This paper is published in Volume-3, Issue-3, 2017
Area
Bio-medical
Author
Keerthana .B, Nikita Pandya, Pooja Joshi, Neha .N, Dr. S. Sathiyan
Org/Univ
SRM University, India
Pub. Date
04 May, 2017
Paper ID
V3I3-1186
Publisher
Keywords
Tri-axial Accelerometer, Electronic compass, GPS, 3G Networks, Cascade Classifier.

Citationsacebook

IEEE
Keerthana .B, Nikita Pandya, Pooja Joshi, Neha .N, Dr. S. Sathiyan. A Smart Phone Based Accident Fall Detection, Positioning and Rescue System, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Keerthana .B, Nikita Pandya, Pooja Joshi, Neha .N, Dr. S. Sathiyan (2017). A Smart Phone Based Accident Fall Detection, Positioning and Rescue System. International Journal of Advance Research, Ideas and Innovations in Technology, 3(3) www.IJARIIT.com.

MLA
Keerthana .B, Nikita Pandya, Pooja Joshi, Neha .N, Dr. S. Sathiyan. "A Smart Phone Based Accident Fall Detection, Positioning and Rescue System." International Journal of Advance Research, Ideas and Innovations in Technology 3.3 (2017). www.IJARIIT.com.

Abstract

This paper proposes the architecture of accident fall detection, positioning and rescue system that uses 3G networks. To perceive the fall detection algorithm, the angles acquired by the electronic compass and the waveform sequence of the tri-axial accelerometer on the smart phone are used as the system inputs. The obtained signals are used to produce an ordered feature sequence and then tested in consecutive way by the proposed cascade classifier for identification purpose. As soon as equivalent characteristic is confirmed by the classifier at the current state, it can continue to the state; contrarily the system will come back to the initial state and halt for the emergence of another feature sequence. When a fall accident event is diagnosed the victim’s position can be acquired by global positioning system (GPS) and forwarded to the rescue centre via 3G network so that immediate medical help can be provided.With the proposed cascaded classification architecture, the computational burden and power consumption issue on the smart phone system can be mollified. Moreover, known fall incident detection accuracy and reliability up to 92% on the sensitivity and 99. 75% on the specificity.