Detection of fall using Embedded Device and Machine Learning Implementation
Illuri Sreenidhi1, Chatharajupalli Navya Sneha2, Penke Satyanarayana3
1Illuri Sreenidhi* Department of ECM, KLEF, Green Fields, Vaddeswaram, Andhra Pradesh.
2Chatharajupalli Navya Sneha, Department of ECM, KLEF, Green Fields, Vaddeswaram, Andhra Pradesh.
4Penke Satyanarayana, Department of ECM, KLEF, Green Fields, Vaddeswaram, Andhra Pradesh.
Manuscript received on November 17, 2019. | Revised Manuscript received on 27 November, 2019. | Manuscript published on December 10, 2019. | PP: 3124-3129 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7659129219/2019©BEIESP | DOI: 10.35940/ijitee.B7659.129219
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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: 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.
Keywords: Raspberry pi4, OpenCV (open computer vision), SVM (Support Vector Machine), HMM (Hidden Markov Model) MOG2 Slgorithm, TWILIO.
Scope of the Article: Machine Learning