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TUTCRIS

A Deep Learning Approach for Vital Signs Compression and Energy Efficient Delivery in mhealth Systems

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Yksityiskohdat

AlkuperäiskieliEnglanti
Sivut33727-33739
Sivumäärä13
JulkaisuIEEE Access
Vuosikerta6
DOI - pysyväislinkit
TilaJulkaistu - 5 kesäkuuta 2018
Julkaistu ulkoisestiKyllä
OKM-julkaisutyyppiA1 Alkuperäisartikkeli

Tiivistelmä

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.

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