Voice Controlled Motorized Wheelchair with Real Time Obstacle ...

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
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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.