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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 M.
dc.contributor.authorTeixeira, M.C.C.
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.accessioned2024-05-29T10:12:01Z
dc.date.available2024-05-29T10:12:01Z
dc.date.issued2018
dc.descriptionThe authors would also like to acknowledge the contribution of the COST Action IC1303 – AAPELE – Architectures, Algorithms and Protocols for Enhanced Living Environments.pt_PT
dc.description.abstractOff-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.sponsorshipThis work was supported by FCT project UID/EEA/50008/2013 (Este trabalho foi suportado pelo projecto FCT UID/EEA/50008/2013).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationPIRES, Ivan M. [et al.] (2018) - Android library for recognition of activities of daily living: implementation considerations, challenges, and solutions. The Open Bioinformatics Journal. 11, p. 61-88. DOI: https://doi.org/10.2174/1875036201811010061pt_PT
dc.identifier.doihttps://doi.org/10.2174/1875036201811010061pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.11/9010
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherBenthampt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_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.issue11pt_PT
oaire.citation.startPage61pt_PT
oaire.citation.titleThe Open Bioinformatics Journalpt_PT
person.familyNameSerrano Pires
person.familyNameCanavarro Teixeira
person.familyNameGarcia dos Santos
person.familyNameZdravevski
person.givenNameIvan Miguel
person.givenNameMaria Cristina
person.givenNameNuno Manuel
person.givenNameEftim
person.identifier.ciencia-id211D-8B3D-0131
person.identifier.ciencia-id0913-BC21-F66E
person.identifier.ciencia-idE719-0DEC-9751
person.identifier.orcid0000-0002-3394-6762
person.identifier.orcid0000-0002-8534-9484
person.identifier.orcid0000-0002-3195-3168
person.identifier.orcid0000-0001-7664-0168
person.identifier.ridK-5276-2014
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication5c53ae6c-7865-4ef1-adea-ea2fa221aee4
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relation.isAuthorOfPublication.latestForDiscovery5c53ae6c-7865-4ef1-adea-ea2fa221aee4

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