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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM26819

IoT Based Novel Approach for Remote Patient Pulse Rate Monitoring and Stroke Prediction using Machine Learning

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM26819

Volume:

7 |

11 |

Nov 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

PPM

Prajakta P. Musale

AMP

Arya M. Pathak

PKP

Pranav K. Patel

RNP

Rukmoddin N. Patel

APP

Aarushi P. Pathak

ARP

Ameya R. Pathak

RBP

Revati B. Pathak

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Abstract

In recent years, the advancement of Internet of Things (IoT) technologies has revolutionized various domains, including healthcare. Remote patient monitoring is an emerging field that allows healthcare professionals to monitor vital signs of patients remotely, ensuring timely intervention and improved healthcare outcomes. This research paper presents a system that combines the ESP32 microcontroller, an Express MVC website, and a web API to enable remote pulse monitoring. The ESP32 collects pulse data and sends HTTP requests to a web API, which then responds with live data to be displayed on the Express MVC website. Additionally, based on specific response codes from the web API, the ESP32 triggers a WhatsApp bot to notify healthcare professionals. The proposed system demonstrates the feasibility and potential for remote pulse monitoring using readily available IoT components .

KEYWORDS

Remote patient monitoring ESP32 IoT healthcare Express MVC MongoDB timeseries

Cite This Article

Prajakta P. Musale, Arya M. Pathak, Pranav K. Patel, Rukmoddin N. Patel, Aarushi P. Pathak, Ameya R. Pathak, Revati B. Pathak, "IoT Based Novel Approach for Remote Patient Pulse Rate Monitoring and Stroke Prediction using Machine Learning," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 11, Nov 2023. https://doi.org/10.55041/IJSREM26819

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Publication Date

11/22/2023

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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