Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 7189267, 20 pages
http://dx.doi.org/10.1155/2016/7189267
Research Article

A Driving Behaviour Model of Electrical Wheelchair Users

1F’SATI, Tshwane University of Technology, Private Bag Box X680, Staatsartillerie Road, Pretoria West, Pretoria 0001, South Africa
2Laboratoire d’Informatique Avancée de Saint de Denis (LIASD), University of Paris 8, France

Received 24 November 2015; Revised 9 March 2016; Accepted 20 March 2016

Academic Editor: Jianjun Ni

Copyright © 2016 S. O. Onyango et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. G. Eizmendi, J. M. Azkoitia, and G. M. Craddock, Challenges for Assistive Technology: AAATE 07, IOS Press, 2007.
  2. L. Fehr, W. E. Langbein, and S. B. Skaar, “Adequacy of power wheelchair control interfaces for persons with severe disabilities: a clinical survey,” Journal of Rehabilitation Research and Development, vol. 37, no. 3, pp. 353–360, 2000. View at Google Scholar · View at Scopus
  3. L. Wei, H. Hu, and Y. Zhang, “Fusing EMG and visual data for hands-free control of an intelligent wheelchair,” International Journal of Humanoid Robotics, vol. 8, no. 4, pp. 707–724, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. B. E. Dicianno, R. A. Cooper, and J. Coltellaro, “Joystick control for powered mobility: current state of technology and future directions,” Physical Medicine and Rehabilitation Clinics of North America, vol. 21, no. 1, pp. 79–86, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Trujillo-León and F. Vidal-Verdú, “Driving interface based on tactile sensors for electric wheelchairs or trolleys,” Sensors, vol. 14, no. 2, pp. 2644–2662, 2014. View at Publisher · View at Google Scholar · View at Scopus
  6. G. U. Sorrento, P. S. Archambault, F. Routhier, D. Dessureault, and P. Boissy, “Assessment of Joystick control during the performance of powered wheelchair driving tasks,” Journal of Neuroengineering and Rehabilitation, vol. 8, article 31, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Bauer, D. Wollherr, and M. Buss, “Human-robot collaboration: a survey,” International Journal of Humanoid Robotics, vol. 5, no. 1, pp. 47–66, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. J. S. Ju, Y. Shin, and E. Y. Kim, “Vision based interface system for hands free control of an intelligent wheelchair,” Journal of NeuroEngineering and Rehabilitation, vol. 6, article 33, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. G. Vanacker, J. D. R. Millán, E. Lew et al., “Context-based filtering for assisted brain-actuated wheelchair driving,” Computational Intelligence and Neuroscience, vol. 2007, Article ID 25130, 12 pages, 2007. View at Publisher · View at Google Scholar
  10. G. Diehm, S. Maier, M. Flad, and S. Hohmann, “An identification method for individual driver steering behaviour modelled by switched affine systems,” in Proceedings of the 52nd IEEE Conference on Decision and Control (CDC '13), pp. 3547–3553, Firenze, Italy, December 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. F. Chenier, P. Bigras, and R. Aissaoui, “An orientation estimator for the wheelchair's caster wheels,” IEEE Transactions on Control Systems Technology, vol. 19, no. 6, pp. 1317–1326, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. T. N. Nguyen, S. Su, and H. T. Nguyen, “Neural network based diagonal decoupling control of powered wheelchair systems,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 22, no. 2, pp. 371–378, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. R. J. K. Vegter, S. de Groot, C. J. Lamoth, D. H. Veeger, and L. H. V. van der Woude, “Initial skill acquisition of handrim wheelchair propulsion: a new perspective,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 22, no. 1, pp. 104–113, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. R. Becher, P. Steinhaus, and R. Dillmann, “The collaborative research center 588: ‘humanoid robots—learning and cooperating multimodal robots’,” International Journal of Humanoid Robotics, vol. 1, no. 3, pp. 429–448, 2004. View at Publisher · View at Google Scholar
