Deep Recurrent Neural Networks for Edge Monitoring of Personal Risk and Warning Situations

• Main Authors: Emanuele Torti, Mirto Musci, Federico Guareschi, Francesco Leporati, Marco Piastra
• Format: Article
• Language: English
• Published: Hindawi Limited 2019-01-01
• Series: Scientific Programming
• Online Access: http://dx.doi.org/10.1155/2019/9135196

Accidental falls are the main cause of fatal and nonfatal injuries, which typically lead to hospital admissions among elderly people. A wearable system capable of detecting unintentional falls and sending remote notifications will clearly improve the quality of the life of such subjects and also helps to reduce public health costs. In this paper, we describe an edge computing wearable system based on deep learning techniques. In particular, we give special attention to the description of the classification and communication modules, which have been developed by keeping in mind the limits in terms of computational power, memory occupancy, and power consumption of the designed wearable device. The system thus developed is capable of classifying 3D-accelerometer signals in real-time and to issue remote alerts while keeping power consumption low and improving on the present state-of-the-art solutions in the literature.