Abstract

Falls have become common nowadays among the elderly. It has been noted by the World Health Organization WHO that approximately one out of 3 elderly people aged above 65 living alone tend to fall and the rate can increase in the coming years. Many ideas have been proposed and worked out including using of inertial sensors, accelerometer and gyro meter. This paper proposes a method where the video from the camera is processed and the features are extracted. The features are extracted using HOG and statistical approach. The database contains fall and daily activities and Support Vector Machine SVM is used for classification which gives an accuracy of 100 . Hephzibah Thomas | Thyla B "Intelligent Fall Detection Using Statistical Features and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1 , December 2018, URL: http://www.ijtsrd.com/papers/ijtsrd19024.pdf Direct URL: http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/19024/intelligent-fall-detection-using-statistical-features-and-machine-learning/hephzibah-thomas

Authors and Affiliations