Deep Learning Based Real Time Fall Detection Model

Health professionals are enormously utilising the advantages of most advanced technologies, leading to a scalable development in the field of health care. There has been a paradigm shift from manual monitoring towards more accurate virtual monitoring with a high accuracy rate. The world is very much concerned about taking care of old people. One of the worst things that can happen to older individuals is a fall. The creation of fall detection systems is urgently needed due to the aging population's rapid growth. With the development of technology, fall detection and rescue systems are able to provide quick emergency assistance to victims of accidental falls, thereby reducing the number of fatalities by detection of fall and providing adequate care. Here, we propose a model for real-time fall detection that uses deep convolutional neural networks. An automated fall detection model is essential for elderly care, since it offers a real time monitoring and fall detection whereas manual fall detection and immediate attention depends on timely availability of many factors. A real time monitoring system coupled with Deep learning techniques can accomplish this task. For fall detection, many deep learning networks can be utilized. Here, we propose an efficient model for real-time fall detection that uses deep convolutional neural networks and torch tensors. The system will recognize Fall and Non Fall from a set of real time input videos and respond according to scenario. We tried a hardware demonstration of an Automated Fall Detection System in an Up Extreme Series single board computer with LSTM neural network model.