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AuthorSaid, A.
AuthorSaid, Ahmed Ben
AuthorAl-Sa'D, Mohamed Fathi
AuthorTlili, Mounira
AuthorAbdellatif, Alaa Awad
AuthorMohamed, Amr
AuthorElfouly, Tarek
AuthorHarras, Khaled
AuthorO'Connor, Mark Dennis
Available date2019-09-18T07:55:29Z
Publication Date2018-06-05
Publication NameIEEE Access
Identifierhttp://dx.doi.org/10.1109/ACCESS.2018.2844308
CitationA. B. said et al., "A Deep Learning Approach for Vital Signs Compression and Energy Efficient Delivery in mhealth Systems," in IEEE Access, vol. 6, pp. 33727-33739, 2018. doi: 10.1109/ACCESS.2018.2844308
ISSN2169-3536
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85048183039&origin=inward
URIhttp://hdl.handle.net/10576/11885
Abstract© 2013 IEEE. Due to the increasing number of chronic disease patients, continuous health monitoring has become the top priority for health-care providers and has posed a major stimulus for the development of scalable and energy efficient mobile health systems. Collected data in such systems are highly critical and can be affected by wireless network conditions, which in return, motivates the need for a preprocessing stage that optimizes data delivery in an adaptive manner with respect to network dynamics. We present in this paper adaptive single and multiple modality data compression schemes based on deep learning approach, which consider acquired data characteristics and network dynamics for providing energy efficient data delivery. Results indicate that: 1) the proposed adaptive single modality compression scheme outperforms conventional compression methods by 13.24% and 43.75% reductions in distortion and processing time, respectively; 2) the proposed adaptive multiple modality compression further decreases the distortion by 3.71% and 72.37% when compared with the proposed single modality scheme and conventional methods through leveraging inter-modality correlations; and 3) adaptive multiple modality compression demonstrates its efficiency in terms of energy consumption, computational complexity, and responding to different network states. Hence, our approach is suitable for mobile health applications (mHealth), where the smart preprocessing of vital signs can enhance energy consumption, reduce storage, and cut down transmission delays to the mHealth cloud.
SponsorThis work was supported by NPRP through the Qatar National Research Fund (a member of the Qatar Foundation) under Grant 7-684-1-127.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Subjectcompression
cross-layer optimization
deep learning
multiple modality data
WBASN
TitleA Deep Learning Approach for Vital Signs Compression and Energy Efficient Delivery in mhealth Systems
TypeArticle
Pagination33727-33739
Volume Number6
dc.accessType Open Access


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