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Journal of Healthcare Engineering
Volume 2018, Article ID 6083565, 20 pages
https://doi.org/10.1155/2018/6083565
Research Article

Intelligent Control Wheelchair Using a New Visual Joystick

Laboratoire SIME, Ecole Nationale Supérieure d’Ingénieurs de Tunis (ENSIT), Université de Tunis, 5 Av. Taha Hussein, 1008 Tunis, Tunisia

Correspondence should be addressed to Yassine Rabhi; moc.liamy@ihbarenissay

Received 5 August 2017; Revised 7 November 2017; Accepted 27 November 2017; Published 7 February 2018

Academic Editor: Maria Lindén

Copyright © 2018 Yassine Rabhi 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. World report on disability: World Health Organization, 2011.
  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
  3. L. Wei, H. Hu, and K. Yuan, “Use of forehead bio-signals for controlling an intelligent wheelchair,” in 2008 IEEE International Conference on Robotics and Biomimetics, pp. 108–113, Bangkok, Thailand, February 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. F. A. Kondori, S. Yousefi, L. Liu, and H. Li, “Head operated electric wheelchair,” in 2014 IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 53–56, San Diego, CA, USA, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. Z. Fang Hu, L. Li, Y. Luo, Y. Zhang, and X. Wei, “A novel intelligent wheelchair control approach based on head gesture recognition,” in 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), vol. 6, pp. V6-159–V6-163, Taiyuan, China, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. P. Jia, H. Hu, T. Lu, and K. Yuan, “Head gesture recognition for hands-free control of an intelligent wheelchair,” Industrial Robot: An International Journal, vol. 34, no. 1, pp. 60–68, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. L. King, H. Nguyen, and P. Taylor, “Hands-free head-movement gesture recognition using artificial neural networks and the magnified gradient function,” in 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 2063–2066, Shanghai, China, 2005. View at Publisher · View at Google Scholar
  8. G. Pacnike, K. Benkic, and B. Brecko, “Voice operated intelligent wheelchair - VOIC,” in Proceedings of the IEEE International Symposium on Industrial Electronics, 2005. ISIE 2005, pp. 1121–1126, Dubrovnik, Croatia, Croatia, 2005. View at Publisher · View at Google Scholar · View at Scopus
  9. G. Pires and U. Nunes, “A wheelchair steered through voice commands and assisted a reactive fuzzy-logic controller,” Journal of Intelligent and Robotic Systems, vol. 34, no. 3, pp. 301–314, 2002. View at Publisher · View at Google Scholar · View at Scopus
  10. R. C. Simpson and S. P. Levine, “Adaptive shared control of a smart wheelchair operated by voice control,” in Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97, pp. 622–626, Grenoble, France, France, 1997. View at Publisher · View at Google Scholar
  11. I. Mougharbel, R. El-Hajj, H. Ghamlouch, and E. Monacelli, “Comparative study on different adaptation approaches concerning a sip and puff controller for a powered wheelchair,” in 2013 Science and Information Conference, pp. 597–603, London, UK, 2013.
  12. M. Jones, K. Grogg, J. Anschutz, and R. A. Fierman, “Sip-and-puff wireless remote control for the Apple iPod,” Assistive Technology, vol. 20, no. 2, pp. 107–110, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. Q. X. Nguyen and S. Jo, “Electric wheelchair control using head pose free eye-gaze tracker,” Electronics Letters, vol. 48, no. 13, p. 750, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. H. A. Lamti, M. M. Ben Khelifa, P. Gorce, and A. M. Alimi, “Brain and gaze-controlled wheelchair,” Computer Methods in Biomechanics and Biomedical Engineering, vol. 16, Supplement 1, pp. 128-129, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. Georgia Institute of Technology, GT | News Center - Tongue Drive Wheelchair, Georgia Institute of Technology, 2013.
  16. Z. Fang Hu, L. Li, Y. Luo, Y. Zhang, and X. Wei, “A novel intelligent wheelchair control approach based on head gesture recognition,” in 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), vol. 6, pp. V6-159–V6-163, Taiyuan, China, 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. G. McAllister, S. McKenna, and I. Ricketts, “Hand tracking for behaviour understanding,” Image and Vision Computing, vol. 20, no. 12, pp. 827–840, 2002. View at Publisher · View at Google Scholar · View at Scopus
  18. K. Oka, Y. Sato, and H. Koike, “Real-time tracking of multiple fingertips and gesture recognition for augmented desk interface systems,” in Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition, pp. 429–434, Washington, DC, USA, 2002. View at Publisher · View at Google Scholar · View at Scopus
  19. I. Laptev and T. Lindeberg, “Tracking of multi-state hand models using particle filtering and a hierarchy of multi-scale image features,” in Proceedings of IEEE Workshop on Scale-Space and Morphology, 2001.
