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

• Published in: Scientific programming Vol. 2019; no. 2019; pp. 1 - 10
• Main Authors: Leporati, Francesco, Guareschi, Federico, Musci, Mirto, Torti, Emanuele, Piastra, Marco
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2019; Hindawi; John Wiley & Sons, Inc
• Subjects: Accelerometers; Artificial intelligence; Deep learning; Design; Edge computing; Machine learning; Neural networks; Occupancy; Older people; Power consumption; Public health; Recurrent neural networks; Sensors; Signal classification; Smartphones; Statistical methods; Teaching methods; Wearable technology; Work stations
• ISSN: 1058-9244; 1875-919X
• DOI: 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.