Name
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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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3266142 | ACM Transactions on Internet Technology (TOIT) |
Keywords | Field | DocType |
Human activity recognition, fog computing, mobile crowdsensing | Computer architecture,Smart environment,Activity recognition,Computer science,Activity tracker,Exploit,Mobile device,Bandwidth (signal processing),Wearable technology,Distributed computing,Cloud computing | Journal |
Volume | Issue | ISSN |
19 | 2 | 1533-5399 |
Citations | PageRank | References |
4 | 0.41 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Federico Concone | 1 | 11 | 2.68 |
| G. Lo Re | 2 | 38 | 6.18 |
| Marco Morana | 3 | 111 | 14.78 |