AUTOMATED MULTI MODES WHEELCHAIR
Keywords:
Raspberry Pi, Motor, Motor Drivers, voice recognition, ultrasonic sensorsAbstract
This paper is to develop a wheel chair control which is useful to the physically disabled person using multiple modes. The modes are Joystick, voice command, hand gesture and Android app. This mode will help persons with almost all sorts of disabilities. There are many wheelchair systems for physically disabled persons available in the market but as we move towards automation its more costly so, the main objective is to lower the price of the wheelchair while advancing the same. Raspberry pi is used as a controller for the system. Smart Wheel Chair is mechanically controlled devices designed to have self-mobility with the help of the user command. This reduces the user’s human effort and force to drive the wheels for wheelchair. Furthermore it provides an opportunity for visually or physically impaired persons to move from one place to another. The wheelchair is also provided with obstacle detection system, which reduces the chance of collision while on the journey.
References
Chin-Tuan Tan and Brian C. J. Moore, Perception of nonlinear distortion by hearing-impaired people, International Journal of Ideology 2008, Vol. 47, No. 5, Pages 246-256.
Oberle, S., and Kaelin, A. "Recognition of acoustical alarm signals for the profoundly deaf using hidden Markov models," in IEEE International Symposium on Circuits and Systems (Hong Kong), pp. 2285-2288., 1995.
A. Shawki and Z. J., A smart reconfigurable visual system for the blind, Proceedings of the Tunisian-German Conference on: Smart Systems and Devices, 2001.
C. M. Higgins and V. Pant, Biomimetic VLSI sensor for visual tracking of small moving targets, IEEE Transactions on Circuits and Systems, vol.51, pp. 2384–2394, 2004.
F. Daerden and D. Lefeber, The concept and design of pleated pneumatic artificial muscles. International Journal of Fluid Power, vol. 2, no. 3, 2001, pp. 41–45.
http://msdn.microsoft.com/enus/library/default.aspx
K. R. Castleman, Digital Image Processing, Pearson Education, 1996.
M. A. Mazidi, AVR microcontroller and Embedded Systems, 2008.
http://electronics.howstuffworks.com/gadgets/hightech-gadgets/speech-recognition.htm
D. Murray and A. Basu, ‘Motion tracking with an active camera’, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol 16, No. 5, pp.449-459, 1994.
http://www.voicerecognition.com/
N. Otsu. A threshold selection method from gray-level histogram, IEEE Trans. System, Man, and Cybernetic. vol. 9, no.1, pp. 62-66, 1979.
O. Mazumder, A. S. Kundu, R. Chattaraj and S. Bhaumik, "Holonomic wheelchair control using EMG signal and joystick interface." In Recent Adv. in Eng. and Comput. Sci., pages [1] – [6], Chandigarh, India, Mar. 2014.
F. Pasteau, A. Krupa and M. Babel, "Vision-based assistance for wheelchair navigation along corridors." In IEEE Int. Conf. Robot. and Auto., pages 4430–4435, Hong-Kong, Hong Kong SAR China, June 2014.
R. Desmond, M. Dickerman, J. Fleming, D. Sinyukov, J. Schaufeld and T. Padir, "Development of modular sensors for semi-autonomous wheelchairs." In IEEE Int. Conf. Technol. for Practical Robot Applicat., pages [1] – [6], Woburn, MA, Apr. 2013.
D. Sinyukov, R. Desmond, M. Dickerman, J. Fleming, J. Schaufeld and T. Padir, "Multi-modal control framework for a semi-autonomous wheelchair using modular sensor designs." Intell. Service Robot., 7(3): [145] – [155], Jul. 2014.
J. d. R. Millan, "BMI: Lessons from tests with impaired users." In Int. Winter Workshop Brain-Comput. Interface, pages [1] – [1], Jeongsun-kun, Feb. 2014.
D.K. Rathore, P. Srivastava, S. Pandey, and S. Jaiswal, "A novel multipurpose smart wheelchair." In IEEE Students’ Conf. Elect., Electron. and Comput. Sci., pages [1] – [4], Bhopal, Mar. 2014.
U. Yayan, B. Akar, F. Inan, and A. Yazici, "Development of indoor navigation software for intelligent wheelchair." In IEEE Int. Symp. Innovations in Intell. Syst. and Applicat. Proc., pages [325] – [329], Alberobello, Jun. 2014.
F. Leishman, V. Monfort, O. Horn, and G. Bourhis, "Driving assistance by deictic control for a smart wheelchair: The assessment issue." IEEE Trans. Human-Mach. Syst., 44(1): [66] – [77], Feb. 2014.
S. Jain and B. Argall, "Automated perception of safe docking locations with alignment information for assistive wheelchairs." In IEEE/RSJ Int. Conf. Intell. Robots and Syst., pages [4997] – [5002], Chicago, IL, Sept. 2014.
R. Simpson, "Smart Wheelchair Component System." J. Rehabil. Research And Develop., 41(3B): 429–442, 2004.
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