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Electronic Wheelchair for Physically Disabled Persons


  • Discipline of Electronics and Electrical Engineering, Lovely Professional University, Jalandhar - 144411, Punjab, India


This paper highlights various physical disorders and its associated disabilities. Plenty of wheel chair have been designed so far. A wheel chair which can assist the disabled people in their day to day life became a necessity. It is possible if their other organs of the body assist the electronic gadgets. Proposed method helps the physically challenged people to make their journey through electronic assisted module which works on signal processing over speech and image. This system works via speech or through recognition of hand gesture. To overcome the loss of signal because of real time inputs, different signal enhancement techniques are introduced to achieve high rate of accuracy and stability. The system's response time is very much considerable as the delays of the system are quite reduced. The operating system created to work on gestures and speech was tested on the test bed with different sets of data. Results marked prominent impression over the conventional ones with accuracy rate of 84.66% in case of image processing and 82.33% in speech signal processing.The refinement of the electronic wheelchair is going to definitely help many suffering from the disease.


Disability, Electronic Wheelchair, Feature Extraction, Motion Detection, Signal Enhancement.

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  • Chatterjee P, Milanfar P. Patch-based near-optimal image de-noising. IEEE Transactions on Image Processing. 2012 Apr; 21(4):1635–49.
  • Talebi H, Milanfar P. Global image de-noising. IEEE Transactions on Image Processing. 2014 Feb; 23(4):755–68.
  • Hirakawa K, Parks T. Image de-noising using total least squares. IEEE Transactions on Image Processing. 2006 Sep; 15(9):2730–42.
  • Umbaugh ES. Computer vision and image processing: A practical approach using CViptools with Cdrom. USA: Prentice Hall PTR; 1998.
  • Pizer SM, Amburn PE, Austin DJ. Adaptive histogram equalization and its variations. Computer Vision, Graphics and ImageProcessing. 1987 Sep; 39(3):355–68.
  • Russ C. The image processing handbook. Boca Raton, FL, USA: CRC Press; 1992.
  • Hummel R. Histogram modification techniques. Computer Graphics and Image Processing. 1975 Sep; 4(3):209–24.
  • Netraveli AN, Haskell BG. Digital pictures: Representation and compression. New York: Plenum; 1988.
  • Roberts LG. Machine perception of 3-D solids. Optical and Electro-Optical Information Processing. Cambridge: MIT Press; 1963. p. 59–68.
  • Abdou IE, Pratt KW. Quantitative design and evaluation enhancement/thresholding edge detectors. Proceedings of IEEE. 1979 May; 67(5):753–63.
  • Wang NK, Nakagawa S. Robust distant speaker recognition based on position dependent cepstral mean normalization. 2007 IEEE International Conference on Acoustics, Speech and Signal processing-ICASSP’07; Honululu. 2007 Apr 15-20. p. 817–20.
  • Rabiner LR. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE. 1989 Feb; 77(2):257–86.
  • Nataraj SK, Paulraj PM, Yaacob SB, Adom HA. Performance comparison of TEP and VEP responses using bispectral estimation to command an intelligent robot chair with communication aid. Indian Journal of Science and Technology. 2015 Aug; 8(20):1–11.


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