  20. S. Lu, D. Metaxas, D. Samaras, and J. Oliensis, “Using multiple cues for hand tracking and model refinement,” in 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings, pp. II-443–II-450, Madison, WI, USA, 2003. View at Publisher · View at Google Scholar
  21. E. B. Sudderth, M. I. Mandel, W. T. Freeman, and A. S. Willsky, “Visual hand tracking using nonparametric belief propagation,” in 2004 Conference on Computer Vision and Pattern Recognition Workshop, Washington, DC, USA, 2004. View at Publisher · View at Google Scholar · View at Scopus
  22. M. Kolsch and M. Turk, “Fast 2D hand tracking with flocks of features and multi-cue integration,” in 2004 Conference on Computer Vision and Pattern Recognition Workshop, Washington, DC, USA, 2004. View at Publisher · View at Google Scholar · View at Scopus
  23. W. Y. Chang, C. S. Chen, and Y. P. Hung, “Appearance-guided particle filtering for articulated hand tracking,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), San Diego, CA, USA, 2005. View at Publisher · View at Google Scholar · View at Scopus
  24. B. Stenger, A. Thayananthan, P. H. S. Torr, and R. Cipolla, “Model-based hand tracking using a hierarchical Bayesian filter,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 9, pp. 1372–1384, 2006. View at Publisher · View at Google Scholar · View at Scopus
  25. M. Isard and A. Blake, “CONDENSATION—conditional density propagation for visual tracking,” International Journal of Computer Vision, vol. 29, no. 1, pp. 5–28, 1998. View at Publisher · View at Google Scholar
  26. M. Isard and A. Blake, “Icondensation: unifying low-level tracking in a stochastic framework,” in Proceedings of European Conference on Computer Vision (ECCV’98), vol. 1, pp. 893–908, Freiburg, Germany, 1998. View at Publisher · View at Google Scholar · View at Scopus
  27. “Vision based hand gesture recognition systems,” http://archive.is/ke010.
  28. M. Bray, E. Koller-Meier, and L. Van Gool, “Smart particle filtering for 3D hand tracking,” in Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings, pp. 675–680, Seoul, South Korea, 2004. View at Publisher · View at Google Scholar · View at Scopus
  29. K. B. Shaika, P. Ganesan, V. Kalist, B. S. Sathish, and J. M. M. Jenitha, “Comparative study of skin color detection and segmentation in HSV and YCbCr color space,” Procedia Computer Science, vol. 57, pp. 41–48, 2015. View at Publisher · View at Google Scholar · View at Scopus
  30. E. Stergiopoulou, K. Sgouropoulos, N. Nikolaou, N. Papamarkos, and N. Mitianoudis, “Real time hand detection in a complex background,” Engineering Applications of Artificial Intelligence, vol. 35, pp. 54–70, 2014. View at Publisher · View at Google Scholar · View at Scopus
  31. H. Sugano and R. Miyamoto, “Parallel implementation of morphological processing on cell/BE with OpenCV interface,” in 2008 3rd International Symposium on Communications, Control and Signal Processing, pp. 578–583, St. Julian’s, Malta, March 2008. View at Publisher · View at Google Scholar · View at Scopus
  32. X. Sun, H. Yao, and S. Zhang, “A refined particle filter method for contour tracking,” in Visual Communications and Image Processing 2010, pp. 1–8, 2010. View at Publisher · View at Google Scholar · View at Scopus
  33. D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-based object tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp. 564–577, 2003. View at Publisher · View at Google Scholar · View at Scopus
  34. M. AL-Rousan and K. Assaleh, “A wavelet and neural network-based voice system for a smart wheelchair control,” Journal of the Franklin Institute, vol. 348, pp. 90–100, 2009. View at Publisher · View at Google Scholar · View at Scopus
  35. Y. Rabhi, M. Mrabet, F. Fnaiech, and P. Gorce, “A feedforward neural network wheelchair-driving joystick,” in 2013 International Conference on Electrical Engineering and Software Applications, pp. 1–6, Hammamet, Tunisia, 2013. View at Publisher · View at Google Scholar · View at Scopus
  36. F. Rose, B. Brooks, and A. Rizo, “Virtual reality in brain damage rehabilitation: review,” Cyberpsychology and Behaviour, vol. 8, no. 3, pp. 241–262, 2005. View at Publisher · View at Google Scholar · View at Scopus
  37. T. Pithona, T. Weissb, S. Richira, and E. Klingera, “Wheelchair simulators: a review,” Technology and Disability, vol. 21, no. 1-2, pp. 1–10, 2009. View at Publisher · View at Google Scholar · View at Scopus
  38. B. M. Faria, L. P. Reis, and N. Lau, “A survey on intelligent wheelchair prototypes and simulators,” in New Perspectives in Information Systems and Technologies, Volume 1, vol. 275 of AISC series, pp. 545–557, Springer, Cham, 2014. View at Publisher · View at Google Scholar · View at Scopus
