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dc.contributor.author김기범-
dc.date.accessioned2020-02-14T01:43:22Z-
dc.date.available2020-02-14T01:43:22Z-
dc.date.issued2019-08-
dc.identifier.citation2019 International Conference on Applied and Engineering Mathematics (ICAEM), Article no. 8853770, Page. 145-150en_US
dc.identifier.isbn978-172812353-0-
dc.identifier.urihttps://ieeexplore.ieee.org/document/8853770-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/125308-
dc.description.abstractThe rapid growth of wearable sensors have increased the importance of human activity analysis in different areas of information technologies. Motion artifacts often degrade the performance of wearable sensors. Several wearable sensors have been used since the last decades in order to recognize physical activity detection. The wearable sensors could have numerous applications in medical and daily life routine activities like human gait analysis, health care, fitness, etc. In this paper, accelerometer and gyroscope sensors dataset has been used to propose an efficient model for physical activity detection. We designed a new feature extraction algorithm, Mel-frequency cepstral coefficient and statistical features to extract valuable features. Then, classification of different daily life activities is performed via Particle Swarm Optimization (PSO) together with SVM algorithm over bench mark motion-sense dataset. The results of our model shows that pre-classifier as PSO and SVM along with feature extraction module excel in term of accuracy and efficiency. Our experimental results have shown accuracy of 87.50% over motion-sense dataset. This model is recommended for the system associating in physical activity detection, especially in medical fitness field.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.publisherIEEEen_US
dc.subjectHuman activity analysisen_US
dc.subjectMedical fitnessen_US
dc.subjectMel-frequency cepstral coefficienten_US
dc.subjectSensor technologiesen_US
dc.titleSensors Technologies for Human Activity Analysis Based on SVM Optimized by PSO Algorithmen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICAEM.2019.8853770-
dc.relation.page145-150-
dc.contributor.googleauthorBatool, Mouazma-
dc.contributor.googleauthorJalal, Ahmad-
dc.contributor.googleauthorKim, Kibum-
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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COLLEGE OF COMPUTING[E] > MEDIA, CULTURE, AND DESIGN TECHNOLOGY(ICT융합학부) > Articles
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