Skip to main content
SpringerLink
Log in

Simulation of a Wheelchair Control System Based on Computer Vision Through Head Movements for Quadriplegic People

  • Conference paper
  • First Online:
Advanced Engineering, Technology and Applications (ICAETA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1983))

  • 108 Accesses

Abstract

People with quadriplegia rely on someone else to get around using a manual wheelchair. Using motorized wheelchairs gives them some independence and, simultaneously, the need of methods to control them. In the state-of-the-art, there is a great variety of these minimally invasive methods that use external devices, for example, accelerometers, which could generate discomfort. The present work proposes a non-invasive system based on artificial vision is proposed that does not require placing any device on the user. The proposed system consists of an image acquisition stage, one for face detection using Viola-Jones algorithm and another for tracking using Kanade-Lucas-Tomasi (KLT) algorithm. Additionally, a way to enter or exit the commands mode is proposed so that the user can activate/deactivate the system as required. For this, a specific movement is presented, as long as this movement is not performed, the user can move his head freely without activating the motors in the angle of focus of the camera 20o to 45o, the system correctly interprets 100% of the head moving towards or away. The novel detail is that the system allows entry and exit of the command mode by means of a special movement of the head, if the user enters the commands mode, the following options are available: tilt his head to the left, right, forward or backward.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Scholten, E.W., et al.: Self-efficacy predicts personal and family adjustment among persons with spinal cord injury or acquired brain injury and their significant others: a dyadic approach. Arch. Phys. Med. Rehabil. 101(11), 1937–1945 (2020)

    Article  Google Scholar 

  2. Fattouh, A., Sahnoun, M., Bourhis, G.: Force feedback joystick control of a powered wheelchair: preliminary study. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 3, pp. 2640–2645 (2004). https://doi.org/10.1109/ICSMC.2004.1400729

  3. Nguyen, V.T., Sentouh, C., Pudlo, P., Popieul, J.-C.: Joystick haptic force feedback for powered wheelchair - a model-based shared control approach. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 4453–4459 (2020). https://doi.org/10.1109/SMC42975.2020.9283235

  4. Scudellari, M.: Self-driving wheelchairs debut in hospitals and airports. IEEE Spectrum 54(10), 14 (2022). https://spectrum.ieee.org/selfdriving-wheelchairs-debut-in-hospitals-and-airports

  5. Sato, F., Koshizen, T., Matsumoto, T., Kawase, H., Miyamoto, S., Torimoto, Y.: Self-driving system for electric wheelchair using smartphone to estimate travelable areas. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 298–304 (2018). https://doi.org/10.1109/SMC.2018.00061

  6. Burhanpurkar, M., Labbe, M., Guan, C., Michaud, F., Kelly, J.: Cheap or robust? The practical realization of self-driving wheelchair technology. In: International Conference on Rehabilitation Robotics (ICORR), pp. 1079–1086 (2017). https://doi.org/10.1109/ICORR.2017.8009393

  7. Rabhi, Y., Mrabet, M., Fnaiech, F.: Intelligent control wheelchair using a new visual joystick. J. Healthc. Eng. 2018, 1–20 (2018). https://doi.org/10.1155/2018/6083565

    Article  Google Scholar 

  8. Kim, J., et al.: Qualitative assessment of Tongue Drive System by people with high-level spinal cord injury. J. Rehabil. Res. Dev. 51(3), 451–466 (2014). https://doi.org/10.1682/JRRD.2013.08.0178

    Article  Google Scholar 

  9. Banach, K., Malecki, M., Rosól, M., Broniec, A.: Brain-computer interface for electric wheelchair based on alpha waves of EEG signal. Bio-Algorithms Med-Syst. 17(3), 165–172 (2021). https://doi.org/10.1515/bams-2021-0095

    Article  Google Scholar 

  10. Carrera, F.F., Chadrina, O., Andrango, E.M., Drozdov, V., et al.: System design to control a wheelchair using brain electric signals. Medisur 17(5), 650–663 (2019)

    Google Scholar 

  11. Al-Fahaidy, F., Al-Doais, H., Al-Fu-Haidy, F., Al-Oqabi, A., Qasabah, E.: Design and implementation of an eye-controlled self-driving wheelchair. Am. J. Sci. Res. Essays 6, 1–15 (2021)

    Google Scholar 

  12. Echefu, S., Lauzon, J., Bag, S., Kangutkar, R., Bhatt, A., Ptucha, R.: Milpet - the self-driving wheelchair. Electron. Imaging Auton. Veh. Mach. 2017(19), 41–49 (2017). https://doi.org/10.2352/ISSN.2470-1173.2017.19.AVM-019

