Comparative Evaluation of Microcontrollers for Real-Time Heart Rate Monitoring and Tachycardia/Bradycardia Detection

Authors

Arxhend Jetullahu, University for Business and Technology - UBTFollow
Luan Mulaku, University for Business and Technology - UBTFollow
Saranda Demolli, University for Business and Technology - UBTFollow

Keywords

Heart rate monitoring, microcontroller comparison, machine learning in healthcare

Abstract

The growing demand for portable healthcare devices has led to combining microcontrollers with machine learning models to enable real-time health monitoring. It's quite paramount, specifically in the context of identifying irregularities in heart rate, like tachycardia—where the heart races unusually fast—and bradycardia, an instance of a sluggish heartbeat. The neat thing is that this study takes a stab at contrasting the effectiveness among three popular microcontroller types—the ESP32, the Raspberry Pi, and the Arduino Nano 33 BLE Sense—in terms of real-time tracking of heartbeats and spotting any oddities (using, of course, a model developed with some machine learning elements). At the heart of this model lies the fundamental structure known as Long Short-Term Memory, or LSTM. This model has been finely tuned to identify those pesky heart rate inconsistencies, after which it finds its home, so to speak, on each of the microcontroller types where it's applied for immediate reasoning. Microcontrollers are assessed on some pivotal metrics, alright? Think inference time or latency; toss in power use, how precise they are, the simplicity of melding them with heart rate sensors, and all that costing - that's good to keep in mind. What we see from our research is that each of these little guys has its perks, certainly - but there's always that trade-off lurking about, with processing mightiness, energy economy, and system expenses. The findings from this mash-up analysis are worth their weight for picking microcontrollers in the healthcare arena, especially for those wearables that can't stop monitoring heart rates. And then, this exploration's deductions are slipping into making more efficient, precise, and cost-friendly healthcare system monitoring stuff.

DOI

10.33107/ijbte.2024.12.1.10

First Page

1

Last Page

15

Recommended Citation

Jetullahu, Arxhend; Mulaku, Luan; and Demolli, Saranda (2024) "Comparative Evaluation of Microcontrollers for Real-Time Heart Rate Monitoring and Tachycardia/Bradycardia Detection," International Journal of Business and Technology: Vol. 12: Iss. 2, Article 1.
DOI: 10.33107/ijbte.2024.12.1.10
Available at: https://knowledgecenter.ubt-uni.net/ijbte/vol12/iss2/1