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Open AccessArticle

Multi-Sensor Fusion for Enhanced Contextual Awareness of Everyday Activities with Ubiquitous Devices

Department of Electronic & Computer Engineering, University of Limerick, Limerick, Ireland
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Sensors 2014, 14(3), 5687-5701; https://doi.org/10.3390/s140305687
Received: 26 January 2014 / Revised: 6 March 2014 / Accepted: 7 March 2014 / Published: 21 March 2014
(This article belongs to the Section Sensor Networks)
In this paper, the authors investigate the role that smart devices, including smartphones and smartwatches, can play in identifying activities of daily living. A feasibility study involving N = 10 participants was carried out to evaluate the devices’ ability to differentiate between nine everyday activities. The activities examined include walking, running, cycling, standing, sitting, elevator ascents, elevator descents, stair ascents and stair descents. The authors also evaluated the ability of these devices to differentiate indoors from outdoors, with the aim of enhancing contextual awareness. Data from this study was used to train and test five well known machine learning algorithms: C4.5, CART, Naïve Bayes, Multi-Layer Perceptrons and finally Support Vector Machines. Both single and multi-sensor approaches were examined to better understand the role each sensor in the device can play in unobtrusive activity recognition. The authors found overall results to be promising, with some models correctly classifying up to 100% of all instances. View Full-Text
Keywords: sensor fusion; ubiquitous activity monitoring; smart devices; smartphone; smartwatch; geospatial awareness; activities of daily living sensor fusion; ubiquitous activity monitoring; smart devices; smartphone; smartwatch; geospatial awareness; activities of daily living
MDPI and ACS Style

Guiry, J.J.; Van de Ven, P.; Nelson, J. Multi-Sensor Fusion for Enhanced Contextual Awareness of Everyday Activities with Ubiquitous Devices. Sensors 2014, 14, 5687-5701.

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