Smart Heart Rate, Oxygen Level and Temperature values Monitoring with Things peak

Abstract

The failure to recognize heart-related problems early enough is the primary cause of a large percentage of fatalities owing to cardiac damage. Early diagnosis and management of cardiac diseases are always aided by continuous monitoring and early prediction. Here, we intend to obtain a  method for continuously monitoring and forecasting the subject's heart rate, oxygen level, and temperature values gathered from sensors, with their Internet-Of-Things capabilities via Thingspeak platform. The subject is equipped with the MAX30102, a commercial photometric biosensing module, and the LM25 temperature sensor which comprise the ability to gather heartbeat, spo2 and temperature values respectively.This project also makes use of incumbent components to analyze and convey the collected data to a cloud database.The acquired data is transferred to the Thingspeak cloud database where the values can be stored, monitored with access from anywhere,manipulated and inspected using MATLAB facilities. This work suggests using a Convolution Neural Network[CNN] based model for diagnosing atypical heart rate to predict illness risk using unstructuredtime-series data.By adding this smart system to the health service, we canprovide better medical treatment, and lower diagnostic costs in healthcare.