Pattern recognition techniques for the identification of Activities of Daily Living using mobile device accelerometer

Ivan Miguel Pires ^1,2,3, Nuno M. Garcia ¹, Nuno Pombo¹, Francisco Flórez-Revuelta⁴, Susanna Spinsante⁵, Maria Canavarro Teixeira^6,7, Eftim Zdravevski⁸

1 Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal

2 Altranportugal, Lisbon, Portugal

3 ALLab - Assisted Living Computing and Telecommunication Laboratory, Computer Science Department, Universidade da Beira Interior, Covilhã, Portugal

4 Department of Computer Technology, Universidad de Alicante, Alicante, Spain

5 Università Politecnica delle Marche, Ancona, Italy

6 UTC de Recursos Naturais e Desenvolvimento Sustentável, Instituto Politécnico de Castelo Branco, Castelo Branco, Portugal

7 CERNAS - Research Centre for Natural Resources, Environment and Society, Instituto Politécnico de Castelo Branco, Castelo Branco, Portugal

8 Faculty of Computer Science and Engineering, St. Cyril and Methodius University, Skopje, Macedonia

DOI: 10.7287/peerj.preprints.27225v2

Published: 2019-02-12
Accepted: 2019-02-12

Subject Areas: Algorithms and Analysis of Algorithms, Artificial Intelligence, Data Mining and Machine Learning, Data Science, Mobile and Ubiquitous Computing
Keywords: Activities of Daily Living (ADL), sensors, mobile devices, feature extraction, accelerometer, pattern recognition, machine learning, data processing, data cleaning, data acquisition

Licence: This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.

Cite this article: Pires IM, Garcia NM, Pombo N, Flórez-Revuelta F, Spinsante S, Canavarro Teixeira M, Zdravevski E. 2019. Pattern recognition techniques for the identification of Activities of Daily Living using mobile device accelerometer. PeerJ Preprints 7:e27225v2 https://doi.org/10.7287/peerj.preprints.27225v2

Abstract

This paper focuses on the recognition of Activities of Daily Living (ADL) applying pattern recognition techniques to the data acquired by the accelerometer available in the mobile devices. The recognition of ADL is composed by several stages, including data acquisition, data processing, and artificial intelligence methods. The artificial intelligence methods used are related to pattern recognition, and this study focuses on the use of Artificial Neural Networks (ANN). The data processing includes data cleaning, and the feature extraction techniques to define the inputs for the ANN. Due to the low processing power and memory of the mobile devices, they should be mainly used to acquire the data, applying an ANN previously trained for the identification of the ADL. The main purpose of this paper is to present a new method based on ANN for the identification of a defined set of ADL with a reliable accuracy. This paper also presents a comparison of different types of ANN in order to choose the type for the implementation of the final model. Results of this research probes that the best accuracies are achieved with Deep Neural Networks (DNN) with an accuracy higher than 80%. The results obtained are similar with other studies, but we compared tree types of ANN in order to discover the best method in order to obtain these results with less memory, verifying that, after the generation of the model, the DNN method, when compared with others, is also the fastest to obtain the results with better accuracy.

Author Comment

Manuscript revised with the comments from the previous submission. Rewrite some sections to clarify.

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