  15. S. O. Onyango, Y. Hamam, and K. Djouani, “Velocity and orientation control in an electrical wheelchair on an inclined and slippery surface,” in Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics, pp. 112–119, IEEE, July 2011. View at Scopus
  16. S. O. Onyango, Y. Hamam, M. Dabo, K. Djouani, and G. Qi, “Dynamic control of an electrical wheelchair on an incline,” in Proceedings of the Conference in Africa (AFRICON '09), pp. 1–6, IEEE, Nairobi, Kenya, September 2009. View at Publisher · View at Google Scholar
  17. S. O. Onyango, Y. Hamam, K. Djouani, and G. Qi, “Dynamic control of powered wheelchair with slip on an incline,” in Proceedings of the 2nd International Conference on Adaptive Science and Technology (ICAST '09), pp. 278–283, IEEE, Accra, Ghana, December 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. H. Emam, Y. Hamam, E. Monacelli, and K. Djouani, “Power wheelchair driver behaviour modelling,” in Proceedings of the 7th International Multi-Conference on Systems, Signals and Devices (SSD '10), pp. 1–7, Amman, Jordan, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Hüntemann, E. Demeester, M. Nuttin, and H. Van Brussel, “Online user modeling with Gaussian Processes for Bayesian plan recognition during power-wheelchair steering,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 08), pp. 285–292, IEEE, Nice, France, September 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. E. Demeester, A. Hüntemann, D. Vanhooydonck et al., “Bayesian estimation of wheelchair driver intents: modeling intents as geometric paths tracked by the driver,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '06), pp. 5775–5780, Beijing, China, October 2006. View at Publisher · View at Google Scholar · View at Scopus
  21. E. Demeester, M. Nuttin, D. Vanhooydonck, and H. Van Brussel, “A model-based, probabilistic framework for plan recognition in shared wheelchair control: experiments and evaluation,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '03), vol. 2, pp. 1456–1461, IEEE, October 2003. View at Scopus
  22. G. Vanacker, D. Vanhooydonck, E. Demeester et al., “Adaptive filtering approach to improve wheelchair driving performance,” in Proceedings of the 15th IEEE International Symposium on Robot and Human Interactive Communication (ROMAN '06), pp. 527–532, IEEE, Hatfield, UK, September 2006. View at Publisher · View at Google Scholar
  23. S. O. Onyango, Y. Hamam, K. Djouani, and B. Daachi, “Identification of wheelchair user steering behaviour within indoor environments,” in Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO '15), IEEE, Zhuhai, China, December 2015.
  24. P. C. Cacciabue and O. Carsten, “A simple model of driver behaviour to sustain design and safety assessment of automated systems in automotive environments,” Applied Ergonomics, vol. 41, no. 2, pp. 187–197, 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. J. Boril, R. Jalovecky, and R. Ali, “Human-machine interaction and simulation models used in aviation,” in Proceedings of the 15th International Conference on Mechatronics (MECHATRONIKA '12), Prague, Czech Republic, December 2012. View at Scopus
  26. J.-H. Kim, S. Hayakawa, T. Suzuki et al., “Modeling of driver’s collision avoidance behavior based on piecewise linear model,” in Proceedings of the 43rd IEEE Conference on Decision and Control (CDC '04), vol. 3, pp. 2310–2315, December 2004. View at Publisher · View at Google Scholar
  27. S. D. Keen and D. J. Cole, “Bias-free identification of a linear model-predictive steering controller from measured driver steering behavior,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 42, no. 2, pp. 434–443, 2012. View at Publisher · View at Google Scholar · View at Scopus
