Prototyping an Autonomous Face Controlled System using Raspberry Pi on Wheelchairs
Shwetha H R1, Nikhitha V Melige2, Shabarish Reddy B S3, Sujeeth U S4, Sanjay D Gowda5

1Shwetha H R, Assistant Professor, Department of Electronics and Communication Engineering, J. N. N. College of Engineering, Shivamogga (Karnataka), India.
2Nikhitha V Melige, Department of Electronics and Communication Engineering, JNN College of Engineering, Shivamogga (Karnataka), India.
3Shabarish Reddy B S, Department of Electronics and Communication Engineering, J. N. N. College of Engineering, Shivamogga (Karnataka), India.
4Sujeeth U S, M.Tech, Department of VLSI Design, Vellore Institute of Technology, Chennai (Tamil Nadu), India.
5Sanjay D Gowda, M.Tech, Department of VLSI Design, Vellore Institute of Technology, Chennai (Tamil Nadu), India.

Manuscript received on October 24, 2021. | Revised Manuscript received on November 03, 2021. | Manuscript published on December 30, 2021. | PP: 5-8 | Volume-11 Issue-2, December 2021. | Retrieval Number: 100.1/ijeat.B32261211221 | DOI: 10.35940/ijeat.B3226.1211221
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© The Authors. 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: Quadriplegics are people who cannot use their extremities. Existing aiding devices are costly, complex, not user friendly and they are guardian dependent for movement. In this paper, a Raspberry Pi enabled wheelchair movement control by head movement detection is presented. A head movement detection technique based on Open – Computer Vision image processing is used which detects the user’s facial movement in real time using the camera. Using this, the wheelchair motors are controlled and are driven in the indicated direction. Also, numerous sensors such as temperature sensor, pulse rate sensor, accelerometer sensor and fire sensor are added to monitor patient health, safety and well-being. 
Keywords: Arduino Mega, Facial Landmark Technique, HOG, Open – CV, Raspberry Pi.