A Cloud-Centric Real-Time Telemonitoring System for Cardiac Patients based on the Internet of Medical Things

A Cloud-Centric Real-Time Telemonitoring System for Cardiac Patients based on the Internet of Medical Things
  IJETT-book-cover           
  
© 2023 by IJETT Journal
Volume-71 Issue-3
Year of Publication : 2023
Author : Adarsh Ravi Mishra, Ravi Mishra, Ragini Shukla
DOI : 10.14445/22315381/IJETT-V71I3P212

How to Cite?

Adarsh Ravi Mishra, Ravi Mishra, Ragini Shukla, "A Cloud-Centric Real-Time Telemonitoring System for Cardiac Patients based on the Internet of Medical Things," International Journal of Engineering Trends and Technology, vol. 71, no. 3, pp. 105-119, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I3P212

Abstract
The convergence of Internet-based technology with primary care utility is critical for the worldwide amalgamation of quality medical management and its accessibility. Innovation has remained a significant aspect of medical care, allowing physicians to employ a variety of instruments to examine, administer, and observe patients. This paper’s central emphasis is on e-health data collection, conveyance, and surveillance apparatus. The wireless monitoring system monitors patients’ medical metrics and communicates them to the furthest edge via the ZigBee gateway. This paper introduces an IoT-enabled smart ehealthcare system that can continuously monitor patients' vital signs and the real-time room environment where patients are currently positioned. This system collects data from the hospital premises using five sensors: the body temperature sensor, the heartbeat sensor, the room temperature sensor, the CO2 sensor, and the CO sensor. For each situation, the percentage of errors in the developed scheme is less than 2%. The patient's situation is relayed to medical personnel via a portal, where they can monitor, process, and analyze the patient's real-time situation. A low-power communication device and an embedded smart IoT system are being designed and developed with the goal of covertly gathering physiological metrics without interfering with daily life. Sensor-based data analytics using machine learning methods were used in this study to identify predictors and/or sudden abnormality indicators accurately. The data for heart rate and body temperature is analyzed using the bio-medical sensors embedded with an ARM Cortex-A7 microcontroller. For the benchmark accuracy of the dataset, a detailed calculation of experiments in the Internet of Medical Things (IoMT) with various machine learning techniques is compared. It has been exemplified through extensive testing that the recommended procedure outperforms machine learning methodologies with a 15% increase in accuracy, facilitating rapid alarms to prevent the aftereffects of abnormalities if they occur, which the system without the utilized methodology lags. Cloud storage allows for the analysis of parameters regarding patients. The error between observed and actual data is analyzed to validate the proposed Internet of Medical Things model. The performance of the proposed method is further identified by using various classifications, like decision trees, discriminant analysis, support vector machines, and Naive Bayes, for detecting any variation from normal data or disorder on real-time datasets.

Keywords
E-health, Internet of Medical Things, ARM-Cortex-A7 microcontroller, ZIGBEE, Machine learning techniques.

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