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Open AccessArticle

Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study

1
Computer Science Department, University of Beira Interior, 6200-001 Covilha, Portugal
2
Institute of Telecommunications, University of Beira Interior, 6200-001 Covilha, Portugal
3
Computer Science Department, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal
4
Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, Macedonia
5
Department of Computing Technology, University of Alicante, P.O. Box 99, E-03080 Alicante, Spain
6
Department of Information Engineering, Marche Polytechnic University, 60131 Ancona, Italy
7
School of Computer Science, University College Dublin, Dublin 4, Ireland
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Electronics 2020, 9(1), 180; https://doi.org/10.3390/electronics9010180
Received: 18 December 2019 / Revised: 9 January 2020 / Accepted: 14 January 2020 / Published: 18 January 2020
(This article belongs to the Special Issue Machine Learning Techniques for Assistive Robotics)
The recognition of Activities of Daily Living (ADL) using the sensors available in off-the-shelf mobile devices with high accuracy is significant for the development of their framework. Previously, a framework that comprehends data acquisition, data processing, data cleaning, feature extraction, data fusion, and data classification was proposed. However, the results may be improved with the implementation of other methods. Similar to the initial proposal of the framework, this paper proposes the recognition of eight ADL, e.g., walking, running, standing, going upstairs, going downstairs, driving, sleeping, and watching television, and nine environments, e.g., bar, hall, kitchen, library, street, bedroom, living room, gym, and classroom, but using the Instance Based k-nearest neighbour (IBk) and AdaBoost methods as well. The primary purpose of this paper is to find the best machine learning method for ADL and environment recognition. The results obtained show that IBk and AdaBoost reported better results, with complex data than the deep neural network methods. View Full-Text
Keywords: activities of daily living; AdaBoost; mobile devices; artificial neural networks; deep neural networks activities of daily living; AdaBoost; mobile devices; artificial neural networks; deep neural networks
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Ferreira, J.M.; Pires, I.M.; Marques, G.; García, N.M.; Zdravevski, E.; Lameski, P.; Flórez-Revuelta, F.; Spinsante, S.; Xu, L. Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study. Electronics 2020, 9, 180.

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