dc.contributor.author |
Rathnayake, K.A.S.V. |
|
dc.contributor.author |
Diddeniya, S.I.A.P. |
|
dc.contributor.author |
Wanniarachchi, W.K.I.L. |
|
dc.date.accessioned |
2022-08-10T06:37:25Z |
|
dc.date.available |
2022-08-10T06:37:25Z |
|
dc.date.issued |
2017 |
|
dc.identifier.citation |
Rathnayake, K.A.S.V., Diddeniya, S.I.A.P. & Wanniarachchi, W.K.I.L. (2017). VOICE AND GESTURAL CONTROL OF HOUSEHOLD APPLIANCES USING KINECT MOTION SENSOR. Proceedings of the 4th International Conference on Multidisciplinary Approaches – 2017 |
en_US |
dc.identifier.uri |
http://dr.lib.sjp.ac.lk/handle/123456789/11584 |
|
dc.description.abstract |
Voice recognition and gesture recognition have given a considerable contribution in
bringing human-machine interactions into a different level without requiring
physical touch, or contact with devices. In this proposed work, voice recognition
and gesture recognition involve creating a system which is able to interpret specific
human gestures and vocal inputs via mathematical algorithms. We use these
algorithms to convey meaningful information and to control appliances in the home.
This proposed system mainly focuses on people who are differently abled and it can
be used as a solution for their problem, the inability of controlling appliances in
their own room by themselves. Microsoft Kinect motion sensor plays a major role
in this developed system by taking gesture inputs and vocal inputs and feeding them
to the central controlling system through a preliminary process. The speech model
of the system uses Microsoft speech grammar and Speech recognition engine to
recognize the vocal input commands. Gesture model of the system uses Visual
Gesture Builder software to train the “ON” input gesture command and the “OFF”
input gesture command. The whole system runs with a Graphical User Interface
making it easier to contact with the user. Ultimately, the user can select the device
and its location, by using pre-defined voice command such as, “Living Room TV”.
And also, that appliance can be controlled by using pre-defined gesture commands
which have been assigned for “ON” and “OFF”. The accuracy of the system is very
important when it is used as a real-time home device controlling system. So the
system was subjected to an experiment which provided response rate. It showed
more than 95% accuracy when the distance to the sensor was 4 m and even when
the noise level at the Kinect sensor was about 53 dB. Hence, this proposed system
will be an efficient home automation system which will make people’s day-to-day
life easier and also will be a very useful system for people who are differently abled
and will help them to live like normal people. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
: Faculty of Graduate Studies, University of Sri Jayewardenepura |
en_US |
dc.subject |
Microsoft Kinect motion sensor 2.0, home automation, speech recognition, gesture recognition, human-machine interactions. |
en_US |
dc.title |
VOICE AND GESTURAL CONTROL OF HOUSEHOLD APPLIANCES USING KINECT MOTION SENSOR |
en_US |
dc.type |
Article |
en_US |