Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150995
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dc.contributor.authorChen, Zhenghuaen_US
dc.contributor.authorJiang, Chaoyangen_US
dc.contributor.authorXie, Lihuaen_US
dc.date.accessioned2021-06-02T08:54:21Z-
dc.date.available2021-06-02T08:54:21Z-
dc.date.issued2018-
dc.identifier.citationChen, Z., Jiang, C. & Xie, L. (2018). A novel ensemble ELM for human activity recognition using smartphone sensors. IEEE Transactions On Industrial Informatics, 15(5), 2691-2699. https://dx.doi.org/10.1109/TII.2018.2869843en_US
dc.identifier.issn1551-3203en_US
dc.identifier.other0000-0002-1719-0328-
dc.identifier.other0000-0002-8669-2911-
dc.identifier.other0000-0002-7137-4136-
dc.identifier.urihttps://hdl.handle.net/10356/150995-
dc.description.abstractHuman activity recognition plays a unique role in many important applications, including ubiquitous computing, health-care services, and smart buildings. Due to the nonintrusive property of smartphones, smartphone sensors are widely used for the identification of human activities. Since the signals of smartphone sensors are quite noisy, feature engineering will be performed to extract more discriminant representations. Then, various machine learning algorithms can be employed to recognize different human activities. Extreme learning machine (ELM) has been shown to be effective in classification tasks with extremely fast learning speed. Due to its randomness property, it is naturally suitable for ensemble learning. In this paper, we propose a novel ensemble ELM algorithm for human activity recognition using smartphone sensors. Gaussian random projection is employed to initialize the input weights of base ELMs. By doing this, more diversities can be generated to boost the performance of ensemble learning. Real experimental data has been applied to evaluate the performance of our proposed approach. We also conduct a comparison of the proposed approach with some state-of-the-art approaches in the literature. The experimental results indicate that our proposed ensemble ELM approach outperforms these approaches and can achieve recognition accuracies of 97.35\% and 98.88\% on two datasets.en_US
dc.description.sponsorshipBuilding and Construction Authority (BCA)en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relationNRF2013EWTEIRP004-012en_US
dc.relation.ispartofIEEE Transactions on Industrial Informaticsen_US
dc.rights© 2018 IEEE. All rights reserved.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleA novel ensemble ELM for human activity recognition using smartphone sensorsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.organizationInstitute for Infocomm Research, A*STARen_US
dc.identifier.doi10.1109/TII.2018.2869843-
dc.identifier.scopus2-s2.0-85053326077-
dc.identifier.issue5en_US
dc.identifier.volume15en_US
dc.identifier.spage2691en_US
dc.identifier.epage2699en_US
dc.subject.keywordsEnsemble Extreme Learning Machineen_US
dc.subject.keywordsFeature Engineeringen_US
dc.description.acknowledgementThis work was supported by National Research Foundation and Building Construction Authority of Singapore under Grant NRF2013EWTEIRP004-012. Paper no. TII-18-1491. (Corresponding author: Chaoyang Jiang.)en_US
item.fulltextNo Fulltext-
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