  39. E. Keshner, “Virtual reality and physical rehabilitation: a new toy or a new research and rehabilitation tool?” Journal of Neuroengineering and Rehabilitation, vol. 1, no. 1, p. 8, 2004. View at Publisher · View at Google Scholar · View at Scopus
  40. M. C. F. Lacquaniti, “Virtual reality: a tutorial,” Electroencephalography and Clinical Neurophysiology/Electromyography and Motor Control, vol. 109, no. 1, pp. 1–9, 1998. View at Publisher · View at Google Scholar · View at Scopus
  41. N. Takenobu and I. Hafid, “Electric wheelchair simulator for rehabilitation of persons with motor disability,” in National Rehabilitation Centre for Persons with Disabilities Conference paper, Japan, 2006.
  42. N. Steyn, Virtual reality platform modelling and design for versatile electric wheelchair simulation in an enabled environment, [Ph.D. thesis], Department of Electrical Engineering, Tshwane University of Technology, 2014.
  43. P. S. Archambault, S. Cachecho, S. Tremblay, F. Routhier, and P. Boissy, “Driving performance in a power wheelchair simulator,” Disability and Rehabilitation, vol. 7, no. 3, pp. 226–233, 2011. View at Publisher · View at Google Scholar · View at Scopus
  44. N. Vignier, J. Ravaud, M. Winance, F. Lepoutre, and I. Ville, “Demographics of wheelchair users in France: results of national community-based handicaps-incapacités-dépendance surveys,” Journal Rehabilitation Medicine, vol. 40, no. 3, pp. 231–239, 2008. View at Publisher · View at Google Scholar · View at Scopus
  45. B. W. Johson and J. H. Aylor, “Dynamic modeling of an electric wheelchair,” IEEE Transactions on Industry Applications, vol. IA-21, no. 5, pp. 1284–1293, 1985. View at Publisher · View at Google Scholar · View at Scopus
  46. Y. Rabhi, M. Mrabet, F. Fnaiech, and P. Gorce, “Intelligent joystick for controlling power wheelchair navigation,” in 3rd International Conference on Systems and Control, pp. 1020–1025, Algiers, Algeria, 2013. View at Publisher · View at Google Scholar · View at Scopus
  47. D. M. Brienza and J. Angelo, “A force feedback joystick and control algorithm for wheelchair obstacle avoidance,” Disability and Rehabilitation, vol. 18, no. 3, pp. 123–129, 1996. View at Publisher · View at Google Scholar
  48. R. Simpson, D. Poirot, and M. F. Baxter, “Evaluation of the Hephaestus smart wheelchair system,” in International Conference on Rehabilitation Robotics, Stanford, CA, USA, 1999.
  49. B. Kuipers, Building and Evaluating an Intelligent Wheelchair, Internal Report, 2006.
  50. P. J. Holliday, A. Mihailidis, R. Rolfson, and G. Fernie, “Understanding and measuring powered wheelchair mobility and manoeuvrability. Part I. Reach in confined spaces,” Disability and Rehabilitation, vol. 27, no. 16, pp. 939–949, 2005. View at Publisher · View at Google Scholar · View at Scopus
  51. T. Carlson and Y. Demiris, “Collaborative control in human wheelchair interaction reduces the need for dexterity in precise manoeuvres,” in Proceedings of “Robotic Helpers: User Interaction, Interfaces and Companions in Assistive and Therapy Robotics”, a Workshop at ACM/IEEE HRI 2008, pp. 59–66, 2008.
  52. C. Urdiales, J. Peula, C. Barrue et al., “A new multi-criteria optimization strategy for shared control in wheelchair assisted navigation,” Autonomous Robots, vol. 30, no. 2, pp. 179–197, 2011. View at Publisher · View at Google Scholar · View at Scopus
  53. S. L. Seyler, A. Kumar, M. F. Thorpe, and O. Beckstein, “Path similarity analysis: a method for quantifying macromolecular pathways,” PLoS Computational Biology, vol. 11, no. 10, article e1004568, 2015. View at Publisher · View at Google Scholar · View at Scopus
  54. L. Lopez-Samaniego, B. Garcia-Zapirain, and A. Mendez-Zorrilla, “Memory and accurate processing brain rehabilitation for the elderly: LEGO robot and iPad case study,” Bio-medical Materials and Engineering, vol. 24, no. 6, pp. 3549–3556, 2014. View at Publisher · View at Google Scholar · View at Scopus
  55. E. Ambrosini, S. Ferrante, M. Rossini et al., “Functional and usability assessment of a robotic exoskeleton arm to support activities of daily life,” Robotica, vol. 32, no. 8, pp. 1213–1224, 2014. View at Publisher · View at Google Scholar · View at Scopus
  56. F. Amirabdollahian, S. Ates, A. Basteris et al., “Design, development and deployment of a hand/wrist exoskeleton for home-based rehabilitation after stroke - SCRIPT project,” Robotica, vol. 32, no. 8, pp. 1331–1346, 2014. View at Publisher · View at Google Scholar · View at Scopus
  57. B. Mónica Faria, S. Vasconcelos, L. Paulo Reis, and N. Lau, “Evaluation of distinct input methods of an intelligent wheelchair in simulated and real environments: a performance and usability study,” Assistive Technology, vol. 25, no. 2, pp. 88–98, 2013. View at Publisher · View at Google Scholar · View at Scopus
  58. J. BrookeP. W. Jordan, B. Thomas, B. A. Weerdmeester, and A. I. McClelland, “SUS: a “quick and dirty” usability scale,” Usability Evaluation Industry, Taylor and Francis, London, UK, 1996. View at Google Scholar