2017
DOI: 10.3390/s17061230
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Location-Enhanced Activity Recognition in Indoor Environments Using Off the Shelf Smart Watch Technology and BLE Beacons

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 … Show more

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Cited by 30 publications
(19 citation statements)
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“…Additionally, the authors introduce multi-lateration along with particle filter, to estimate the final position. The authors in [ 39 ] use BLE for activity recognition, relating the location of the user with a particular activity.…”
Section: Related Workmentioning
confidence: 99%
“…Additionally, the authors introduce multi-lateration along with particle filter, to estimate the final position. The authors in [ 39 ] use BLE for activity recognition, relating the location of the user with a particular activity.…”
Section: Related Workmentioning
confidence: 99%
“…User location In-home Out-of-home Dionisi et al [8] x x Pärkkä et al [32] x x Lara et al [24] x x Reddy et al [34] x x Di Francesco et al [7] x Zhu et al [51] x x Wang et al [48] x Sharma et al [37] x Filippoupolitis et al [13] x x Ishimaru et al [19] x…”
Section: Vital Signsmentioning
confidence: 99%
“…For the best or our knowledge, few works have fused the information on indoor user location with wearable systems to increase the performance of the recognition tasks. Filippoupolitis et al [13] used smartwatch acceleration data and BLE network to estimate indoor user location to recognize eight different activities usually performed by a technical support staff member such as typing, scanning, installing or assembling. Nevertheless, it is important to focus the attention also on daily activities to identify potential dangerous situation which can occur during daily life.…”
Section: Vital Signsmentioning
confidence: 99%
“…Other approaches propose the merge of signals with contextual information; for instance, Chun Zu et al [23] proposed an approach to indoor human daily activity recognition which combines motion data and location information, where location is a context information and an accelerometer provides raw data from the user movements using Bayes' theorem to fuse the context and accelerometer data. In their work, Avgoustinos Filippoupolitis et al [24] designed and evaluated an activity recognition system composed of a smart watch, enhanced by contextual location information acquired from Bluetooth Low Energy (BLE) beacons. They claimed a classification accuracy ranging from 92% to 100%.…”
Section: Introductionmentioning
confidence: 99%