Baby Incubators Research Articles

Kontrol Temperatur dan Kelembapan pada Ingkubator Bayi Menggunakan Platform Antares

The condition of premature birth is a condition where a baby needs an incubator to maintain the baby's body temperature, because premature babies do not have many weaknesses to regulate body temperature so that they are prone to hypothermia. Setting the temperature and humidity in the baby incubator is an important aspect of caring for premature babies. The use of baby incubators in hospitals requires a high cost of up to Rp. 500,000 per night, even though premature babies need care in an incubator for up to one month, therefore a tool is needed to control humidity and temperature based on the Internet of Things. Control the humidity and temperature of the air used in the patient's home using a microcontroller where the microcontroller will detect the humidity and temperature inside the baby and the temperature measurement results can be viewed via the internet of things which can display temperature and humidity reading data remotely. The results of the comparison between LYNX32 and the platform between the displayed temperature and humidity values have an error of 0%. The Antares platform display displays temperature and humidity data in real time and has historical data that can be downloaded via the Antares platform, making it easier for nurses to monitor conditions.

Design of Incu Analyzer for IoT-Based Baby Incubator Calibration

An incubator analyzer, serving as a calibration tool, is utilized to measure diverse parameters such as temperature, mattress temperature, humidity, airflow, and noise in infant incubators. The present study focuses on the development of the Design Incubator Analyzer for IoT (Mattress Temperature and Humidity) with LCD and ThingSpeak display, specifically designed for calibrating baby incubators. The primary objective is to design and develop an Incubator Analyzer as a calibration device for assessing various parameters in infant incubators, encompassing temperature, mattress temperature, humidity, airflow, and noise. The design of this calibration device incorporates a Thermocouple Type-K sensor for baby incubator mattress temperature parameters, a DHT22 sensor for humidity parameters, and an ESP32 microcontroller. The ESP32 processes data from the Thermocouple Type-K and DHT22 sensors to generate values for mattress temperature (TM) and humidity (RH), which are then displayed on LCD and ThingSpeak displays. The device underwent rigorous testing against an established measuring device, the INCU II. In the study, the TM parameter or mattress temperature exhibited the smallest error of -0.0140% at 35°C and the largest error of 0.0584% at 36°C. Concerning the humidity parameter, the largest error was 0.0570% at 32°C, while the smallest error was 0.0207% at 35°C. Overall, the Incubator Analyzer Design for IoT-Based Baby Incubator Calibration device, or IoT-based Incubator Analyzer, demonstrates potential usability following the planning and execution phases, including a thorough review of existing literature. To enhance user experience during the calibration process, an IoT system was developed for data transmission over Wi-Fi, presenting results on the ThingSpeak platform in real-time

PID Temperature Control of Baby Incubator Transport Battery Efficiency

Transport baby incubators are used to keep babies warm and safe while in transport using battery voltage sources or DC electricity, which are portable and can be used without getting a supply of electrical energy. The problem that often occurs with this tool is the limited battery power system. causes a risk to the infant in the event of power failure or battery exhaustion. We aim to evaluate the battery efficiency of Baby Incubator Transport using a PID temperature controller. The evaluation is done by comparing and analyzing the battery voltage of the device to the standard device, as well as considering the setting temperature and duration of use of the device so that it can provide convenience in evacuating babies in an emergency. The tool uses the PID method to control temperature and maximize battery power. In this design, researchers only look at the efficiency of the PID method on temperature control and the battery to be used. This module will have a display that will display the battery voltage value, battery voltage percentage, skin temperature, chamber temperature, humidity, and temperature control that has been selected in the form of a graph. Compared with the digital multimeter measuring instrument. From the results of data collection, it can be concluded that the PID method has a faster rise time to reach the setting temperature, while the fuzzy method has a longer rise time to reach the setting temperature. However, the PID method requires more battery power than the Fuzzy method. The measurement results between the display and the measuring device have a difference of 3.1% at 34°C, at 35°C it is 3.9%, and at 36°C it is 4.7%. The biggest error is at a temperature of 36ºC, the smallest is at a temperature of 34ºC. Based on the results of the observation analysis of battery power consumption, it is found that the smaller the battery energy, the smaller the current issued, as well as the voltage issued. But if the load is large, the current is inversely proportional to the center, the battery voltage decreases while the current increases.

IoT-based incubator monitoring and machine learning powered alarm predictions.

Incubators, especially the ones for babies, require continuous monitoring for anomaly detection and taking action when necessary. This study aims to introduce a system in which important information such as temperature, humidity and gas values being tracked from incubator environment continuously in real-time. Multiple sensors, a microcontroller, a transmission module, a cloud server, a mobile application, and a Web application were integrated Data were made accessible to the duty personnel both remotely via Wi-Fi and in the range of the sensors via Bluetooth Low Energy technologies. In addition, potential emergencies were detected and alarm notifications were created utilising a machine learning algorithm. The mobile application receiving the data from the sensors via Bluetooth was designed such a way that it stores the data internally in case of Internet disruption, and transfers the data when the connection is restored. The obtained results reveal that a neural network structure with sensor measurements from the last hour gives the best prediction for the next hour measurement. The affordable hardware and software used in this system make it beneficial, especially in the health sector, in which the close monitoring of baby incubators is vitally important.

