Heart Rate Variability or heart rate in humans is used to monitor the heart rate in humans, the function of the heart rate monitor is to monitor the human heart rate. The purpose of making this tool is to compare the results of heart rate readings using the discrete wavelet transform method to facilitate the detection of R peak. This can be learned by evaluating and studying each decomposition result from level 1 to level 4 on Discrete Wavelet Transform processing using Haar mother wavelets. This study uses a raspberry pi 3B as a microcontroller as a data processor that is obtained from the ECG module. From this study, it can be concluded that in heart rate readings, level 2 decomposition details coefficient has the best value as data processing that helps for heart rate readings with an error value of 0.531%, HRV readings of 0.005 in comparison with phantom tools and a standard deviation of 0.039. The advantage of this tool is a good precision value in HRV and BPM readings. In reading the HRV of the respondent, it was found that each initial condition of the patient's HRV would be high due to the respondent's unstable condition. The disadvantage of this tool is that there is a delay in running the program, there is no display in the form of a signal in real time.

FHR is the fetal heart rate from bpm recording detected by doppler, FHR monitoring is very important to monitor fetal health to avoid fetal distress or fetal death, FHR provides more in-depth information about how the baby is doing compared to traditional monitoring of the baby. IoT media is a medium for monitoring remote sensor values ​​using internet connections, but there are several obstacles, namely there are doubts about the data displayed by IoT media, namely the risk of missing or unsent data, this will be very dangerous if the data that is should be monitored by doctors as a reference for medical diagnosis and treatment is lost or not displayed on the IoT, because if there is missing data it will cause inaccurate diagnosis or health treatment decisions by doctors. The aim of this study to analyze the effect of lost data on the formation of the Fetal Heart Rate graph on the IoT platform as a medium for remote diagnosis. In addition, FHR data can be saved for further diagnosis by a doctor if needed. This study uses an ESP32 microcontroller which will also be used to send data to IoT (Thinger.io). The independent variable used in this study is FHR data before it is uploaded to the IoT, and the dependent variable is FHR data when it is uploaded to the IoT. The greatest data loss is at the farthest distance of 30 meters with a value of 62.47%. Based on the research that has been done, this study has the advantage that the results obtained from Doppler are close to the BPM value in humans. And also this research has developments that can be done in the future such as adding storage to the website that is used for monitoring, and placing the right position on Doppler so that the results are more stable.

In December 2019, the world was introduced to a new coronavirus, severe acute respiratory syndrome (COVID-19).The primary strategy for COVID-19 patients is supportive care, using high-flow nasal oxygen therapy (HFNC) reported to be effective in improving oxygenation. Stability is the ability of a medical device to maintain its performance [1]. Medical equipment must have the stability necessary to maintain critical performance conditions over a period of time. Accuracy is the closeness of agreement between the value of a measuring quantity, and the value of the actual quantity of the measuring quantity[2].The purpose of this study is to ensure that the readings of the HFNC device are accurate and stable so that it is safe and comfortable when used on patients. The development of the equipment that will be used by the author adds graphs to the TFT LCD to help monitor stable data in real time so that officers can monitor the flow and fraction of oxygen in the device to be stable. This study uses Arduino Nano while the sensor used is the GFS131 sensor, then the results are displayed on the Nextion TFT LCD. The test is carried out with comparing the setting value of the HFNC tool that appears on the TFT LCD with a gas flow analyzer with a measurement range of 20 LPM to 60 LPM 5 times at each point. Based on measurements on the gas flow analyzer, the HFNC module has an average error (error (%)) of6.40%. Average uncertainty (Ua) 0.05. Conclusion from these results that the calibrator module has a relative error (error value) that is still within the allowable tolerance limit, which is ±10%, the tool is precise because of the small uncertainty and good stability of the stability test carried out within a certain time.