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dc.contributor.author김기범-
dc.date.accessioned2021-09-09T05:02:28Z-
dc.date.available2021-09-09T05:02:28Z-
dc.date.issued2020-12-
dc.identifier.citationSUSTAINABILITY, v. 12, no. 24, Article no. 10324, 21ppen_US
dc.identifier.issn2071-1050-
dc.identifier.urihttps://www.proquest.com/docview/2469956385?accountid=11283-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/165030-
dc.description.abstractHuman behavior modeling (HBM) is a challenging classification task for researchers seeking to develop sustainable systems that precisely monitor and record human life-logs. In recent years, several models have been proposed however, HBM remains an inspiring problem that is only partly solved. This paper proposes a novel framework of human behavior modeling based on wearable inertial sensors the system framework is composed of data acquisition, feature extraction, optimization and classification stages. First, inertial data is filtered via three different filters, i.e., Chebyshev, Elliptic and Bessel filters. Next, six different features from time and frequency domains are extracted to determine the maximum optimal values. Then, the Probability Based Incremental Learning (PBIL) optimizer and the K-Ary tree hashing classifier are applied to model different human activities. The proposed model is evaluated on two benchmark datasets, namely DALIAC and PAMPA2, and one self-annotated dataset, namely, IM-LifeLog, respectively. For evaluation, we used a leave-one-out cross validation scheme. The experimental results show that our model outperformed existing state-of-the-art methods with accuracy rates of 94.23%, 94.07% and 96.40% over DALIAC, PAMPA2 and IM-LifeLog datasets, respectively. The proposed system can be used in healthcare, physical activity detection, surveillance systems and medical fitness fields.en_US
dc.description.sponsorshipThis research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (no. 2018R1D1A1A02085645).en_US
dc.language.isoen_USen_US
dc.publisherMDPIen_US
dc.subjectDiscrete Hartely Transformen_US
dc.subjectinertial sensorsen_US
dc.subjectProbability Based Incremental Learningen_US
dc.subjectsustainable surveillance systemen_US
dc.subjectK-Ary tree hashing classifieren_US
dc.titleSustainable Wearable System: Human Behavior Modeling for Life-Logging Activities Using K-Ary Tree Hashing Classifieren_US
dc.typeArticleen_US
dc.relation.no24-
dc.relation.volume12-
dc.identifier.doi10.3390/su122410324-
dc.relation.page1-21-
dc.relation.journalSUSTAINABILITY-
dc.contributor.googleauthorJalal, Ahmad-
dc.contributor.googleauthorBatool, Mouazma-
dc.contributor.googleauthorKim, Kibum-
dc.relation.code2020057982-
dc.sector.campusE-
dc.sector.daehakCOLLEGE OF COMPUTING[E]-
dc.sector.departmentDIVISION OF MEDIA, CULTURE, AND DESIGN TECHNOLOGY-
dc.identifier.pidkibum-
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