A Review on Robo Chair Assistance using Head Gesture Recognition
Shraddha V. Manikpure1, Rushikesh T. Bankar2, Suresh S. Salankar3
1Ms. Shraddha V. Manikpure, Student, M. Tech. in Communication, Department of Electronics & Telecommunication Engineering, G. H. Raisoni College of Engineering, Nagpur, India.
2Rushikesh T. Bankar, Asst. Prof., Department of Electronics & Telecommunication Engineering, G. H. Raisoni College of Engineering, Nagpur, India.
3Dr. Suresh S. Salankar, Prof., Department of Electronics & Telecommunication Engineering, G. H. Raisoni College of Engineering, Nagpur, India.
Manuscript received on January 07, 2015. | Revised Manuscript received on January 12, 2015. | Manuscript published on January 15, 2015. | PP: 60-62 | Volume-3 Issue-2, January 2015. | Retrieval Number: B0792013215/2014©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Face detection is a computer technology that determines the location & size of human faces in digital images. Thus by determining the head gesture of person sitting on robo chair the controlling of the chair can be done by the improved Adaboost algorithm. The recognized gestures are used to generate motion control commands to the low-level DSP motion controller so that it can control the motion of the Robo Chair according to the user’s need. Looking for something, when the commands for the movement are generating must be considered unnecessary movement, thus to avoid this, Head gesture interface focused on the central position of a person sitting on robo chair & identify only the useful head gesture. This paper determines, the improved Adaboost algorithm used for face detection is to increase the output results for the system, effectiveness of the system & efficiency on which the system implements. The concept of Obstacle detection is also used for the enhancement of the system, it is done by using ultra sonic sensors.
Keywords: Face Recognition, Head Gestures, Face Tracking, Obstacle Avoidance.