Monitoring Heart Rate And Temperature Based On Internet Of Things

Monitoring and measurement of body temperature is very important to know the condition of the patient. The body temperature is associated with a number of the human heartbeat, little change in body temperature can significantly affect the performance of high cardiac health problems this causes the need for health monitoring. IoT helps to move from manual heart rate monitoring system for heart rate monitoring system remotely. The purpose of this study is to develop the monitoring heart rate and temperature-based Internet of Things. This tool can see the dawn of the body and the value of BPM from a distance. The way the device is very simple to use cardiac leads in Lead II to obtain the value of BPM and DS18B20 temperature sensor to measure the body temperature will be displayed on Thing-Speak through the ESP32 module. Keyword-Internet of Things, ESP32, Thingspeak


I. INTRODUCTION
Monitoring and measurement of body temperature are very important to know the condition of the patient. A healthy body is able to maintain a constant body temperature even if the environmental conditions change. The body temperature was associated with a number of the human heartbeat, little change in body temperature can significantly affect the performance of the heart because the farther the patient's normal temperature affect how quickly the patient's heart in pumping blood around the body [1].
At this time, health problems such as heart failure, and heartrelated diseases are increasing day by day. The high concerns about heart health have led to the need for health monitoring, especially in the cardiac activity itself [2]. This heart disease requires ongoing monitoring and long term to have proper controls against him. IoT helps to move from manual heart rate monitoring system for heart rate monitoring system remotely. A doctor may not always be present at all times to provide treatment or care to the patient or guardian can not be present all the time to bring the patient to the hospital. Heart rate monitoring system used to remotely monitor physical parameters such as heart rate and heart rate were measured sent directly to the doctor [2].
Monitoring BPM (Beat Per Minute) or Heart Rate and Temperature is monitoring the patient's condition are continuously helping medical personnel to monitor the development of the patient's condition. BPM or Heart Rate is a unit to count the number of heartbeats per unit of minutes while the body temperature is the difference between the amount of heat produced by the body and the amount of heat lost to the environment.
IoT first raised by Kevin Ashton in 1999 in one presentation. Many are predicting that the influence of the Internet of Things is "the next big thing" in the world of information technology, as in the IoT devices can transmit their data directly to the server cloud that can then be analyzed/processed by the account owner can then be distributed to media and communication Internet connected to the IoT. As well as two-way communication can occur between devices, users, and between devices with the user. Internet of Things in implementation will also be able to identify, locate, track, monitor objects and trigger the corresponding event automatically and in real-time. As an example of its application in the medical field [3].
In a previous study of this tool ever made by Nur Hudatu Munawaroh entitled "Temperature Monitoring with Chart View on the PC". which is capable of monitoring the patient's body temperature. However, this tool is still not there BPM parameter. It had also created a monitoring tool BPM and body temperature by I Dewa Made and Putu Adi. Both tools use the android interface. In 2016 developed again by Nadya Nezwa Damayanti entitled "BPM and Temperature Wireless Monitoring". That adds a nurse call using the display on the PC.
With the description above, the writer intends to make an appliance-based IOT as a display of heart rate and body temperature for continuous monitoring, easier for nurses to monitor without having to visit a patient, helping specialists to access the data in real-time from anywhere and can discuss the results of such data with other specialists. The data will be displayed on the website. With the hope to be monitored through the system without using a wireless or wired media as data delivery. It is expected that the patient's condition can be monitored directly (online), through the website so that the data in heart rate and body temperature of the patient's health can be monitored at any time. IoT-based system so that the distance is not an obstacle in the monitoring process.

A. Experimental Settings
This study uses ten normal subjects. The subject has taken randomly and data collection was repeated 5 times.

1) Materials and Tools
This study using electrodes. Instrumentation ECG built by LM324 OP-AMP. Microcontroller ESP32 used for ECG data acquisition and sensor DS18B20. A digital storage oscilloscope is used to test analog circuits. Phantom ECG is used to calibrate the analog circuit.

2) Experiment
In this study, after the design is completed then the frequency response of the device is tested using a generator function according to the specifications of the ECG signal. In the calibration phase, was tested using a simulator ECG Module (phantom) with all the range (30, 60, 120, 180, and 240 BPM). Each setting, Holter output is calculated to validate these results. Then the modules are tested on the human body.

B. Block Diagram
Electrodes attached to the patient and DS18B20 sensor paired in the armpits to detect the body temperature. Output signals from the electrode leads are leads II will enter into a series of instruments in order to get the ECG signal amplifier but still there is little noise and the signal. Then the output of the instrument amplifier into the HPF to be filtered and amplified in the non-inverting and filtered at LPF block further into the notch filter for a frequency of 50 Hz notch filter then enter the filter LPF in order to signal more clearly and there is no noise. From the LPF output back to the summing amplifier strengthened. The output of the summing amplifier entered on ESP32 ADC pin.
While the DS18B20 sensor output will go straight into the digital pin ESP32. ESP32 incoming data to be processed and shipped. The results can be seen in ThingSpeak when the temperature exceeds or BPM setting there will be a notification in the mail. The picture above is a flow diagram of the internet of things based heart rate and temperature monitoring module Instrumentation amplifier circuit as shown above to get input from the lead electrodes attached to the right arm and left leg man.   Notch Filter circuit in the picture above using a cut-off frequency of 50 Hz. This circuit has characteristics hold signal with a frequency corresponding cut-off frequency and will pass signals outside the cut-off frequency.    The summing Amplifier circuit is a circuit that serves to raise the ECG signal voltage levels, some of which rose to a positive ECG signal entirely.

8) Summing Amplifier
9) The temperature sensor. Fig. 14. Temperature Sensor Circuit DS18B20 temperature sensor output digital form so that the sensor output is directly connected to digital pin ESP32.

III. RESULT
In this study, the Instrument has been tested using an ECG phantom, thermometer and ECG from the human body.

1)
Program for Arduino 2.1) Thingspeak initialization program // Use this file to store all of the private credentials // and connection details #define SECRET_SSID "Miracle-"// replace MySSID with your WiFi network name #define SECRET_PASS "operator" // replace MyPassword with your WiFi password #define SECRET_CH_ID 632347 // replace 0000000 with your channel number #define SECRET_WRITE_APIKEY "O5WECCXIUNTZM201" // replace XYZ with your channel write API Key On secret.h used to perform some of the settings of the WiFi channel number and write APIKEY to thingspeak account to connect with the channel to be used to enter the WiFi connection is used.   IV. DISCUSSION Based on the measurements by comparing errors between design and tools BPM Oximetry (with input from the simulator ECG) showed the greatest error value is 1.3% and it is in the BPM value measurement 30. From the results of temperature measurements of the respondents showed the greatest error value is 0, 6%. While the BPM value Measurement results of the respondents showed the greatest error value is 0.6%. This is because of the effect of the patient's condition and position of the patient on the temperature sensors and electrodes.

V. CONCLUSION
Based on the results of the discussion and goals can be concluded appliance module can work. However, there are unresolved issues such as the value of a great error when there is the movement of the patient and there is also a problem of delay that relies heavily on the Internet network used.