![]() This is most meaningful for geriatric patients 1, 2 that are more likely to suffer lasting injuries from a fall, especially if treatment is delayed. ![]() Real-time activity recognition in a hospital room or nursing home is important, because it can help to detect troublesome events, such as the fall of a patient, as soon as possible. This enables fast notifications to staff if troublesome activities occur (such as falling) by the on-premise device, while the off-premise device captures activities missed or misclassified by the on-premise device. Next, a part of the calculation and the prediction is sent to a more capable off-premise machine (most likely in the cloud or a data center) where a backward RNN calculation is performed that improves the previous prediction sent by the on-premise device. First, a forward Recurrent Neural Network (RNN) calculation is performed on an on-premise device (usually close to the radar sensor) which already gives a prediction of what activity is performed, and can be used for time-sensitive use-cases. This work presents a framework that splits the processing of data in two parts. Often these networks are large and do not scale well when processing a large amount of radar streams at once, for example when monitoring multiple rooms in a hospital. This can be achieved by using Deep Neural Networks, which are able to effectively process the complex radar data. Zhu Bin, Jin Wei-dong (2012) Radar emitter signal recognition based on EMD and neural network.Radar systems can be used to perform human activity recognition in a privacy preserving manner. In: CIE’06 international conference on radar Yuan X, Huang G, Zhang Q (2006) Implementation of radar emitter intelligent recognition system based on neural network. Wu Z, Yang Z, Yin Z, Zuo L, Gao H (2012) A novel RBF neural network for radar emitter recognition based on rough sets. Wiley RG (1982) ELINT, the interception and analysis of radar signals. Cambridge University Press, New York, pp 109–130 Spanos A (1999) Probability theory and statistical inference. ![]() Shieh CS, Lin CT (2002) A vector neural network for emitter identification. Sato A, Huang R, Yen NY (2015) Design of fusion technique-based mining engine for smart business. Pincus SM, Gladstone IM, Ehrenkranz RA (1991) A regularity statistic for medical data analysis. Petrov N, Jordanov IN, Roe J (2013) Radar emitter signals recognition and classification with feedforward networks. Lin CM, Chen YM (2014) A self-organizing interval type-2 fuzzy neural network for radar emitter identification. In: Third annual conference on advances in cognitive systems Lee-Urban S, Trewhitt E, Bieder I, Odom J, Boone T, Whitaker E (2015) CORA: A flexible hybrid approach to building cognitive systems. Keegan N, Ji S-Y, Chaudhary A, Concolato C, Yu B (2016) A survey of cloud-based network intrusion detection analysis. Granger E, Rubin MA, Grossberg S, Lavoie P (2001) A what-and-where fusion neural network for recognition and tracking of multiple radar emitters. ![]() In: MILCOMĬho YS, Moon SC (2015) Recommender system using periodicity analysis via mining sequential patterns with time-series and FART analysis. In: International conference on MICROWAVEĪrik M, Akan OB (2015) Enabling cognition on electronic counter measure systems against next-generation radars. Anjaneyulu L, Murthy NS, Sarma N (2008) Radar emitter classification using self-organising neural network models. ![]()
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