A Multiple Data Source Framework for the Identification of Activities of Daily Living Based on Mobile Device Data
Journal
arXiv preprint arXiv:1711.00104
Date Issued
2017-10-31
Author(s)
Pires, Ivan Miguel
Garcia, Nuno
Pombo, Nuno
Flórez-Revuelta, Francisco
Canavarro Teixeira, Maria
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.
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.
Subjects
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