Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study
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, dat...
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doaj-1e67e6cd9c2d44e4847fc52414907afb2020-11-25T02:42:00ZengMDPI AGElectronics2079-92922020-01-019118010.3390/electronics9010180electronics9010180Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative StudyJosé M. Ferreira0Ivan Miguel Pires1Gonçalo Marques2Nuno M. García3Eftim Zdravevski4Petre Lameski5Francisco Flórez-Revuelta6Susanna Spinsante7Lina Xu8Computer Science Department, University of Beira Interior, 6200-001 Covilha, PortugalInstitute of Telecommunications, University of Beira Interior, 6200-001 Covilha, PortugalInstitute of Telecommunications, University of Beira Interior, 6200-001 Covilha, PortugalInstitute of Telecommunications, University of Beira Interior, 6200-001 Covilha, PortugalFaculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, MacedoniaFaculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, MacedoniaDepartment of Computing Technology, University of Alicante, P.O. Box 99, E-03080 Alicante, SpainDepartment of Information Engineering, Marche Polytechnic University, 60131 Ancona, ItalySchool of Computer Science, University College Dublin, Dublin 4, IrelandThe 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.https://www.mdpi.com/2079-9292/9/1/180activities of daily livingadaboostmobile devicesartificial neural networksdeep neural networks |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
José M. Ferreira Ivan Miguel Pires Gonçalo Marques Nuno M. García Eftim Zdravevski Petre Lameski Francisco Flórez-Revuelta Susanna Spinsante Lina Xu |
spellingShingle |
José M. Ferreira Ivan Miguel Pires Gonçalo Marques Nuno M. García Eftim Zdravevski Petre Lameski Francisco Flórez-Revuelta Susanna Spinsante Lina Xu Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study Electronics activities of daily living adaboost mobile devices artificial neural networks deep neural networks |
author_facet |
José M. Ferreira Ivan Miguel Pires Gonçalo Marques Nuno M. García Eftim Zdravevski Petre Lameski Francisco Flórez-Revuelta Susanna Spinsante Lina Xu |
author_sort |
José M. Ferreira |
title |
Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study |
title_short |
Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study |
title_full |
Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study |
title_fullStr |
Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study |
title_full_unstemmed |
Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study |
title_sort |
activities of daily living and environment recognition using mobile devices: a comparative study |
publisher |
MDPI AG |
series |
Electronics |
issn |
2079-9292 |
publishDate |
2020-01-01 |
description |
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. |
topic |
activities of daily living adaboost mobile devices artificial neural networks deep neural networks |
url |
https://www.mdpi.com/2079-9292/9/1/180 |
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