Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/159353
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dc.contributor.authorQian, Hangweien_US
dc.contributor.authorPan, Sinno Jialinen_US
dc.contributor.authorMiao, Chunyanen_US
dc.date.accessioned2022-06-15T02:30:33Z-
dc.date.available2022-06-15T02:30:33Z-
dc.date.issued2021-
dc.identifier.citationQian, H., Pan, S. J. & Miao, C. (2021). Weakly-supervised sensor-based activity segmentation and recognition via learning from distributions. Artificial Intelligence, 292, 103429-. https://dx.doi.org/10.1016/j.artint.2020.103429en_US
dc.identifier.issn0004-3702en_US
dc.identifier.urihttps://hdl.handle.net/10356/159353-
dc.description.abstractSensor-based activity recognition aims to recognize users' activities from multi-dimensional streams of sensor readings received from ubiquitous sensors. It has been shown that data segmentation and feature extraction are two crucial steps in developing machine learning-based models for sensor-based activity recognition. However, most previous studies were only focused on the latter step by assuming that data segmentation is done in advance. In practice, on the one hand, doing data segmentation on sensory streams is very challenging. On the other hand, if data segmentation is considered as a pre-process, the errors in data segmentation may be propagated to latter steps. Therefore, in this paper, we propose a unified weakly-supervised framework based on kernel embedding of distributions to jointly segment sensor streams, extract powerful features from each segment, and train a final classifier for activity recognition. We further offer an accelerated version for large-scale data by utilizing the technique of random Fourier features. We conduct experiments on four benchmark datasets to verify the effectiveness and scalability of our proposed framework.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipMinistry of Health (MOH)en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relationM4081532.020en_US
dc.relation2018-T1-002-143en_US
dc.relationMOH/NIC/COG04/2017en_US
dc.relationMOH/NIC/HAIG03/2017en_US
dc.relation.ispartofArtificial Intelligenceen_US
dc.rights© 2020 Elsevier B.V. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleWeakly-supervised sensor-based activity segmentation and recognition via learning from distributionsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1016/j.artint.2020.103429-
dc.identifier.scopus2-s2.0-85097348312-
dc.identifier.volume292en_US
dc.identifier.spage103429en_US
dc.subject.keywordsHuman Activity Recognitionen_US
dc.subject.keywordsSensor Readings Segmentationen_US
dc.description.acknowledgementThis research is partially supported by the NTU Singapore Nanyang Assistant Professorship (NAP) grant M4081532.020, Singapore MOE AcRF Tier-1 grant 2018-T1-002-143, the National Research Foundation-Prime Minister’s office, Republic of Singapore under its IDM Futures Funding Initiative, the Singapore Ministry of Health under its National Innovation Challenge on Active and Confident Ageing (NIC Project No. MOH/NIC/COG04/2017 and MOH/NIC/HAIG03/2017), and the Interdisciplinary Graduate School, Nanyang Technological University under its Graduate Research Scholarship.en_US
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item.grantfulltextnone-
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