Elderly Fall Detection by Sensitive Features Based on Image Processing and Machine Learning

Abstract

The world’s elderly population is growing every year. It is easy to say that the fall is one of the major dangers that threaten them. This paper offers a Trained Model for fall detection to help the older people live comfortably and alone at home. The purpose of this paper is to investigate appropriate methods for diagnosing falls by analyzing the motion and shape characteristics of the human body. Several machine learning technologies have been proposed for automatic fall detection. The proposed research reported in this paper detects a moving object by using a background subtraction algorithm with a single camera. The next step is to extract the features that are very important and generally describe the human shape and show the difference between the human falls from the daily activities. These features are based on motion, changes in human shape, and oval diameters around the human and temporal head position. The features extracted from the human mask are eventually fed in to various machine learning classifiers for fall detection. Experimental results showed the efficiency and reliability of the proposed method with a fall detection rate of 81% that have been tested with UR Fall Detection dataset.