Open AccessDetection of fall using Embedded Device and Machine Learning Implementation
Author(s) -
Illuri Sreenidhi,
C Sneha,
Penke Satyanarayana
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b7659.129219
Subject(s) - fall of man , computer science , accidental , artificial intelligence , computer security , internet of things , the internet , rest (music) , falling (accident) , machine learning , medicine , world wide web , physics , environmental health , politics , political science , acoustics , law , cardiology
The health issues caused due to the abnormal and accidental falls increasing every day, some falls are even leading to death or fatal injuries. Such falls can cause trauma both physically and psychologically. To overcome these circumstances fall detection has become an important topic for researchers and scientists to provide better and effective solution. A proper detection of fall can save a life of human being be it any age by giving immediate required treatment. Generally alerting the concerned authorities regarding the fall happens to be crucial in the fall detection systems. There are many existing systems that tend to this problem but they all are heavily equipped and have some drawbacks. In this proposed system Raspberry pi4 is used with OpenCV for using MOG2 machine learning algorithm to detect the fall by concentrating only on the person. And for alerting the fall this system uses internet based REST API called TWILIO.