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AuthorSaid A.B.
AuthorMohamed A.
AuthorElfouly T.
AuthorAbualsaud K.
AuthorHarras K.
Available date2020-03-03T06:19:37Z
Publication Date2018
Publication Name2018 14th International Wireless Communications and Mobile Computing Conference, IWCMC 2018
URIhttp://dx.doi.org/10.1109/IWCMC.2018.8450434
URIhttp://hdl.handle.net/10576/13225
AbstractIn the context of mobile Health (mHealth) applications, data are prone to several sources of contamination which would lead to false interpretation and misleading classification results. In this paper, a robust deep learning approach with low rank model is proposed to classify mHealth vital signs. Further-more, we propose using the Schatten-p norm instead of the classic nuclear norm since it has shown better recovery performance for several applications. We conduct a comprehensive study where we compare our method to the state-of-art methods and evaluate its performance with respect to the key system parameters. Our findings show indeed that combining deep network with dictionary learning model is effective for vital signs classification even in presence of 50% corruption with 8% improvement over the closest performance.
SponsorThis publication was made possible by NPRP grant #7-684-1-127 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
dc.source Scopus
Subjectclassification
Subjectdeep learning
Subjectlow rank
SubjectmHealth
TitleUAV-based Semi-Autonomous Data Acquisition and Classification
TypeConference Paper
Pagination358 - 363


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