Advancing healthcare: integrating telemedicine with baby incubators at puskesmas dampit to ensure seamless health services

Puskesmas in Malang Regency do not yet have facilities for monitoring LBW in real time via android either by health workers at the health centre or the patient's family. Puskesmas Dampit serves communities spread across several villages with a coverage area spread across several villages. This is an obstacle for families to get access to monitor the patient's progress, because the patient is in the NICU or ICU room. Another thing is that there is no training for users in the use of telemedicine-based Baby Incubator equipment. The role of users and families of patients is very important to keep the equipment in good condition. However, operating trainings based on new technology for users have never been conducted. Efforts to solve the above problems are to carry out counselling and assistance in the use of baby incubator equipment to users including nurses, midwives and other non-health worker users, so as to increase the knowledge of officers or users which in turn will have an impact on increasing the productivity of officers or users and improving the quality of service at the health centre. The purpose of this Community Service is to increase the role and participation of Poltekkes Kemenkes Surabaya in improving the degree of public health in the Puskesmas Dampit area, Malang Regency. The results of community service through the application of telemedicine-based Baby Incubator at Puskesmas Dampit are increased knowledge and skills regarding telemedicine-based baby incubator equipment. This will improve health services, especially for babies who need intensive care. The implementation of this technology provides significant benefits, including increased accessibility of health services, speed of response to patient health conditions, family involvement in care, and efficient use of resources. Assistance to Puskesmas personnel and patient families in the form of training, monitoring and evaluation of the use of appropriate technology in the form of Telemedicine-based baby incubator utilization.

Monitoring Baby Incubator Central through Internet of Things (IoT) based on Raspberry Pi Zero W with Personal Computer View

kids born before 37 weeks of pregnancy or weighing less than 2500 grams are considered premature, whereas kids born between 38 and 40 weeks of pregnancy and between 2500 to 4000 grams are considered full-term. Given that their organ systems are still developing within the womb, premature infants find it difficult to adjust to life outside the womb. As a result, special consideration must be made. They include modifying the environment's temperature, humidity, and oxygen needs to reflect those of the mother's womb. These conditions might be replaced with a baby incubator. This tool's creation is intended to make it easier for midwives and other healthcare professionals to keep an eye on many baby incubators. The Internet of Things (IoT) system is used by this instrument to transfer data. Using three ESP32 modules that have been put together to create modules that can collect data and have that data analyzed by a server (central monitoring) Raspberry Pi Zero W. Data will be sent via Internet of Things (IoT) technology, and the website will display the data. Two tests were conducted at 32 degrees Celsius, one at 34 degrees Celsius, and one at 36 degrees Celsius for a total of five tests. This technique was developed using a form of pre-experimental, after-only study. In this configuration, researchers may only see the module reading results; incubator analyzer data are not shown. Error value 3 in monitoring at 32 degrees Celsius has a maximum error of -0.04 percent. The largest error value occurs when the temperature is set to 34 degrees Celsius, when the monitoring error value is -0.016%. Monitoring inaccuracy is at its highest, 0.01%, when the temperature is 36 degrees Celsius. The monitoring 3 error value is most at 32 degrees Celsius (-0.025 percent), followed by 34 degrees Celsius (0.031 percent), and finally 36 degrees Celsius (0.049 percent), as shown by data on noise measurements. The findings demonstrate that each measurement performed by the module still contains mistakes. Medical staff should find it easier to concurrently monitor many infant incubators thanks to this discovery.

Baby Incubator with Room Temperature and Skin Temperature Monitoring Via IoT-Based WIFI Network

Newborns require special attention to the labor process, this affects the health of the baby itself. Similarly, with premature babies who have a high level of sensitivity to the surrounding environment, one of the biggest causes of death in premature babies is hypothermia, therefore adjustments are needed starting from room temperature and skin in the baby incubator. The purpose of this study is to develop a monitoring system that will facilitate the performance of nurses in monitoring the parameters of the baby incubator. Related to this, an incubator device is needed that can be monitored remotely. This module uses a wifi network system for data transmission. Using the ESP32 module assembled into a module for monitoring and control which will then be displayed on the LCD and in the blynk as a monitoring display. Data transmission will be communicated using an external WIFI network and the monitoring results of each sensor will be displayed on Blynk. The highest error values were 1.24% for incubator room temperature parameters and 1.15% for skin temperature parameters. The results showed that there were still inaccuracies in some parameter readings, one of the factors that read inaccurate parameters was sensor sensitivity. This research is expected to help medical personnel to facilitate monitoring the condition of premature babies in baby incubators.

