Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer
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 thei...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/9/3/509 |
| _version_ | 1811186422852354048 |
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| author | Ivan Miguel Pires Gonçalo Marques Nuno M. Garcia Francisco Flórez-Revuelta Maria Canavarro Teixeira Eftim Zdravevski Susanna Spinsante Miguel Coimbra |
| author_facet | Ivan Miguel Pires Gonçalo Marques Nuno M. Garcia Francisco Flórez-Revuelta Maria Canavarro Teixeira Eftim Zdravevski Susanna Spinsante Miguel Coimbra |
| author_sort | Ivan Miguel Pires |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-04-11T13:46:12Z |
| format | Article |
| id | doaj.art-76d5e14888834e51a3cfce8d8ff711cc |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-11T13:46:12Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-76d5e14888834e51a3cfce8d8ff711cc2022-12-22T04:21:05ZengMDPI AGElectronics2079-92922020-03-019350910.3390/electronics9030509electronics9030509Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device AccelerometerIvan Miguel Pires0Gonçalo Marques1Nuno M. Garcia2Francisco Flórez-Revuelta3Maria Canavarro Teixeira4Eftim Zdravevski5Susanna Spinsante6Miguel Coimbra7Computer Science Department, Polytechnic Institute of Viseu, 3504-510 Viseu, PortugalInstituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, PortugalInstituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, PortugalDepartment of Computing Technology, University of Alicante, P.O. Box 99, E-03080 Alicante, SpainUTC de Recursos Naturais e Desenvolvimento Sustentável, Polytechnique Institute of Castelo Branco, 6001-909 Castelo Branco, PortugalFaculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, MacedoniaDepartment of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, ItalyInstituto de Telecomunicações, Faculdade de Ciências da Universidade do Porto, 4169-007 Porto, PortugalThe 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).https://www.mdpi.com/2079-9292/9/3/509accelerometeractivities of daily livingmobile devicessensors |
| spellingShingle | Ivan Miguel Pires Gonçalo Marques Nuno M. Garcia Francisco Flórez-Revuelta Maria Canavarro Teixeira Eftim Zdravevski Susanna Spinsante Miguel Coimbra Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer Electronics accelerometer activities of daily living mobile devices sensors |
| title | Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer |
| title_full | Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer |
| title_fullStr | Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer |
| title_full_unstemmed | Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer |
| title_short | Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer |
| title_sort | pattern recognition techniques for the identification of activities of daily living using a mobile device accelerometer |
| topic | accelerometer activities of daily living mobile devices sensors |
| url | https://www.mdpi.com/2079-9292/9/3/509 |
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