Multi-Sensor Fusion for Enhanced Contextual Awareness of Everyday Activities with Ubiquitous Devices
Open Access
- 21 March 2014
- Vol. 14 (3), 5687-5701
- https://doi.org/10.3390/s140305687
Abstract
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.Keywords
This publication has 16 references indexed in Scilit:
- On the Capability of Smartphones to Perform as Communication Gateways in Medical Wireless Personal Area NetworksSensors, 2014
- Comprehensive Context Recognizer Based on Multimodal Sensors in a SmartphoneSensors, 2012
- Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancyThe Lancet, 2012
- Activity recognition using cell phone accelerometersACM SIGKDD Explorations Newsletter, 2011
- Forecasting the Future of Cardiovascular Disease in the United StatesCirculation, 2011
- Barometric Pressure and Triaxial Accelerometry-Based Falls Event DetectionIEEE Transactions on Neural Systems and Rehabilitation Engineering, 2010
- An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometerJournal of Applied Physiology, 2009
- Direct measurement of human movement by accelerometryMedical Engineering & Physics, 2008
- Economic burden of cardiovascular diseases in the enlarged European UnionEuropean Heart Journal, 2006
- Gait variability and fall risk in community-living older adults: A 1-year prospective studyArchives Of Physical Medicine and Rehabilitation, 2001