A Multiple Data Source Framework for the Identification of Activities of Daily Living Based on Mobile Device Data

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 di...

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Bibliographic Details
Published in:arXiv.org 2019-08
Main Authors: Ivan Miguel Pires, Garcia, Nuno M, Pombo, Nuno, Flórez-Revuelta, Francisco, Teixeira, Maria Canavarro, Zdravevski, Eftim, Spinsante, Susanna
Format: Article
Language:English
Subjects:
Accuracy
Acoustics
Activities of daily living
Artificial intelligence
Artificial neural networks
Data integration
Data processing
Electronic devices
Identification
Mobile communication systems
Monitoring
Movement
Multisensor fusion
Pattern recognition
Sensors
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Summary: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.
ISSN:2331-8422