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Low Cost Head Gesture Controlled Wheelchair for Quadriplegic Patients

  • A. AnithaEmail author
  • N. Dharshini
  • B. Raga Ravali
  • Shriya Chaurasia
  • G. Christina
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 26)

Abstract

Quadriplegia is a paralytic condition in which the affected individuals have partial or total loss of their limb movement. This paper proposes a prototype a low cost head gesture controlled electric wheelchair which can be a cost-effective alternate to the existing models. This is achieved by enhancing the functionality of an ordinary wheelchair through image processing algorithms such as Kanade Lucas Tomasi (KLT) algorithm implemented in Raspberry pi. Raspberry pi uses ARM Cortex-A53 processor which is programmed in Python. The wheelchair moves in accordance with user commands in the form of head gestures. These commands are picked up using web camera. Basic face detection and face tracking in different directions such as straight, left and right has been achieved and deployment of the entire setup on the chassis has been completed and a prototype is developed at a cost of Rs.10000. Further, safe movement of the wheelchair may be ensured using ultrasonic sensors which detects obstacles in its path and stops the wheelchair if need arises.

Keywords

Raspberry Pi Kanade Lucas Tomasi (KLT) algorithm 

References

  1. 1.
    Ruzaij, M.F., Neubert, S., Stoll, N., Thurow, K.: A speed compensation algorithm for a head tilts controller used for wheelchairs and rehabilitation applications. In: Proceedings of IEEE 15th International Symposium, Slovakia, 26–28 January 2017Google Scholar
  2. 2.
    Gupta, M.S.D., Patchava, V., Menezes, V.: Healthcare based on IoT using Raspberry Pi. In: Proceedings of 2015 International Conference on Green Computing and Internet of Things (ICGIoT), Noida (2015)Google Scholar
  3. 3.
    Chatrath, J., Gupta, P., Ahuja, P., Goel, A., Arora, S.M.: Real time human face detection and tracking. In: Proceedings of 2014 International conference on Signal Processing and Integrated Networks (SPIN), Noida (2014)Google Scholar
  4. 4.
    Al-Najdawi, N., Tedmori, S., Edirisinghe, E., Bez, H.: An automated real-time people tracking system based on KLT features detection. Int. Arab. J. Inf. Technol. 9(1), 100–107 (2012)Google Scholar
  5. 5.
    Chatterjee, D., Chandran, S.: Comparative study of Camshift and KLT algorithms for real time face detection and tracking applications. In: Proceedings of 2nd International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), Kolkata (2016)Google Scholar
  6. 6.
    Lan, X., Xiongy, Z., Zhangz, W., Li, S., Chang, H., Zengy, W.: A super-fast online face tracking system for video surveillance. In: Proceedings of 2016 IEEE International Symposium on Circuits and Systems, Canada, 22–25 May 2016Google Scholar
  7. 7.
    Cismas, A., Ciobanu, V., Matei, I., Casu, G.: Crash detection using IMU sensor. In: Proceedings of IEEE 21st International Conference on Control Systems and Computer Science, Romania, 31 May 2017Google Scholar
  8. 8.
    Abed, A.A., Rahman, S.A.: Python-based raspberry pi for hand gesture recognition. Int. J. Comput. Appl. 173(4), 18–24 (2017)Google Scholar
  9. 9.
    Abin, A.A., Fotouhi, M., Kasaei, S.: Real time multiple face detection and tracking. In: Proceedings of IEEE 14th International CSI Computer Conference, Iran, 20–21 October 2015Google Scholar
  10. 10.
    Li, W., Yi, Z., Zhou, W., Zhu, Y., Liu, J.: Vehicle rollover dynamic monitoring based on tilt sensor. In: Proceedings of 2nd International Conference on Industrial and Information Systems (2010)Google Scholar
  11. 11.
    Sanudin, R., Mun, Y.T., Zaki, W.S.W., Wahab, M.H.A.: Wireless appliance control system. In: Proceedings of Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA 2009), Malaysia (2009)Google Scholar
  12. 12.
    Noiruxsar, C., Samanpiboon, P.: Face orientation recognition for electric wheelchair control. J. Autom. Control. Eng. 2(4), 402–405 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • A. Anitha
    • 1
    Email author
  • N. Dharshini
    • 1
  • B. Raga Ravali
    • 1
  • Shriya Chaurasia
    • 1
  • G. Christina
    • 1
  1. 1.Department of ECECoimbatore Institute of TechnologyCoimbatoreIndia

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