Abstract
This paper proposes the design of a wearable IoT based, battery-powered real-time heart rate monitoring and alert system for tachycardia. The device uses an optical heart rate and a temperature sensor connected to a microcontroller with a GPS module to constantly send real-time medical and location data to a central server using a GSM module via AT commands. The central server will store the data periodically in a database based on MongoDB and NodeJS. In the database, the heart rate and temperature data are evaluated via an unsupervised machine learning algorithm using a K-means classification method whose centroids have been previously tuned. If the data is registered as dangerous, an SMS is sent to a selected guardian. The SMS received by the guardian will have the triggered location of the system in a link that displays the location on google maps. The time-stamped location and medical data from whenever an alert was sent is accessible to the user and is visualized on the Google Maps API via an Angular based website, hosted on an AWS server. The data is displayed on the website by the database using JavaScript upon user request, communicated by SQL queries.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rajendra Acharya U, Fujita H, Lih OS, Hagiwara Y, Tan JH, Adam M (2017) Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Inf Sci 405:81–90
Fernandez F, Pallis GC. Opportunities and challenges of the internet of things for healthcare: systems engineering perspective. In: International conference on wireless mobile communication and healthcare, pp 263–266
Hassanalieragh M, Page A, Soyata T, Sharma G, Aktas M, Mateos G (2015) Health monitoring and management using internet-of-things (IoT) sensing with cloud-based processing: opportunities and challenges services computing (SCC). In: 2015 IEEE international conference, pp 285–292
He S, Chen J et al (2014) Mobility and intruder prior information improving the barrier coverage of sparse sensor networks. IEEE Trans Mob Comput 13:1268–1282
Jersak LC, da Costa AC, Callegari DA (2013) A systematic review on mobile health care. Technical report 073, Faculdade de Informática PUCRS—Brazil, May 2013
Lee H, Shin SY, Seo M, Nam GB, Joo S (2016) Prediction of ventricular tachycardia one hour before occurrence using artificial neural networks. Sci Rep 6:1–7. https://doi.org/10.1038/srep32390
Kracht M, German Aerospace Centre (DLR) (2004) Tracking and interviewing individuals with GPS and GSM technology on mobile electronic devices. In: Conference paper submitted to the seventh international conference on travel survey methods, Costa Rica
Lee SJ, Tewolde G, Kwon J (2014) Design and implementation of a vehicle tracking system using GPS/GSM/GPRS technology and smartphone application. In: Conference paper submitted to the 2014 IEEE world forum on internet of things (WF-IoT)
Gnana Sheela K, Varghese AR, Department of ECE, APJ Abdul Kalam Technological University, Kerala, India (2019) Machine learning based health monitoring system. In: International multi-conference on computing, communication, electrical and nanotechnology, I2CN-2K19, 25th and 26th Apr 2019
Vineetha Y, Misra Y, Krishna Kishore K, ECE Department, GMRIT, Rajam, Affiliated to JNTUK University, AP, India (2020) A real-time IoT based patient health monitoring system using machine learning algorithms. Euro J Mole Clin Med 7(4):2912–2925
Wong TK, Mun HK, Phang SK, Lum KL, Tan WQ (2020) Real-time machine health monitoring system using machine learning with IoT technology. In: Conference paper submitted to the MATEC web of conferences 335, 14th EURECA 2020
SrivardhanReddy K, Sidaarth R, Reddy SA, Shettar R (2019) IoT based health monitoring system using machine learning. Int J Adv Res Innov Ideas Educ
Paramita S, Bebartta HND, Pattanayak P. IoT based healthcare monitoring system using 5G communication and machine learning models. In: Health informatics: a computational perspective in healthcare, pp 159–182
Vairam T, Sarathambekai S, Umamaheswari K, Jothibanu R, Manojkumar D (2020) Real-time cardiovascular health monitoring system using IoT and machine learning algorithms. In: Securing IoT and big data, 17 Dec 2020
Murthy SVSN, Satyanarayana BVV, Srinivas CVVS (2021) Location tracking and warning system of a ship using Arduino. Published in 2021 5th international conference on computing methodologies and communication (ICCMC)
Patil U, Mathad SN, Patil SR (2018) Vehicle tracking system using GPS and GSM using mobile applications. Int J Innov Sci Res Technol 3(5)
Mackowiak PA, Wasserman SS, Levine MM (1992) A critical appraisal of 98.6 ℉, the upper limit of the normal body temperature, and other legacies of Carl Reinhold August Wunderlich. J Am Med Assoc 268:1578–1580
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jacob, R.O., Niranjana Murthy, H.S. (2023). IoT Based Real-Time Wearable Tachycardia Monitoring System Using Machine Learning. In: Gupta, M., Ghatak, S., Gupta, A., Mukherjee, A.L. (eds) Artificial Intelligence on Medical Data. Lecture Notes in Computational Vision and Biomechanics, vol 37. Springer, Singapore. https://doi.org/10.1007/978-981-19-0151-5_10
Download citation
DOI: https://doi.org/10.1007/978-981-19-0151-5_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-0150-8
Online ISBN: 978-981-19-0151-5
eBook Packages: EngineeringEngineering (R0)