Intelligent Image Analysis System for Position Control of People with Locomotor Disabilities

  • Marius Popescu
  • Antoanela NaajiEmail author
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 6)


The paper presents a remote-controlled system mounted on the seats used by the persons with locomotor disabilities. In these cases, a multitude of problems may be avoided, such as obstacles or bumps, without the direct action of the human being, because the device adapts to each new situation, to reach the destination. The system used to guide or to move a wheelchair on the ground comprises several functional blocks that are distinct as structure, but interdependent. The movement area is monitored by a camcorder connected to the microcontroller, which transmits images to it. The microprocessor processes the images, calculates and sends signals to the communication interface of the equipment. The system receives the commands sent by the microcontroller, interprets them and carries out the movement, together with the transmission of various pieces of information towards the microcontroller such as: confirmations regarding data reception and their validity, data from the sensors, the image itself, etc.


Image analysis Avoiding obstacles Persons with locomotor disabilities Smart wheelchair 


  1. 1.
    G. Bourhis, K. Moumen, P. Pino, S. Rohmer, A. Pruski, Assisted navigation for a powered wheelchair, in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Systems Engineering in the Service of Humans, Le Touquet, France (IEEE, Piscataway, NJ, 1993), pp. 553–558 Google Scholar
  2. 2.
    S.P. Levine, D.A. Bell, L.A. Jaros, R.C. Simpson, Y. Koren, J. Borenstein, The NavChair assistive wheelchair navigation system. IEEE Trans. Rehabil. Eng. 7(4), 443–451 (1999)CrossRefGoogle Scholar
  3. 3.
    E. Prassler, J. Scholz, P. Fiorini, A robotic wheelchair for crowded public environments. IEEE Robot. Autom. Mag. 8(1), 38–45 (2001)CrossRefGoogle Scholar
  4. 4.
    R.C. Simpson, E.F. LoPresti, S. Hayashi, I.R. Nourbakhsh, D.P. Miller, The smart wheelchair component system. J. Rehabil. Res. Dev. 41(3B), 429–442 (2004)CrossRefGoogle Scholar
  5. 5.
    M. Mazo, An integral system for assisted mobility. IEEE Robot. Autom. Mag. 8(1), 46–56 (2001)CrossRefGoogle Scholar
  6. 6.
    E. Subhadra, O.F. Andrew, C. Neophytou, L. de Souza, Older adults’ use of, and satisfaction with, electric powered indoor/outdoor wheelchairs. Age Ageing 36(4), 431–435 (2007)CrossRefGoogle Scholar
  7. 7.
    R.A.M. Braga, M.P. Beng, L.P. Reis, A.P. Moreira, Intell Wheels: platforma modulara de dezvoltare pentru scaune cu rotile inteligente. J. Rehabil. Res. Dev. 48(9), 1061–1076 (2011)CrossRefGoogle Scholar
  8. 8.
    C.S. Richard, Smart wheelchairs: a literature review. J. Rehabil. Res. Dev. 42(4), 423–436 (2005)CrossRefGoogle Scholar
  9. 9.
    R.C. Simpson, D. Poirot, M.F. Baxter, The Hephaestus smart wheelchair system. IEEE Trans. Neural Syst. Rehabil. Eng. 10(2), 118–122 (2002)CrossRefGoogle Scholar
  10. 10.
    E.S. Boy, C.L. Teo, E. Burdet, Collaborative wheelchair assistant, in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Lausanne, Switzerland (Piscataway, NJ, 2002), pp. 1511–1516Google Scholar
  11. 11.
    R. Cooper, T. Corfman, S. Fitzgerald, M. Boninger, D. Spaeth, W. Ammer, J. Arva, Performance assessment of a pushrim activated power assisted wheelchair. IEEE Trans. Control Syst. Technol. 10(1), 121–126 (2002)CrossRefGoogle Scholar
  12. 12.
    D. Cagigas, J. Abascal, Hierarchical path search with partial materialization of costs for a smart wheelchair. J. Intell. Robot. Syst. 39(4), 409–431 (2004)CrossRefGoogle Scholar
  13. 13.
    Y. Matsumoto, T. Ino, T. Ogasawara, Development of intelligent wheelchair system with face-and gaze-based interface, in Proceedings of the 10th IEEE International Workshop on Robot and Human Interactive Communication, Bordeaux-Paris, France (Piscataway, 2001), pp. 262–267Google Scholar
  14. 14.
    R.C. Simpson, S.P. Levine, Voice control of a powered wheelchair. IEEE Trans. Neural Syst. Rehabil. Eng. 10(2), 122–125 (2002)CrossRefGoogle Scholar
  15. 15.
    E.M. Craparo, M. Karatas, T.U. Kuhn, Sensor placement in active multistatic sonar networks. Nav. Res. Logist. 287–304 (2017)MathSciNetCrossRefGoogle Scholar
  16. 16.
    M.C. Popescu, A. Petrisor, Robots-Robot Control Systems (in Romanian) (Ed. Universitaria, Craiova, 2009), p. 139Google Scholar
  17. 17.