  28. P. Angkititrakul, C. Miyajima, and K. Takeda, “Modeling and adaptation of stochastic driver-behavior model with application to car following,” in Proceedings of the IEEE Intelligent Vehicles Symposium (IV '11), pp. 814–819, IEEE, Baden-Baden, Germany, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  29. J. Engström and E. Hollnagel, “A general conceptual framework for modelling behavioural effects of driver support functions,” in Modelling Driver Behaviour in Automotive Environments, pp. 61–84, Springer, London, UK, 2007. View at Publisher · View at Google Scholar
  30. B. Peters and L. Nilsson, “Modelling the driver in control,” in Modelling Driver Behaviour in Automotive Environments, pp. 85–104, Springer, London, UK, 2007. View at Google Scholar
  31. O. Carsten, “From driver models to modelling the driver: what do we really need to know about the driver?” in Modelling Driver Behaviour in Automotive Environments, pp. 105–120, Springer, London, UK, 2007. View at Google Scholar
  32. K. Bengler, “Subject testing for evaluation of driver information systems and driver assistance systems learning effects and methodological solutions,” in Modelling Driver Behaviour in Automotive Environments, pp. 123–134, Springer US, 2007. View at Google Scholar
  33. M. Brackstone and M. McDonald, “Car-following: a historical review,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 2, no. 4, pp. 181–196, 1999. View at Publisher · View at Google Scholar · View at Scopus
  34. P. Ranjitkar, T. Nakatsuji, and M. Asano, “Performance evaluation of microscopic traffic flow models with test track data,” Transportation Research Record, vol. 1876, pp. 90–100, 2004. View at Google Scholar · View at Scopus
  35. J. L. Blanco, J. A. Fernandez-Madrigal, and J. Gonzalez, “A novel measure of uncertainty for mobile robot SLAM with rao blackwellized particle filters,” The International Journal of Robotics Research, vol. 27, no. 1, pp. 73–89, 2008. View at Google Scholar
  36. T. Okada, W. T. Botelho, and T. Shimizu, “Motion analysis with experimental verification of the hybrid robot PEOPLER-II for reversible switch between walk and roll on demand,” The International Journal of Robotics Research, vol. 29, no. 9, pp. 1199–1221, 2010. View at Publisher · View at Google Scholar · View at Scopus
  37. A. James McKnight and B. B. Adams, “Driver education task analysis. Volume I: task descriptions. Final report (August 1969–July 1970),” Tech. Rep., Human Resources Research Organization, Alexandria, Va, USA, 1970. View at Google Scholar
  38. T. A. Ranney, “Models of driving behavior: a review of their evolution,” Accident Analysis & Prevention, vol. 26, no. 6, pp. 733–750, 1994. View at Publisher · View at Google Scholar · View at Scopus
  39. J. A. Michon, “A critical view of driver behavior models: what do we know, what should we do?” in Human Behavior and Traffic Safety, L. Evans and R. C. Schwing, Eds., pp. 485–524, Springer, New York, NY, USA, 1985. View at Publisher · View at Google Scholar
  40. J. Rasmussen, Information Processing and Human-Machine Interaction: An Approach to Cognitive Engineering, North-Holland, New York, NY, USA, 1986.
  41. T. Pilutti and A. Galip Ulsoy, “Identification of driver state for lane-keeping tasks,” IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, vol. 29, no. 5, pp. 486–502, 1999. View at Publisher · View at Google Scholar · View at Scopus
  42. L.-K. Chen and A. G. Ulsoy, “Identification of a driver steering model, and model uncertainty, from driving simulator data,” Journal of Dynamic Systems, Measurement and Control, vol. 123, no. 4, pp. 623–629, 2001. View at Publisher · View at Google Scholar · View at Scopus
  43. N. Steyn, E. Monacelli, and Y. Hamam, “Differential Driven Mobility Aid,” Green Gazette, South Africa, 2013, https://www.greengazette.co.za/pages/patent-journal-9-of-25-september-2013-vol-46-part-2-of-2_20130925-PAT-00009-018.pdf.
