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Android Library for Recognition of Activities of Daily Living: Implementation Considerations, Challenges, and Solutions

2018Journal article Open access
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dc.contributor.authorPires, Ivan
dc.contributor.authorTeixeira, Maria Cristina Canavarro
dc.contributor.authorPombo, Nuno
dc.contributor.authorGarcia, Nuno M.
dc.contributor.authorFlórez-Revuelta, Francisco
dc.contributor.authorSpinsante, Susanna
dc.contributor.authorGoleva, Rossitza
dc.contributor.authorZdravevski, Eftim
dc.date.accessioned2020-01-14T16:19:04Z
dc.date.available2020-01-14T16:19:04Z
dc.date.issued2018
dc.description.abstractBackground: Off-the-shelf-mobile devices have several sensors available onboard that may be used for the recognition of Activities of Daily Living (ADL) and the environments where they are performed. This research is focused on the development of Ambient Assisted Living (AAL) systems, using mobile devices for the acquisition of the different types of data related to the physical and physiological conditions of the subjects and the environments. Mobile devices with the Android Operating Systems are the least expensive and exhibit the biggest market while providing a variety of models and onboard sensors. Objective: This paper describes the implementation considerations, challenges and solutions about a framework for the recognition of ADL and the environments, provided as an Android library. The framework is a function of the number of sensors available in different mobile devices and utilizes a variety of activity recognition algorithms to provide a rapid feedback to the user. Methods: The Android library includes data fusion, data processing, features engineering and classification methods. The sensors that may be used are the accelerometer, the gyroscope, the magnetometer, the Global Positioning System (GPS) receiver and the microphone. The data processing includes the application of data cleaning methods and the extraction of features, which are used with Deep Neural Networks (DNN) for the classification of ADL and environment. Throughout this work, the limitations of the mobile devices were explored and their effects have been minimized. Results: The implementation of the Android library reported an overall accuracy between 58.02% and 89.15%, depending on the number of sensors used and the number of ADL and environments recognized. Compared with the results available in the literature, the performance of the library reported a mean improvement of 2.93%, and they do not differ at the maximum found in prior work, that based on the Student’s t-test. Conclusion: This study proves that ADL like walking, going upstairs and downstairs, running, watching TV, driving, sleeping and standing activities, and the bedroom, cooking/kitchen, gym, classroom, hall, living room, bar, library and street environments may be recognized with the sensors available in off-the-shelf mobile devices. Finally, these results may act as a preliminary research for the development of a personal digital life coach with a multi-sensor mobile device commonly used daily.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.2174/1875036201811010061pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.6/8265
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.subjectActivities of daily livingpt_PT
dc.subjectSensorspt_PT
dc.subjectMobile devicespt_PT
dc.subjectPattern recognitionpt_PT
dc.subjectData fusionpt_PT
dc.subjectAndroid librarypt_PT
dc.subjectArtificial neural networkspt_PT
dc.subjectRecognitionpt_PT
dc.titleAndroid Library for Recognition of Activities of Daily Living: Implementation Considerations, Challenges, and Solutionspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage88pt_PT
oaire.citation.issue1pt_PT
oaire.citation.startPage61pt_PT
oaire.citation.titleThe Open Bioinformatics Journalpt_PT
oaire.citation.volume11pt_PT
person.familyNameSerrano Pires
person.familyNameCanavarro
person.familyNamePombo
person.familyNameGarcia dos Santos
person.familyNameFlórez-Revuelta
person.familyNameSpinsante
person.familyNameGoleva
person.familyNameZdravevski
person.givenNameIvan Miguel
person.givenNameCristina
person.givenNameNuno
person.givenNameNuno Manuel
person.givenNameFrancisco
person.givenNameSusanna
person.givenNameRossitza
person.givenNameEftim
person.identifier-6iey0oAAAAJ
person.identifier.ciencia-id211D-8B3D-0131
person.identifier.ciencia-id0913-BC21-F66E
person.identifier.ciencia-id0F16-A18D-96BA
person.identifier.ciencia-idE719-0DEC-9751
person.identifier.orcid0000-0002-3394-6762
person.identifier.orcid0000-0002-8534-9484
person.identifier.orcid0000-0001-7797-8849
person.identifier.orcid0000-0002-3195-3168
person.identifier.orcid0000-0002-3391-711X
person.identifier.orcid0000-0002-7323-4030
person.identifier.orcid0000-0002-6268-0756
person.identifier.orcid0000-0001-7664-0168
person.identifier.ridJ-3370-2013
person.identifier.ridP-5437-2014
person.identifier.ridC-2465-2016
person.identifier.ridK-5276-2014
person.identifier.scopus-author-id56715367700
person.identifier.scopus-author-id55832925000
person.identifier.scopus-author-id55389546100
person.identifier.scopus-author-id13106226300
person.identifier.scopus-author-id6506113067
person.identifier.scopus-author-id55376768000
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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