon.2004.09.006
  4. Galan, F., Nuttin, M., Lew, E., Ferrez, P. W., Vanacker, G., Philips, J. and Millan, J. del R., A brain-actuated wheelchair: asynchronous and non-invasive brain-computer interfaces for continuous control of robots, Clinical Neurophysiology, 119(9), 2159-2169, 2008. https://doi.org/10.1016/j.clinph.2008.06.001
  5. Iturrate, I., Antelis J. M., Kubler, A. and Mingues, J., A noninvasive brainactuated wheelchair based on a P300 neurophysiological protocol and automated navigation, IEEE Transactions on Robotics, 25(3), 614-627, 2009. https://doi.org/10.1109/TRO.2009.2020347
  6. Jaime, G-G., Israel, S-J-G., Luis, F. N-A. and Alonso-Garcia Sergio, A-G., Steering a tractor by means of an EMG-based human-machine interface, Sensors, 11, 7110-7126, 2011. https://doi.org/10.3390/s110707110
  7. Kim, K-H., Kim, H. K., Kim, J-S., Son, W. and Lee, S-Y., A biosignalbased human interface controlling a power-wheelchair for people with motor disabilities, ETRI Journal, 28(1), 111-114, 2006. https://doi.org/10.4218/etrij.06.0205.0069
  8. Korea Institute for Health and Social Affairs, Survey of Disability 2005, http://www.kihasa.re.kr.
  9. Kwak, J., Jeon, T., Park, H., Kim, S. and An, K., Development of an EMG-based car interface using artificial neural networks for the physically handicapped, Korea Society of IT Service, 7(2), 149-164, 2008.
  10. Lee, D-Y., Rhee, K-M., Lee, D-Y., Lee, S-C., Lee, S-W., Lim, M-J. and Kim, K-M., A study on the conceptual design of cars accessible for persons with disabilities, The Journal of Special Education: Theory and Practice, 5(3), 139-159, 2004.
  11. Oonishi, Y., Oh, S. and Hori, Y., A new control method for power-assisted wheelchair based on the surface myoelectric signal, IEEE Transactions on Industrial Electronics, 57(9), 3191-3196, 2010. https://doi.org/10.1109/TIE.2010.2051931
  12. Park, S-S., Hu, H. and Lee, W-S., A study on physiological signal changes due to distraction in simulated driving, Journal of the Ergonomics Society of Korea, 29(1), 55-59, 2010. https://doi.org/10.5143/JESK.2010.29.1.055
  13. Pellerito, Jr., J. M., Driver rehabilitation and community mobility principle and practice, Elsevier Mosby, USA, 2006.
  14. Rebsamen, B., Teo, C.L., Zeng, Q., Ang Jr., M. H., Burdet, E., Guan, C., Zhang, H. and Laugier, C., Controlling a wheelchair indoors using thought, IEEE Intelligent Systems, 22(2), 18-23, 2007.
  15. Road Traffic Act, Article 54, Section 2 & Article 61, 2011, http://law.go.kr.
  16. Road Traffic Authority, Driver's License Examination Office, http://www.dla.go.kr.
  17. Statics Korea, Survey of Census Population, 2009, http://www.kostat.go.kr.
  18. Tanaka, K., Matsunaga, K. and Wang, H. O., Electroencephalogram-based control of an electric wheelchair, IEEE Transactions on Robotics, 21(4), 762-766, 2005. https://doi.org/10.1109/TRO.2004.842350
  19. Tsutomu, I., Selection of automobiles and assist devices for persons with physical disabilities, National Rehabilitation Center for Persons with Disabilities, Japan, 2004.
  20. Vanacker, G., Millan J. del R., Lew, E., Ferrez, P. W., Galan, F., Philips, J., Brussel, H. V. and Nuttin, M., Context-based filtering for assisted brain-actuated wheelchair driving, Computational Intelligence and Neuroscience, vol.2007, Article ID 25130, 12pages, 2007.
  21. Zhao, Q. B., Zhang, L. Q. and Cichocki, A., EEG-based asynchronous BCI control of a car in 3D virtual reality environments, Chinese Science Bulletin, 54(1), 78-87, 2009. https://doi.org/10.1007/s11434-008-0547-3
  22. Zhao, C., Zhao, M., Liu, J. and Zheng, C., Electroencephalogram and electrocardiograph assessment of mental fatigue in a driving simulator, Accident Analysis and Prevention, 45, 83-90, 2012. https://doi.org/10.1016/j.aap.2011.11.019

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