An efficient vision based elderly care monitoring framework using fall detection

Abstract

Fall is one of the major reason of elderly people living alone for getting injured. Numerous elderly individuals are living alone in their homes. In case if the elderly tumble down, it might be troublesome for them to ask for help. Due to this problem there is a dire requirement of an efficient home monitoring framework. An effective fall detection system can help provide rapid help to the patient and improve the chances of survival of the patient. The principle goal of this paper is to design a vision-based fall detection framework for the elderly people. Fall identification framework at home is progressively vital and dependable observation framework is a need to mitigate the outcomes of fall. In this paper, we look forward to detecting a fall and normal walking of an individual using background subtraction, Motion History Image, feature extractor like Zernike Moments and then using a learning model. The proposed framework is able to detect fall and normal walking with an accuracy of 92.86% and 90.82%.

Keywords:

• Fall detection
• Background subtraction
• Motion History Image (MHI)
• Zernike Moments (ZM)
• Principal Component Analysis
• Support Vector Machine (SVM)