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J. Sens. Actuator Netw. 2017, 6(4), 26; doi:10.3390/jsan6040026

Big Sensed Data Meets Deep Learning for Smarter Health Care in Smart Cities

School of Electrical Engineering and Computer Science, University Ottawa, Ottawa, ON K1N 6N5, Canada
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Received: 20 October 2017 / Revised: 12 November 2017 / Accepted: 17 November 2017 / Published: 20 November 2017
(This article belongs to the Special Issue Sensors and Actuators in Smart Cities)
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With the advent of the Internet of Things (IoT) concept and its integration with the smart city sensing, smart connected health systems have appeared as integral components of the smart city services. Hard sensing-based data acquisition through wearables or invasive probes, coupled with soft sensing-based acquisition such as crowd-sensing results in hidden patterns in the aggregated sensor data. Recent research aims to address this challenge through many hidden perceptron layers in the conventional artificial neural networks, namely by deep learning. In this article, we review deep learning techniques that can be applied to sensed data to improve prediction and decision making in smart health services. Furthermore, we present a comparison and taxonomy of these methodologies based on types of sensors and sensed data. We further provide thorough discussions on the open issues and research challenges in each category. View Full-Text
Keywords: wearable sensors; biosensors; smart health; deep learning; machine learning; analytics wearable sensors; biosensors; smart health; deep learning; machine learning; analytics

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Obinikpo, A.A.; Kantarci, B. Big Sensed Data Meets Deep Learning for Smarter Health Care in Smart Cities. J. Sens. Actuator Netw. 2017, 6, 26.

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