A Heuristic Model for Predicting Human Fall Detection using Machine Learning Techniques
Mohammed Inayathulla1, PA Hima Kiran2, M Chandana Sri3, M Deepika4

1Mohammed Inayathulla1, Assistant Professor, Department of Computer Science & Engineering, Malla Reddy Engineering College (Autonomous), Hyderabad, Telangana, India.
2PA Hima Kiran2, Associate Professor, Department of Computer Science & Engineering, Malla Reddy Engineering College (Autonomous), Hyderabad, Telangana, India.
3M Chandana Sri3, UG Student , Department of Computer Science & Engineering, Malla Reddy Engineering College (Autonomous), Hyderabad, Telangana, India.
4M Deepika4, UG Student Department of Computer Science & Engineering, Malla Reddy Engineering College (Autonomous), Hyderabad, Telangana, India. 

Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 119-121 | Volume-9 Issue-2, July 2020. | Retrieval Number: B3180079220/2020©BEIESP | DOI: 10.35940/ijrte.B3180.079220
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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: It is very obvious that human fall due to unconsciousness is a very common health problem in every human being. With the evolution of many smart health devices, we should contribute the technological advancement of machine learning into it. Different techniques are already used in order to detect human fall detection in human beings. In this paper we have studied the patterns of falling of human through the fall detection dataset while this human was performing various motions. By understanding all these we have generated the prediction protocol which estimates the fall of a person using fall detection dataset. Machine Learning classifiers were used to predict the human fall and a comparative study of various algorithms used was developed to find out the best classifier.
Keywords: Classification, Fall Prediction, Machine Learning, Random Forest.