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

Sensors

卷:14

John J. Guiry¹  Pepijn van de Ven¹  John Nelson¹ 

[1] Department of Electronic & Computer Engineering, University of Limerick, Limerick, Ireland;

关键词: sensor fusion;    ubiquitous activity monitoring;    smart devices;    smartphone;    smartwatch;    geospatial awareness;    activities of daily living;   

DOI  :  10.3390/s140305687

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown