Subject: Mobile devices; Activities of Daily Living (ADL); sensors; data fusion; feature extraction; pattern recognition
Year: 2017
Type: Journal Article
Title: A Multiple Data Source Framework for the Identification of Activities of Daily Living Based on Mobile Device Data
Author: Pires, Ivan Miguel
Author: Garcia, Nuno
Author: Pombo, Nuno
Author: Flórez-Revuelta, Francisco
Author: Canavarro Teixeira, Maria
Author: Zdravevski, Eftim
Author: Spinsante, Susanna
Abstract: Most mobile devices include motion, magnetic, acoustic, and location sensors. They allow the implementation of a framework for the recognition of Activities of Daily Living (ADL) and its environments, composed by the acquisition, processing, fusion, and classification of data. This study compares different implementations of artificial neural networks, concluding that the obtained results were 85.89% and 100% for the recognition of standard ADL. Additionally, for the identification of standing activities with Deep Neural Networks (DNN) respectively, and 86.50% for the identification of the environments with Feedforward Neural Networks. Numerical results illustrate that the proposed framework can achieve robust performance from the data fusion of off-the-shelf mobile devices.
Publisher:
Relation: arXiv preprint arXiv:1711.00104
Identifier: oai:repository.ukim.mk:20.500.12188/21229
Identifier: http://hdl.handle.net/20.500.12188/21229