  44. E. Masehian and D. Sedighizadeh, “Classic and heuristic approaches in robot motion planning—a chronological review,” World Academy of Science, Engineering and Technology, vol. 29, no. 5, pp. 101–106, 2007. View at Google Scholar
  45. T. Fraichard, “A short paper about motion safety,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '07), pp. 1140–1145, IEEE, Roma, Italy, April 2007. View at Publisher · View at Google Scholar · View at Scopus
  46. T. Kruse, A. K. Pandey, R. Alami, and A. Kirsch, “Human-aware robot navigation: a survey,” Robotics and Autonomous Systems, vol. 61, no. 12, pp. 1726–1743, 2013. View at Publisher · View at Google Scholar · View at Scopus
  47. O. Khatib, “Real-time obstacle avoidance for manipulators and mobile robots,” in Proceedings of the IEEE International Conference on Robotics and Automation, vol. 2, pp. 500–505, St. Louis, Mo, USA, March 1985. View at Publisher · View at Google Scholar
  48. S. S. Ge and Y. J. Cui, “New potential functions for mobile robot path planning,” IEEE Transactions on Robotics and Automation, vol. 16, no. 5, pp. 615–620, 2000. View at Publisher · View at Google Scholar · View at Scopus
  49. S. S. Ge and Y. J. Cui, “Dynamic motion planning for mobile robots using potential field method,” Autonomous Robots, vol. 13, no. 3, pp. 207–222, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  50. J.-C. Latombe, Robot Motion Planning, Kluwer Academic Publishers, 1991.
  51. F. Taychouri, Y. Hamam, E. Monacelli, and N. Chebbo, “Path planning for electrical wheelchair, accessibility and comfort for disabled people,” in Proceedings of the 6th EUROSIM Congress on Modelling and Simulation, pp. 9–13, Ljubljana, Slovenia, September 2007.
  52. R. Raja, A. Dutta, and K. S. Venkatesh, “New potential field method for rough terrain path planning using genetic algorithm for a 6-wheel rover,” Robotics and Autonomous Systems, vol. 72, pp. 295–306, 2015. View at Publisher · View at Google Scholar · View at Scopus
  53. O. Montiel, R. Sepúlveda, and U. Orozco-Rosas, “Optimal path planning generation for mobile robots using parallel evolutionary artificial potential field,” Journal of Intelligent & Robotic Systems, vol. 79, no. 2, pp. 237–257, 2014. View at Publisher · View at Google Scholar · View at Scopus
  54. F. Ahmed and K. Deb, “Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms,” Soft Computing, vol. 17, no. 7, pp. 1283–1299, 2013. View at Publisher · View at Google Scholar · View at Scopus
  55. P. Vadakkepat, K. C. Tan, and W. Ming-Liang, “Evolutionary artificial potential fields and their application in real time robot path planning,” in Proceedings of the Congress on Evolutionary Computation (CEC 00), vol. 1, pp. 256–263, July 2000. View at Scopus
  56. O. Montiel, U. Orozco-Rosas, and R. Sepúlveda, “Path planning for mobile robots using bacterial potential field for avoiding static and dynamic obstacles,” Expert Systems with Applications, vol. 42, no. 12, pp. 5177–5191, 2015. View at Publisher · View at Google Scholar · View at Scopus
  57. J. Barraquand, B. Langlois, and J.-C. Latombe, “Numerical potential field techniques for robot path planning,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 22, no. 2, pp. 224–241, 1992. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  58. T. Yong, W. Tianmiao, W. Hongxing, and C. Diansheng, “A behavior control method based on hierarchical POMDP for intelligent wheelchair,” in Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM '09), pp. 893–898, IEEE, Singapore, July 2009. View at Publisher · View at Google Scholar · View at Scopus
  59. T. Taha, J. V. Miró, and G. Dissanayake, “POMDP-based long-term user intention prediction for wheelchair navigation,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '08), pp. 3920–3925, IEEE, Pasadena, Calif, USA, May 2008. View at Publisher · View at Google Scholar · View at Scopus
  60. T. Taha, J. V. Miró, and G. Dissanayake, “Wheelchair driver assistance and intention prediction using POMDPs,” in Proceedings of the International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP '07), pp. 449–454, IEEE, Melbourne, Australia, December 2007. View at Publisher · View at Google Scholar · View at Scopus
  61. T. Carlson and Y. Demiris, “Collaborative control in human wheelchair interaction reduces the need for dexterity in precise manoeuvres,” in Proceedings of the “Robotic Helpers: User Interaction, Interfaces and Companions in Assistive and Therapy Robotics”, a Workshop at ACM/IEEE HRI, pp. 59–66, University of Hertfordshire, Hatfield, UK, 2008.
