Plug and play self-configurable IoT gateway node for telemonitoring of ECG.

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ID: 60485
2019
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Abstract
In the era of IoT and hyperconnection, an efficient electrocardiogram (ECG) telemonitoring system in wireless body area network (WBAN) demands an easy to use, self-configurable, secure, plug and play system with minimum hardware and computational complexities. The compression and quantization parameters required for an efficient representation of ECG signal will vary from patient to patient, from lead to lead, and from time to time. To this end, we propose a compressed sensing based WBAN with self-configurable gateway node (CS-SCGN) using deterministic binary block diagonal (DBBD) measurement matrix. The self-configurability is brought in through a low complex method for adaptive tuning of parameters with a careful choice of measurement matrix and data length. The redundant data transfer between sensor nodes and gateway node is avoided by addressing the diverse requirements in ECG remote health monitoring through three modes of configuration in the proposed system. A further reduction in communication and storage cost is achieved by optimizing the number of bits transmitted by sensor nodes by automatically tuning the compression ratio and quantization depth based on the dynamics of ECG signal. The self-configuration algorithm is designed to run at the gateway node in such a way as to optimize the power efficiency of sensor nodes without causing an extra power drain at the gateway node. Also, we investigate the feasibility of using smartphone as an IoT gateway node for performing primary processing to provide local utility before sending the received data to the remote server. The energy efficiency and real-time feasibility of the proposed algorithm are evaluated by implementing the gateway node on Odroid-XU4 board which runs on the same processor as in the latest smartphones. The experimental results indicate that our proposed self-configurable system at the gateway node makes the entire ECG telemonitoring system flexible, plug and play, patient independent and power-efficient.
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koya2019plugcomputers Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Koya, Aneesh M;Deepthi, P P;
Journal Computers in biology and medicine
Year 2019
DOI
S0010-4825(19)30236-7
URL
Keywords
energy efficiency compressed sensing (cs) deterministic binary block diagonal (dbbd) matrix electrocardiogram (ecg) monitoring heterogeneous multi-processing (hmp) inter-pulse interval (ipi) wireless body area network (wban)

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