Peer-Reviewed Journal Details
Mandatory Fields
Guiry, JJ,van de Ven, P,Nelson, J
2014
March
Sensors
Multi-Sensor Fusion for Enhanced Contextual Awareness of Everyday Activities with Ubiquitous Devices
Published
()
Optional Fields
sensor fusion ubiquitous activity monitoring smart devices smartphone smartwatch geospatial awareness activities of daily living MOBILE PHONES LIFE ACCELEROMETRY DISEASES SENSORS BURDEN GAIT RISK
14
5687
5701
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, Na ve 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.
10.3390/s140305687
Grant Details