    M.C. Popescu, Telecomunications (in Romanian) (The Printing House of the University of Craiova, 2005), p. 233Google Scholar
  18. 18.
    A. Lankenau, T. Röfer, A versatile and safe mobility assistant. IEEE Robot. Autom. Mag. 8(1), 29–37 (2001)CrossRefGoogle Scholar
  19. 19.
    M.C. Popescu, Telecomunications (in Romanian) (Ed. Universitaria, Craiova, 2008), p. 428Google Scholar
  20. 20.
    T. Gomi, A. Griffith, Developing intelligent wheelchairs for the handicapped, in Assistive Technology and Artificial Intelligence: Applications in Robotics, User Interfaces and Natural Language Processing. Lecture Notes in Artificial Intelligence (Springer-Verlag, Heidelberg, 1998), pp. 150–178Google Scholar
  21. 21.
    I. Moon, M. Lee, J. Ryu, M. Mun, Intelligent robotic wheelchair with EMG, gesture-, and voice-based interfaces, in IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, NV (Piscataway, NJ, 2003), pp. 3453–3458Google Scholar
  22. 22.
    J.D. Yoder, E.T. Baumgartner, S.B. Skaar, Initial results in the development of a guidance system for a powered wheelchair. IEEE Trans. Rehabil. Eng. 4(3), 143–151 (1996)CrossRefGoogle Scholar
  23. 23.
    M.C. Popescu, A. Petrisor, 2D tracking control algorithms, in Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics, Venice, Italy, pp. 368–373 (2006)Google Scholar
  24. 24.
    A. Petrisor, N.G. Bizdoaca, M. Drighiciu, M.C. Popescu, Control strategy of a 3-DOF walking robot, in The International Conference on “Computer as a Tool” (IEEE, Warsaw, 2007), pp. 2337–2342Google Scholar
  25. 25.
    H.N. Chow, Y. Xu, S.K. Tso, Learning human navigational skill for smart wheelchair, in IEEE/RSJ International Conference on Intelligent Robots and Systems, Lausanne, Switzerland (Piscataway, NJ, 2002), pp. 996–1001Google Scholar
  26. 26.
    X. Li, X. Zhao, T. Tan, A behavior-based architecture for the control of an intelligent powered wheelchair, in IEEE International Workshop on Robot and Human Interactive Communication, Osaka, Japan (Piscataway, NJ, 2000), pp. 80–83Google Scholar
  27. 27.
    A. Petrisor, N. Bizdoacă, A. Drighiciu, M.C. Popescu, Three legs robot—application for modelling and simulation of walking robots control algorithms, in The 3rd International Conference on Robotics, Buletinul Institutului Politehnic din Iasi, Editat de Universitatea Tehnica “Gh. Asachi”, Tomul LII (LVI), Fascicula 7B, Sectia Constructii de masini, pp. 127–133 (2006)Google Scholar
  28. 28.
    S.P. Parikh, R.S. Rao, S.H. Jung, V. Kumar, J.P. Ostrowski, C.J. Taylor, Human robot interaction and usability studies for a smart wheelchair, in IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, NV (Piscataway, NJ, 2003), pp. 3206–3211Google Scholar
  29. 29.
    H. Wakaumi, K. Nakamura, T. Matsumura, Development of an automated wheelchair guided by a magnetic ferrite marker lane. J. Rehabil. Res. Dev. 29(1), 27–34 (1992)CrossRefGoogle Scholar
  30. 30.
    H.P. Moravec, Certainty grids for mobile robots, in NASA/JPL Space Telerobotics Workshop (Pasadena, CA, JPL Publications, 1987), pp. 307–312Google Scholar
  31. 31.
    S.B. Shuvra, Handbook of Signal Processing Systems (Springer International Publishing AG, 2019)Google Scholar
  32. 32.
    M. Sun, J. Hu, An image edge feature extraction method based on multi-operator fusion. Rev. Téc. Ing. Univ. Zulia 39(10), 331–339 (2016)Google Scholar
  33. 33.
    J. Lee, H. Tang, J. Park, Energy efficient canny edge detector for advanced mobile vision applications. IEEE Trans. Circuits Syst. Video Technol. 28(4), 1037–1046 (2018)CrossRefGoogle Scholar
  34. 34.
    C. Poynton, Digital Video and HD, 2nd edn. (Morgan Kaufmann, Burlington, 2012), p. 134Google Scholar
  35. 35.
  36. 36.
    Y. Zheng, Y. Chang, M. Sarem, Accurate computation of geometric moments using non-symmetry and anti-packing model for color images. Int. J. Comput. Commun. Eng. 6(1), 19–28 (2017)CrossRefGoogle Scholar
  37. 37.
    C. Cercel, Command the Position of an Object in a Plane Through Image Analysis (in Romanian), pp. 35–52 (2006)Google Scholar
  38. 38.
    R. Braniscan, M.C. Popescu, A. Naaji, Secure PHP OpenSSL crypto online tool. Int. J. Adv. Comput. Netw. ITS Secur. 5(2), 108–112 (2015)Google Scholar
  39. 39.
    V. Chernov, J. Alander, V. Bochko, Integer-based accurate conversion between RGB and HSV color spaces. Comput. Electr. Eng. 46, 328–337 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Engineering and Computer Science“Vasile Goldis” Western University of AradAradRomania

Personalised recommendations