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Sensors 2017, 17(6), 1230; doi:10.3390/s17061230

Location-Enhanced Activity Recognition in Indoor Environments Using Off the Shelf Smart Watch Technology and BLE Beacons

Computing and Information Systems Department, University of Greenwich, Old Royal Naval College, Park Row, London SE10 9LS, UK
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Academic Editors: Oresti Banos, Hermie Hermens, Chris Nugent and Hector Pomares
Received: 2 April 2017 / Revised: 17 May 2017 / Accepted: 19 May 2017 / Published: 27 May 2017
(This article belongs to the Special Issue Smart Sensing Technologies for Personalised Coaching)
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Abstract

Activity recognition in indoor spaces benefits context awareness and improves the efficiency of applications related to personalised health monitoring, building energy management, security and safety. The majority of activity recognition frameworks, however, employ a network of specialised building sensors or a network of body-worn sensors. As this approach suffers with respect to practicality, we propose the use of commercial off-the-shelf devices. In this work, we design and evaluate an activity recognition system composed of a smart watch, which is enhanced with location information coming from Bluetooth Low Energy (BLE) beacons. We evaluate the performance of this approach for a variety of activities performed in an indoor laboratory environment, using four supervised machine learning algorithms. Our experimental results indicate that our location-enhanced activity recognition system is able to reach a classification accuracy ranging from 92% to 100%, while without location information classification accuracy it can drop to as low as 50% in some cases, depending on the window size chosen for data segmentation. View Full-Text
Keywords: activity recognition; wearable devices; inertial sensors; Bluetooth beacons; machine learning activity recognition; wearable devices; inertial sensors; Bluetooth beacons; machine learning
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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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MDPI and ACS Style

Filippoupolitis, A.; Oliff, W.; Takand, B.; Loukas, G. Location-Enhanced Activity Recognition in Indoor Environments Using Off the Shelf Smart Watch Technology and BLE Beacons. Sensors 2017, 17, 1230.

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