**Avgoustinos Filippoupolitis \*, William Oliff, Babak Takand and George Loukas**

Computing and Information Systems Department, University of Greenwich, Old Royal Naval College, Park Row, London SE10 9LS, UK; william.oliff@gre.ac.uk (W.O.); b.takand@gre.ac.uk (B.T.); g.loukas@gre.ac.uk (G.L.)

**\*** Correspondence: a.filippoupolitis@gre.ac.uk

Academic Editors: Oresti Banos, Hermie Hermens, Chris Nugent and Hector Pomares Received: 2 April 2017; Accepted: 19 May 2017; Published: 27 May 2017

**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.

**Keywords:** activity recognition; wearable devices; inertial sensors; Bluetooth beacons; machine learning
