JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Driving Performance Evaluation Using Bio-signals from the Prefrontal Lobe in the Driving Simulator
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Driving Performance Evaluation Using Bio-signals from the Prefrontal Lobe in the Driving Simulator
Kim, Young-Hyun; Kim, Yong-Chul;
  PDF(new window)
 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
Bio-signal;EEG sensor;Driving simulator;Human machine interface(HMI);
 Language
English
 Cited by
1.
Development of Smart Driving System Using iPod and Its Performance Evaluation for People with Severe Physical Disabilities in the Driving Simulator,;;

Journal of the Ergonomics Society of Korea, 2012. vol.31. 5, pp.637-646 crossref(new window)
1.
Development of Smart Driving System Using iPod and Its Performance Evaluation for People with Severe Physical Disabilities in the Driving Simulator, Journal of the Ergonomics Society of Korea, 2012, 31, 5, 637  crossref(new windwow)
2.
Driving Performance of Adaptive Driving Controls using Drive-by-Wire Technology for People with Disabilities, Journal of the Ergonomics Society of Korea, 2016, 35, 1, 11  crossref(new windwow)
 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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)