Machine learning based BPM/Pulse interval predictor of human being using ATMega328p based development board. (2023)

• Record Type: Journal Article
• Authors: Sharma, Divya; Jain, Rohan; Sharma, Ritu; Shan, B. Priestly; Shiney, O. Jeba
• Is Part Of: Materials today. Volume 80:Part 3(2023)
• Page Start: 3898
• Page End: 3908
• Publication Date: 2023
• Subjects: Integrated Development Environment (IDE) -- ATMega328p, Body Mass Index (BMI) -- BPM (beats per minute) -- Machine Learning (ML) -- Artificial Intelligence (AI); Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2021.07.411
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: This paper will go through an attempt to predict vital data namely BPM and pulse interval of human being. Study involves data set produced with a total of 83 subjects spread across various categories by age group and BMI ratio. A specialized device fabricated for this purpose designed on a development board divided in two layers of printed circuit board based on Atmega328p microcontroller enables the collection of data using a pulse sensor. The machine learning algorithm running on the device saves encoded data files and produces calculation based on the previous data saved. Thus, the accuracy improves after every data sample obtained enabling the device with experience-based learning. The system makes use of a few mathematical equations on the dataset obtained for each category and is successfully able to predict BPM and pulse interval more than 70% times for age group 21 to 25 involving proportionate subjects according to BMI ratio with a database of 140 samples for each parameter taken from 70 subjects; predict BPM more than 50% times and pulse interval more than 30% times for age group 51 to 55 also involving proportionate subjects according to BMI ratio with a database of 26 samples for each parameter taken from 13 subjects while subjects were sitting or standing idle represented by the class "Regular".