  62. T. Carlson and Y. Demiris, “Collaborative control for a robotic wheelchair: evaluation of performance, attention, and workload,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 42, no. 3, pp. 876–888, 2012. View at Publisher · View at Google Scholar · View at Scopus
  63. D. Vanhooydonck, E. Demeester, A. Hüntemann et al., “Adaptable navigational assistance for intelligent wheelchairs by means of an implicit personalized user model,” Robotics and Autonomous Systems, vol. 58, no. 8, pp. 963–977, 2010. View at Publisher · View at Google Scholar · View at Scopus
  64. E. Demeester, M. Nuttin, D. Vanhooydonck, and H. Van Brussel, “Fine motion planning for shared wheelchair control: requirements and preliminary experiments,” in Proceedings of the 11th International Conference on Advanced Robotics (ICAR '03), pp. 1278–1283, Coimbra, Portugal, June-July 2003.
  65. S. P. Parikh, V. Grassi Jr., V. Kumar, and J. Okamoto Jr., “Incorporating user inputs in motion planning for a smart wheelchair,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '04), vol. 2, pp. 2043–2048, IEEE, May 2004. View at Scopus
  66. S. P. Parikh, V. Grassi Jr., V. Kumar, and J. Okamoto Jr., “Integrating human inputs with autonomous behaviors on an intelligent wheelchair platform,” IEEE Intelligent Systems, vol. 22, no. 2, pp. 33–41, 2007. View at Publisher · View at Google Scholar · View at Scopus
  67. Q. Li, W. Chen, and J. Wang, “Dynamic shared control for human-wheelchair cooperation,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '11), pp. 4278–4283, Shanghai, China, May 2011. View at Publisher · View at Google Scholar · View at Scopus
  68. C. Urdiales, E. J. Perez, G. Peinado et al., “On the construction of a skill-based wheelchair navigation profile,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 21, no. 6, pp. 917–927, 2013. View at Publisher · View at Google Scholar · View at Scopus
  69. S. Simon Levine, D. David Bell, L. Lincoln Jaros, R. Richard Simpson, Y. Koren, and J. Borenstein, “The NavChair assistive wheelchair navigation system,” IEEE Transactions on Rehabilitation Engineering, vol. 7, no. 4, pp. 443–451, 1999. View at Publisher · View at Google Scholar · View at Scopus
  70. L. Montesano, M. Díaz, S. Bhaskar, and J. Minguez, “Towards an intelligent wheelchair system for users with cerebral palsy,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 18, no. 2, pp. 193–202, 2010. View at Publisher · View at Google Scholar · View at Scopus
  71. N. Steyn, Virtual reality platform modelling and design for versatile electric wheelchair simulation in an enabled environment [Ph.D. thesis], Tshwane University of Technology, 2014.
  72. H. Emam, Y. Hamam, E. Monacelli, and I. Mougharbel, “Dynamic model of an electrical wheelchair with slipping detection,” in Proceedings of the 6th EUROSIM Congress on Modelling and Simulation (EUROSIM '07), pp. 9–13, EUROSIM/SLOSIM, Ljubljana, Slovenia, September 2007.
  73. Y. Hori, Y. Toyoda, and Y. Tsuruoka, “Traction control of electric vehicle: basic experimental results using the test EV UOT electric march,” IEEE Transactions on Industry Applications, vol. 34, no. 5, pp. 1131–1138, 1998. View at Publisher · View at Google Scholar · View at Scopus
  74. A. N. Beare and R. E. Dorris, “A simulator-based study of human errors in nuclear power plant control room tasks,” Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 27, no. 2, pp. 170–174, 1983. View at Google Scholar
  75. D. Dennett, The Intentional Stance, MIT Press, Cambridge, Mass, USA, 1989.
  76. D. H. Taylor, “Drivers' galvanic skin response and the risk of accident,” Ergonomics, vol. 7, no. 4, pp. 439–451, 1964. View at Publisher · View at Google Scholar