Open Access

ARTICLE

Design of Online Vitals Monitor by Integrating Big Data and IoT

by E. Afreen Banu^1,*, V. Rajamani²

1 Department of Computer Science Engineering, Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, 600062, India
2 Department of Electronics & Communication Engineering, Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, 600062, India

* Corresponding Author: E. Afreen Banu. Email:

Computer Systems Science and Engineering 2023, 44(3), 2469-2487. https://doi.org/10.32604/csse.2023.021332

Received 30 June 2021; Accepted 02 August 2021; Issue published 01 August 2022

Abstract

In this work, we design a multisensory IoT-based online vitals monitor (hereinafter referred to as the VITALS) to sense four bedside physiological parameters including pulse (heart) rate, body temperature, blood pressure, and peripheral oxygen saturation. Then, the proposed system constantly transfers these signals to the analytics system which aids in enhancing diagnostics at an earlier stage as well as monitoring after recovery. The core hardware of the VITALS includes commercial off-the-shelf sensing devices/medical equipment, a powerful microcontroller, a reliable wireless communication module, and a big data analytics system. It extracts human vital signs in a pre-programmed interval of 30 min and sends them to big data analytics system through the WiFi module for further analysis. We use Apache Kafka (to gather live data streams from connected sensors), Apache Spark (to categorize the patient vitals and notify the medical professionals while identifying abnormalities in physiological parameters), Hadoop Distributed File System (HDFS) (to archive data streams for further analysis and long-term storage), Spark SQL, Hive and Matplotlib (to support caregivers to access/visualize appropriate information from collected data streams and to explore/understand the health status of the individuals). In addition, we develop a mobile application to send statistical graphs to doctors and patients to enable them to monitor health conditions remotely. Our proposed system is implemented on three patients for 7 days to check the effectiveness of sensing, data processing, and data transmission mechanisms. To validate the system accuracy, we compare the data values collected from established sensors with the measured readouts using a commercial healthcare monitor, the Welch Allyn® Spot Check. Our proposed system provides improved care solutions, especially for those whose access to care services is limited.

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Keywords

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