Paper Info
Title
Identification of activities of daily living through data fusion on motion and magnetic sensors embedded on mobile devices.
Abstract
Several types of sensors have been available in off-the-shelf mobile devices, including motion, magnetic, vision, acoustic, and location sensors. This paper focuses on the fusion of the data acquired from motion and magnetic sensors, i.e., accelerometer, gyroscope and magnetometer sensors, for the recognition of Activities of Daily Living (ADL). Based on pattern recognition techniques, the system developed in this study includes data acquisition, data processing, data fusion, and classification methods like Artificial Neural Networks (ANN). Multiple settings of the ANN were implemented and evaluated in which the best accuracy obtained, with Deep Neural Networks (DNN), was 89.51%. This novel approach applies L2 regularization and normalization techniques on the sensors’ data proved it suitability and reliability for the ADL recognition.
Year
DOI
Venue
2018
10.1016/j.pmcj.2018.05.005
Pervasive and Mobile Computing
Keywords
Field
DocType
Mobile devices sensors,Sensor data fusion,Artificial neural networks,Identification of activities of daily living
Computer vision,Gyroscope,Data processing,Normalization (statistics),Computer science,Accelerometer,Data acquisition,Sensor fusion,Mobile device,Artificial intelligence,Artificial neural network,Distributed computing
Journal
Volume
ISSN
Citations 
47
1574-1192
1
PageRank 
References 
Authors
0.36
6
6
Name
Order
Citations
PageRank
Ivan Miguel Pires14617.56
Nuno M. Garcia28931.80
Nuno Pombo36517.31
Francisco Flórez-revuelta448134.95
Susanna Spinsante516735.64
Maria Cristina Canavarro Teixeira610.36