    Article  Google Scholar 

  13. Ju, J., Shin, Y., Kim, E.: Vision based interface system for hands free control of an intelligent wheelchair. J. NeuroEngineering Rehabil. 6, 33 (2009). https://doi.org/10.1186/1743-0003-6-33

    Article  Google Scholar 

  14. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. I-511–I-518 (2001). https://doi.org/10.1109/CVPR.2001.990517

  15. Jia, P., Hu, H.H., Lu, T., Yuan, K.: Head gesture recognition for hands-free control of an intelligent wheelchair. Ind. Robot. 34(1), 60–68 (2007). https://doi.org/10.1108/01439910710718469

    Article  Google Scholar 

  16. Chatterjee, D., Chandran, S.: Comparative study of camshift and KLT algorithms for real time face detection and tracking applications. In: Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), pp. 62–65 (2016). https://doi.org/10.1109/ICRCICN.2016.7813552

  17. Manta, F., Cojocaru, D., Vladu, I., Dragomir, A., Mariniuc, A.: Wheelchair control by head motion using a noncontact method in relation to the pacient. In: 20th International Carpathian Control Conference (ICCC), pp. 1–6 (2019). https://doi.org/10.1109/CarpathianCC.2019.8765982

  18. Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Sig. Process. Lett. 23(10), 1499–1503 (2016). https://doi.org/10.1109/LSP.2016.2603342

    Article  Google Scholar 

  19. Kumar, A., Kaur, A., Kumar, M.: Face detection techniques: a review. Artif. Intell. Rev. 52(2), 927–948 (2019). https://doi.org/10.1007/s10462-018-9650-2

    Article  Google Scholar 

  20. Shi, J., Tomasi: Good features to track. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 1994, pp. 593–600 (1994). https://doi.org/10.1109/CVPR.1994.323794

  21. Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: 7th International Joint Conference on Artificial Intelligence (IJCAI 1981), pp. 121–130 (1981). Retrieved from the ResearchGate database

    Google Scholar 

  22. Furht, B., Akar, E., Andrews, W.: Digital Image Processing: Practical Approach. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-96634-2

    Book  Google Scholar 

  23. Kadir, K., Kamaruddin, M., Nasir, H., Safie, S., Bakti, Z.: A comparative study between LBP and Haar-like features for Face Detection using OpenCV. In: 4th International Conference on Engineering Technology and Technopreneuship (ICE2T), pp. 335–339 (2014). https://doi.org/10.1109/ICE2T.2014.7006273

  24. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002). https://doi.org/10.1109/TPAMI.2002.1017623

    Article  Google Scholar 

  25. Gotardo, P., Bellon, O., Boyer, K., Silva, L.: Range image segmentation into planar and quadric surfaces using an improved robust estimator and genetic algorithm. IEEE Trans. Syst. Man Cybern. Part B Cybern. 34(6), 2303–2316 (2004). https://doi.org/10.1109/TSMCB.2004.835082

    Article  Google Scholar 

  26. Torr, P., Zisserman, A.: MLESAC: a new robust estimator with application to estimating image geometry. Comput. Vis. Image Underst. 78(1), 138–156 (2000). https://doi.org/10.1006/cviu.1999.0832

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Escuela Politécnica Nacional, Av. Ladrón de Guevara E11-253, Quito, 170525, Ecuador

    Jirón Jhon, Álvarez Robin, Vega David, Lupera Pablo & Flores Antonio

  2. Colegio de Ciencias e Ingenierías “El Politécnico”, Universidad San Francisco de Quito USFQ, Quito, Ecuador

    Felipe Grijalva

Authors
  1. Jirón Jhon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Álvarez Robin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vega David
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Felipe Grijalva
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lupera Pablo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Flores Antonio
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Álvarez Robin .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Alessandro Ortis

  2. Istinye University, Istanbul, Türkiye

    Alaa Ali Hameed

  3. National University of Computer and Emerging Sciences, Islamabad, Pakistan

    Akhtar Jamil

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jhon, J., Robin, Á., David, V., Grijalva, F., Pablo, L., Antonio, F. (2024). Simulation of a Wheelchair Control System Based on Computer Vision Through Head Movements for Quadriplegic People. In: Ortis, A., Hameed, A.A., Jamil, A. (eds) Advanced Engineering, Technology and Applications. ICAETA 2023. Communications in Computer and Information Science, vol 1983. Springer, Cham. https://doi.org/10.1007/978-3-031-50920-9_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-50920-9_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50919-3

  • Online ISBN: 978-3-031-50920-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation