Abstract
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.
Highlights
Smart cities are built on the foundation of information and communication technologies with the sole purpose of connecting citizens and technology for the overall improvement of the quality of lives
Smart healthcare applications are becoming a part of daily life to prolong the lifetime of members of society and improve quality of life
Deep learning has evolved from the traditional artificial neural networks concept, it has become an evolving field with the advent of improved computational power, as well as the convergence of wired/wireless communication systems
Summary
Smart cities are built on the foundation of information and communication technologies with the sole purpose of connecting citizens and technology for the overall improvement of the quality of lives. In the same vein, Anthopoulus (see [4]) divides the smart city into the following eight components: (1) smart infrastructures where facilities utilize sensors and chips; (2) smart transportation where vehicular networks along with the communication infrastructure are deployed for monitoring purposes; (3) smart environments where ICTs are used in the monitoring of the environment to acquire useful information regarding environmental sustainability; (4) smart services where ICTs are used for the the provision of community health, tourism, education and safety; (5) smart governance, which aims at proper delivery of government services; (6) smart people that use ICTs to access and increase humans’ creativity; (7) smart living where technology is used for the improvement of the quality of life; and (8) smart economy, where businesses and organizations develop and grow through the use of technology Given these components, a smart health system within a smart city appears to be one of the leading gateways to a more productive and liveable structure that ensures the well-being of the community.
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