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Authors: Ivan Miguel Pires 1 ; Nuno M. Garcia 2 ; Nuno Pombo 2 and Francisco Flórez-Revuelta 3

Affiliations: 1 Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal, Altranportugal, Lisbon, Portugal, ALLab - Assisted Living Computing and Telecommunications Laboratory, Computer Science Department, Universidade da Beira Interior, Covilhã and Portugal ; 2 Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã and Portugal ; 3 Department of Computer Technology, Universidad de Alicante and Spain

ISBN: 978-989-758-318-6

Keyword(s): Activities of Daily Living, Sensors, Mobile Devices, Data Fusion, Feature Extraction, Pattern Recognition.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Business Analytics ; Data Engineering ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Predictive Modeling ; Sensor Networks ; Soft Computing

Abstract: Due to the commodity of the use of the off-the-shelf mobile devices and technological devices by ageing people, the automatic recognition of the Activities of Daily Living (ADL) and their environments using these devices is a research topic were studied in the last years, but this project consists in the creation of an automatic method that recognizes a defined dataset of ADL using a large set of sensors available in these devices, such as the accelerometer, the gyroscope, the magnetometer, the microphone and the Global Positioning System (GPS) receiver. The fusion of the data acquired from the selected sensors allows the recognition of an increasing number of ADL and environments, where the ADL are mainly recognized with motion, magnetic and location sensors, but the environments are mainly recognized with acoustic sensors. During this project, several methods have been researched in the literature, implementing three types of neural networks, these are Multilayer Perceptron (MLP) wi th Backpropagation, Feedforward neural network (FNN) with Backpropagation and Deep Neural Networks (DNN), verifying that the neural networks that report highest results are the DNN method for the recognition of ADL and standing activities, and the FNN method for the recognition of environments. (More)

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Paper citation in several formats:
Miguel Pires, I.; M. Garcia, N.; Pombo, N. and Flórez-Revuelta, F. (2018). Framework for the Recognition of Activities of Daily Living and Their Environments in the Development of a Personal Digital Life Coach.In Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-318-6, pages 163-170. DOI: 10.5220/0006824301630170

@conference{data18,
author={Ivan Miguel Pires. and Nuno M. Garcia. and Nuno Pombo. and Francisco Flórez{-}Revuelta.},
title={Framework for the Recognition of Activities of Daily Living and Their Environments in the Development of a Personal Digital Life Coach},
booktitle={Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2018},
pages={163-170},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006824301630170},
isbn={978-989-758-318-6},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Framework for the Recognition of Activities of Daily Living and Their Environments in the Development of a Personal Digital Life Coach
SN - 978-989-758-318-6
AU - Miguel Pires, I.
AU - M. Garcia, N.
AU - Pombo, N.
AU - Flórez-Revuelta, F.
PY - 2018
SP - 163
EP - 170
DO - 10.5220/0006824301630170

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