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Volume 8
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10.3390/electronics8121499
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Article

Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devices

by
Ivan Miguel Pires
1,2,*,†,
Gonçalo Marques
1,†,
Nuno M. Garcia
1,†,
Nuno Pombo
1,†,
Francisco Flórez-Revuelta
3,†,
Susanna Spinsante
4,†,
Maria Canavarro Teixeira
5,6,† and
Eftim Zdravevski
7,†
1
Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
2
Computer Science Department, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal
3
Department of Computing Technology, University of Alicante, P.O. Box 99, E-03080 Alicante, Spain
4
Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy
5
UTC de Recursos Naturais e Desenvolvimento Sustentável, Polytechnique Institute of Castelo Branco, 6001-909 Castelo Branco, Portugal
6
CERNAS-Research Centre for Natural Resources, Environment and Society, Polytechnique Institute of Castelo Branco, 6001-909 Castelo Branco, Portugal
7
Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, Macedonia
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Electronics 2019, 8(12), 1499; https://doi.org/10.3390/electronics8121499
Submission received: 23 November 2019 / Revised: 2 December 2019 / Accepted: 3 December 2019 / Published: 7 December 2019
(This article belongs to the Special Issue Machine Learning Techniques for Assistive Robotics)
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Abstract

The identification of Activities of Daily Living (ADL) is intrinsic with the user’s environment recognition. This detection can be executed through standard sensors present in every-day mobile devices. On the one hand, the main proposal is to recognize users’ environment and standing activities. On the other hand, these features are included in a framework for the ADL and environment identification. Therefore, this paper is divided into two parts—firstly, acoustic sensors are used for the collection of data towards the recognition of the environment and, secondly, the information of the environment recognized is fused with the information gathered by motion and magnetic sensors. The environment and ADL recognition are performed by pattern recognition techniques that aim for the development of a system, including data collection, processing, fusion and classification procedures. These classification techniques include distinctive types of Artificial Neural Networks (ANN), analyzing various implementations of ANN and choosing the most suitable for further inclusion in the following different stages of the developed system. The results present 85.89% accuracy using Deep Neural Networks (DNN) with normalized data for the ADL recognition and 86.50% accuracy using Feedforward Neural Networks (FNN) with non-normalized data for environment recognition. Furthermore, the tests conducted present 100% accuracy for standing activities recognition using DNN with normalized data, which is the most suited for the intended purpose.
Keywords: Activities of Daily Living (ADL); data fusion; environments; feature extraction; pattern recognition; sensors Activities of Daily Living (ADL); data fusion; environments; feature extraction; pattern recognition; sensors

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MDPI and ACS Style

Pires, I.M.; Marques, G.; Garcia, N.M.; Pombo, N.; Flórez-Revuelta, F.; Spinsante, S.; Teixeira, M.C.; Zdravevski, E. Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devices. Electronics 2019, 8, 1499. https://doi.org/10.3390/electronics8121499

AMA Style

Pires IM, Marques G, Garcia NM, Pombo N, Flórez-Revuelta F, Spinsante S, Teixeira MC, Zdravevski E. Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devices. Electronics. 2019; 8(12):1499. https://doi.org/10.3390/electronics8121499

Chicago/Turabian Style

Pires, Ivan Miguel, Gonçalo Marques, Nuno M. Garcia, Nuno Pombo, Francisco Flórez-Revuelta, Susanna Spinsante, Maria Canavarro Teixeira, and Eftim Zdravevski. 2019. "Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devices" Electronics 8, no. 12: 1499. https://doi.org/10.3390/electronics8121499

APA Style

Pires, I. M., Marques, G., Garcia, N. M., Pombo, N., Flórez-Revuelta, F., Spinsante, S., Teixeira, M. C., & Zdravevski, E. (2019). Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devices. Electronics, 8(12), 1499. https://doi.org/10.3390/electronics8121499

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