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Open AccessArticle

Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer

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Computer Science Department, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal
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Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
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Department of Computing Technology, University of Alicante, P.O. Box 99, E-03080 Alicante, Spain
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UTC de Recursos Naturais e Desenvolvimento Sustentável, Polytechnique Institute of Castelo Branco, 6001-909 Castelo Branco, Portugal
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CERNAS—Research Centre for Natural Resources, Environment and Society, Polytechnique Institute of Castelo Branco, 6001-909 Castelo Branco, Portugal
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Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, Macedonia
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Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy
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Instituto de Telecomunicações, Faculdade de Ciências da Universidade do Porto, 4169-007 Porto, Portugal
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Electronics 2020, 9(3), 509; https://doi.org/10.3390/electronics9030509
Received: 5 February 2020 / Revised: 4 March 2020 / Accepted: 14 March 2020 / Published: 19 March 2020
(This article belongs to the Special Issue Machine Learning Techniques for Assistive Robotics)
The application of pattern recognition techniques to data collected from accelerometers available in off-the-shelf devices, such as smartphones, allows for the automatic recognition of activities of daily living (ADLs). This data can be used later to create systems that monitor the behaviors of their users. The main contribution of this paper is to use artificial neural networks (ANN) for the recognition of ADLs with the data acquired from the sensors available in mobile devices. Firstly, before ANN training, the mobile device is used for data collection. After training, mobile devices are used to apply an ANN previously trained for the ADLs’ identification on a less restrictive computational platform. The motivation is to verify whether the overfitting problem can be solved using only the accelerometer data, which also requires less computational resources and reduces the energy expenditure of the mobile device when compared with the use of multiple sensors. This paper presents a method based on ANN for the recognition of a defined set of ADLs. It provides a comparative study of different implementations of ANN to choose the most appropriate method for ADLs identification. The results show the accuracy of 85.89% using deep neural networks (DNN). View Full-Text
Keywords: accelerometer; activities of daily living; mobile devices; sensors accelerometer; activities of daily living; mobile devices; sensors
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MDPI and ACS Style

Pires, I.M.; Marques, G.; Garcia, N.M.; Flórez-Revuelta, F.; Canavarro Teixeira, M.; Zdravevski, E.; Spinsante, S.; Coimbra, M. Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer. Electronics 2020, 9, 509.

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