IoT based smart health monitoring with CNN using edge computing

In the last years, healthcare monitoring has followed a great upwards grown compared to the past decade with the intrusion of the Internet of Things (IoT). The IoT based health paradigm has a vibrant role in health care services to enhance data processing and data prediction. Fall accidents are common in the elderly persons. IoT with Artificial Intelligence (AI) provides a major paradigm to predict the human control, to analyze the cause of human tendency. Fall detection is a major prevailing need with the elderly person, and in order to mitigate this problem an AI based deep Convolutional neural network is proposed to analyze the cause of falling. The deep convolution neural network has been proposed together with the fog and edge computing to analyze the health monitoring tasks. This work analyses the architecture and motions of the persons for fall detection with the sensor nodes. An experimental study is carried out with a benchmark dataset and the higher accuracy in the classification is obtained with this proposal in the simulation results.