Monitoring Bpm and Body Temperature Based Internet of Things (IoT) Thing speak Platform

BPM, Body Temperature, Max30102, Mlx90614, Thing speak

Authors

  • Novita Nur Azize Romadhini
    Novitanurazize@gmail.com
    Department of Electromedical Engineering Poltekkes Kemenkes, Surabaya, Indonesia , Indonesia
  • Abdul Kholiq Department of Electromedical Engineering Poltekkes Kemenkes, Surabaya, Indonesia , Indonesia
  • Triana Rahmawati Department of Electromedical Engineering Poltekkes Kemenkes, Surabaya, Indonesia , Indonesia
  • Faraz Masood Department of Computer Science, Aligarh Muslim University, Uttar Pradesh, India, India

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Technological advancements in the field of healthcare, coupled with contemporary scientific and technological 
progress, have led to significant advancements in various operational procedures within medical institutions. These 
advancements include the adoption of automated systems in lieu of human intervention. An example of this progress is the 
implementation of automated systems for monitoring individuals' heart rates and body temperatures. Heart rate and body 
temperature stand as critical indicators employed by medical professionals to assess both physical and mental well-being. 
Deviations from normal heart rate and body temperature values can signify underlying issues. Body temperature, specifically, 
can offer insights into internal bodily conditions. This transition to automated monitoring tools has resulted in heightened 
practicality and efficiency. These tools offer real-time monitoring capabilities and the option for remote oversight. The 
monitoring device's architecture employs the Max30102 as a BPM sensor, which boasts a digital output. Additionally, the 
MLx90614 sensor functions as a digital temperature sensor. The collected data is then processed and showcased on an I2C 
LCD screen, with information transmitted to the ThingSpeak platform via the ESP32, serving as a Wi-Fi module. Notably, the 
BPM sensor demonstrates a minimal error rate of 0.23% and a maximum of 2.11%, while the temperature sensor showcases a 
minimal error rate of 0.59% and a maximum of 3.37%. The outcomes of this research exhibit potential application in enhancing 
the efficiency of remote monitoring systems when integrated into patient monitoring setups.