Intelligent Fall Detection Monitoring and Convolutional Neural Networks

Abstract

Intelligent Fall Detection And Convolutional Neural Networks System  events are important health issues in elderly people who are alone in homes. Hence, a confident and real-time video surveillance device that pays attention could make their lives better. In this system there is will be live video streaming and in that video streaming we will detect the postures of the person using Convolution neural network. If we find that a person has fallen then we can capture that particular frame and can send the alert notifications along with that frame to the caretakers. 

One of the biggest challenges in modern societies is the improvement of healthy aging and the support to older persons in their daily activities. In
particular, given its social and economic impact. The automatic detection of falls has attracted considerable attention in the computer vision and pattern recognition communities.

Although the approaches based on wearable sensors have provided high detection rates, some of the potential users are reluctant to wear them and thus their use is not yet normalized. As a consequence, alternative approaches such as vision- based methods have emerged.Fall is one of the main causes of severe accidents or even death especially for the elderly.