Docker-Based Intelligent Fall Detection Using Edge-Fog Cloud Infrastructure

Health sector is a life critical domain, which requires fast and intelligent decisions. Artificial intelligence-based monitoring systems can help the elderly people in situations like fall. In e-health, systems are equipped with wearable devices that aid in remote monitoring with the help of Internet of Things (IoT). Our proposed work ensures fall detection using a three-layer architecture (Edge-Fog-Cloud) which takes advantage of the available smart devices. The edge detection involves vision-based detection using a compressed neural network running on a smart device constructed using transfer learning. Decision making in fog involves ensemble learning methodology using sensor-based data and decision from the edge. The cloud is used for permanent storage and model building. The work also takes advantage of image augmentation for data set building to improve the performance of the model. The model is evaluated based on accuracy, and the advantage of the fog layer is evaluated based on the latency. The proposed model gives an accuracy of 98.5% which is compared with the existing state-of-the-art algorithms available for detection.