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A smartphone accelerometer data-driven approach to recognize activities of daily life: A comparative study

2023-12Artigo científico Acesso aberto
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datacite.subject.fosCiências Médicas::Medicina Básica
datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.fosCiências Médicas::Ciências da Saúde
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg03:Saúde de Qualidade
dc.contributor.authorHussain, Faisal
dc.contributor.authorGoncalves, Norberto Jorge
dc.contributor.authorAlexandre, Daniel
dc.contributor.authorCoelho, Paulo Jorge
dc.contributor.authorAlbuquerque, Carlos
dc.contributor.authorLeithardt, Valderi Reis Quietinho
dc.contributor.authorPires, Ivan Miguel
dc.date.accessioned2026-10-07T14:53:24Z
dc.date.available2026-10-07T14:53:24Z
dc.date.issued2023-12
dc.description.abstractSmartphones have become an indispensable part of our everyday life, influencing various aspects of our routines, from wake-up alarms to managing daily life activities. Nowadays, almost every smartphone has a built-in accelerometer sensor. Motivated by the notable increase in smartphone usage in our everyday life, in this research, we focus on harnessing the potential of smartphone accelerometers to recognize human daily life activities, aiming to leverage the usability and convenience of smartphones. We used smartphone accelerometer data from data collection to daily life activity recognition. To accomplish this, we first collected the smartphone's accelerometer data while performing five activities of daily living (ADLs) namely: moving downstairs, upstairs, running, standing, and walking, from 25 volunteers through a mobile application. After this, we extracted 15 statistical features from the smartphone's accelerometer data to efficiently classify the five referred ADLs. We then applied data pre-processing techniques, i.e., data cleaning and feature extraction. Afterward, we trained nine commonly used machine learning models to recognize five ADLs. Finally, we evaluated and compared the performance of all nine ML models to recognize each activity and analyzed the performance of these trained ML models to identify all five ADLs. The evaluated results revealed that the Adaboost (AB) classifier outperformed all other ML models with 100% area under the curve (AUC), precision, recall, accuracy, and F1-score for recognizing the five ADLs.eng
dc.description.sponsorshipThis work is funded by FCT/MEC through national funds and co-funded by FEDER—PT2020 partnership agreement under the project UIDB/50008/2020.This work is also funded by National Funds through the FCT—Foundation for Science and Technology, I.P., within the scope of the project UIDB/00742/2020. This work is also funded by FCT/MEC through national funds and, when applicable, co-funded by the FEDER-PT2020 partnership agreement under the project UIDB/00308/2020. This article is based upon work from COST Action IC1303–AAPELE–Architectures, Algorithms and Protocols for Enhanced Living Environments and COST Action CA16226–SHELD-ON–Indoor living space improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology). More information is available at www.cost.eu.
dc.identifier.citationFaisal Hussain, Norberto Jorge Goncalves, Daniel Alexandre, Paulo Jorge Coelho, Carlos Albuquerque, Valderi Reis Quietinho Leithardt, Ivan Miguel Pires, A smartphone accelerometer data-driven approach to recognize activities of daily life: A comparative study, Smart Health, Volume 30, 2023, 100432, ISSN 2352-6483, https://doi.org/10.1016/j.smhl.2023.100432.
dc.identifier.doi10.1016/j.smhl.2023.100432
dc.identifier.eissn2352-6483
dc.identifier.urihttp://hdl.handle.net/10400.8/16985
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relationInstituto de Telecomunicações
dc.relationHealth Sciences Research Unit: Nursing
dc.relationInstitute for Systems Engineering and Computers at Coimbra - INESC Coimbra
dc.relation.hasversionhttps://www.sciencedirect.com/science/article/pii/S2352648323000600?via%3Dihub
dc.relation.ispartofSmart Health
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectHuman activities recognition
dc.subjectActivities recognition
dc.subjectDaily life activities
dc.subjectHuman activities detection
dc.subjectMachine learning
dc.subjectWearable sensors
dc.titleA smartphone accelerometer data-driven approach to recognize activities of daily life: A comparative studyeng
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberUIDB/50008/2020
oaire.awardNumberUIDB/00742/2020
oaire.awardNumberUIDB/00308/2020
oaire.awardTitleInstituto de Telecomunicações
oaire.awardTitleHealth Sciences Research Unit: Nursing
oaire.awardTitleInstitute for Systems Engineering and Computers at Coimbra - INESC Coimbra
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50008%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00742%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00308%2F2020/PT
oaire.citation.titleSmart Health
oaire.citation.volume30
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameCoelho
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person.identifier2068530
person.identifier.ciencia-id3818-FA4F-CC36
person.identifier.orcid0000-0002-4383-0472
person.identifier.ridV-1924-2018
person.identifier.scopus-author-id57128835100
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
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Smartphones have become an indispensable part of our everyday life, influencing various aspects of our routines, from wake-up alarms to managing daily life activities. Nowadays, almost every smartphone has a built-in accelerometer sensor. Motivated by the notable increase in smartphone usage in our everyday life, in this research, we focus on harnessing the potential of smartphone accelerometers to recognize human daily life activities, aiming to leverage the usability and convenience of smartphones. We used smartphone accelerometer data from data collection to daily life activity recognition. To accomplish this, we first collected the smartphone's accelerometer data while performing five activities of daily living (ADLs) namely: moving downstairs, upstairs, running, standing, and walking, from 25 volunteers through a mobile application. After this, we extracted 15 statistical features from the smartphone's accelerometer data to efficiently classify the five referred ADLs. We then applied data pre-processing techniques, i.e., data cleaning and feature extraction. Afterward, we trained nine commonly used machine learning models to recognize five ADLs. Finally, we evaluated and compared the performance of all nine ML models to recognize each activity and analyzed the performance of these trained ML models to identify all five ADLs. The evaluated results revealed that the Adaboost (AB) classifier outperformed all other ML models with 100% area under the curve (AUC), precision, recall, accuracy, and F1-score for recognizing the five ADLs.
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