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A Fog-Based Application for Human Activity Recognition Using Personal Smart Devices

Published:28 March 2019Publication History
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Abstract

The diffusion of heterogeneous smart devices capable of capturing and analysing data about users, and/or the environment, has encouraged the growth of novel sensing methodologies. One of the most attractive scenarios in which such devices, such as smartphones, tablet computers, or activity trackers, can be exploited to infer relevant information is human activity recognition (HAR). Even though some simple HAR techniques can be directly implemented on mobile devices, in some cases, such as when complex activities need to be analysed timely, users’ smart devices can operate as part of a more complex architecture. In this article, we propose a multi-device HAR framework that exploits the fog computing paradigm to move heavy computation from the sensing layer to intermediate devices and then to the cloud. As compared to traditional cloud-based solutions, this choice allows to overcome processing and storage limitations of wearable devices while also reducing the overall bandwidth consumption. Experimental analysis aims to evaluate the performance of the entire platform in terms of accuracy of the recognition process while also highlighting the benefits it might bring in smart environments.

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          • Published in

            cover image ACM Transactions on Internet Technology
            ACM Transactions on Internet Technology  Volume 19, Issue 2
            Special Issue on Fog, Edge, and Cloud Integration
            May 2019
            288 pages
            ISSN:1533-5399
            EISSN:1557-6051
            DOI:10.1145/3322882
            • Editor:
            • Ling Liu
            Issue’s Table of Contents

            Copyright © 2019 ACM

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 28 March 2019
            • Accepted: 1 August 2018
            • Revised: 1 July 2018
            • Received: 1 December 2017
            Published in toit Volume 19, Issue 2

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