Voice Controlled Motorized Wheelchair with Real Time Obstacle ...

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Figure 3 shows the steps in which the image is converted from the real time image taken from the camera to the binary image which is used for actual pixel calculation. The top picture shows the real time image taken from the camera. The next picture shows the image converted into a grey scale image. The third picture shows the same image after it has its contrast adjusted and the final image is our required binary image. Figure 3- Conversion of real time image into binary image 727 VI. CONCLUSIONS We have successfully designed and implemented a motorized wheelchair controlled by a joystick or through voice recognition. The total cost was Rs.25000 (US $ 300) excluding the cost of the wheelchair. The voice recognition system worked for most of the commands (over 95%). Only when a word was not properly vocalized, the system did not recognize it. However, the joystick can always be used as a foolproof backup in this case. Overall, users reported satisfaction with the system. The obstacle avoidance system had satisfactory performance. Only very small objects like pencils, tennis balls or books were difficult to identify. Further work is needed to better identify small objects. VII. RESULTS After completion of our project, it was first tested indoors using easy to spot obstacles like chairs, flower pots, walls and people. With these objects the obstacle avoidance worked without any error. Next we tested our system on smaller objects like books, pencils, tennis balls and other similar small objects. Although the obstacle avoidance worked well with these objects too but in some rare cases (around 2-3%) the obstacles were not properly detected. The voice recognition system was first tested in a quiet room with a single user. All words were correctly recognized. Next we tested it with a different user on whom the system was not trained. About 5% errors occurred in this case, for example words like “right” were recognized as “write”. This was because the recognizer heard a different pronunciation. However, after the user had spoken the word a number of times the recognizer had enough examples and properly determined what pronunciation the user spoke. Next we tested the system in a noisy room by turning on some music in that room. When the music was light there was no problem in correctly recognizing the words but when we turned the volume high the recognizer found it difficult to recognize the user’s voice and often took commands from what it heard in the song. The joystick control was foolproof and worked perfectly in all cases with no problems.
VIII. REFERENCES [1] 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. [2] 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 [3] A. Shawki and Z. J., A smart reconfigurable visual system for theblind, Proceedings of the Tunisian-German Conference on: Smart Systems and Devices, 2001. [4] C. M. Higgins and V. Pant, Biomimetic VLSI sensor for visual tracking of small moving targets, IEEE Transactions on Circuits an Systems, vol. 51, pp. 2384–2394, 2004. [5] 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 [6] http://msdn.microsoft.com/enus/library/default.aspx [7] K. R. Castleman, Digital Image Processing, Pearson Education, 1996. [8] M. A. Maziddi, PIC microcontroller and Embedded Systems, 2008. [9] http://electronics.howstuffworks.com/gadgets /high-tech-gadgets/speech-recognition.htm [10] 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. [11] http://www.voicerecognition.com/ [12] N. Otsu. A threshold selection method from gray-level histogram, IEEE Trans. System, Man, and Cybernetic. vol. 9, no.1, pp. 62-66, 1979. 728
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