Design of Incu Analyzer for IOT based Baby Incubator Calibration (Chamber Temperature)

Incubator Analyzer is a calibration tool used to measure temperature, mattress temperature, humidity, airflow, and sound level in baby incubators. The purpose of this research is to make a tool "Design of Incu Analyzer for IOT based Baby Incubator Calibration (Chamber Temperature)” with LCD display and Thingspeak. The design of this calibration tool consists of baby incubator chamber temperature parameters consisting of 5 points, namely T1, T2, T3 , T4, T5 using the DS18B20 sensor and using the ESP32 microcontroller to take advantage of the IOT system. Utilization of IOT on ESP32 is using Wi-Fi. The ESP32 microcontroller processes data from the DS18B20 temperature sensor which is then displayed on the LCD display and Thingspeak. Testing of this tool is done by comparing incubator analyzer module with a standard measuring instrument, INCU II. In this study, the temperature parameter obtained the smallest error value of -0,059293% at T5 with a setting temperature of 36°C and the largest error value of -0,0254188% at T2 with a setting temperature of 35°C. Overall the conclusions obtained after carrying out the literature study process, and planning, it can be concluded that the tool "Incu Analyzer Design for IOT based Baby Incubator Calibration" can be used according to the plan, namely Incubator Analyzer with IOT based.

Advancing healthcare: integrating telemedicine with baby incubators at puskesmas dampit to ensure seamless health services

Puskesmas in Malang Regency do not yet have facilities for monitoring LBW in real time via android either by health workers at the health centre or the patient's family. Puskesmas Dampit serves communities spread across several villages with a coverage area spread across several villages. This is an obstacle for families to get access to monitor the patient's progress, because the patient is in the NICU or ICU room. Another thing is that there is no training for users in the use of telemedicine-based Baby Incubator equipment. The role of users and families of patients is very important to keep the equipment in good condition. However, operating trainings based on new technology for users have never been conducted. Efforts to solve the above problems are to carry out counselling and assistance in the use of baby incubator equipment to users including nurses, midwives and other non-health worker users, so as to increase the knowledge of officers or users which in turn will have an impact on increasing the productivity of officers or users and improving the quality of service at the health centre. The purpose of this Community Service is to increase the role and participation of Poltekkes Kemenkes Surabaya in improving the degree of public health in the Puskesmas Dampit area, Malang Regency. The results of community service through the application of telemedicine-based Baby Incubator at Puskesmas Dampit are increased knowledge and skills regarding telemedicine-based baby incubator equipment. This will improve health services, especially for babies who need intensive care. The implementation of this technology provides significant benefits, including increased accessibility of health services, speed of response to patient health conditions, family involvement in care, and efficient use of resources. Assistance to Puskesmas personnel and patient families in the form of training, monitoring and evaluation of the use of appropriate technology in the form of Telemedicine-based baby incubator utilization.

INFANT INCUBATOR MONITORING AND EMERGENCY ALARM SYSTEM USING THE INTERNET OF THINGS

Giving birth to a child is one of the most precious moments in life. Every second a new life is brought into the world and not many children are lucky enough to be healthy. Monitoring the health conditions of a baby in an incubator is considered a critical medical issue. Baby incubators play a vital role in the life-saving of premature babies since premature newborn babies cannot regulate their body temperatures on their own like normal newborn babies. Hence, there is a need for health caregiver interactions due to certain conditions. So a novel-based solution is provided in this paper for the above-mentioned problem by using the Internet of Things (IoT) in incubators to alert the neonatal nurses so that preventive measures can be taken. It prevents the theft from being held in the incubator rooms of the baby. The parameters such as heartbeat, temperature, wetness, etc., of the baby are monitored, measured, and saved in the cloud for every 10 minutes. The power failure is also intimated within a short span. Index Terms— Incubators, Internet of Things, Alerting, Neonatal, Parameters, Heartbeat, Temperature, Wetness, Cloud, Power Failure

Assessment of Ambient Air Quality in Neonatal Incubators at Selected Sites of Baghdad Hospitals

An infant incubator in the neonatal intensive care unit (NICU) is a medical instrument of care that provides oxygen, warmth and moisture to a newborn baby. Due to environmental conditions affecting the infants foster babies may experience discomfort and pain at some point. Thus, this study aimed to assess ambient air quality in neonatal incubators to improve the environmental quality of neonatal intensive care units and safety. Air pollutants concentrations consisting of particulate matter (pm2.5, pm10), hydrocarbons (HOCH), volatile organic compounds (VOC), air quality index (AQI), humidity and temperature, were measured at four selected Baghdad hospitals (Al-Karkh and Rusafa) . The results showed that the increase in relative humidity (RH%) measured in all baby incubators and in all locations during the summer and winter seasons contributed to an increase in polluting gases emission inside the incubators, which was the highest measurement that gave significant difference for the incubator.The highest recorded measurement with significant difference for pm2.5 was 73.78 ± 0.096 in site2 in winter, pm10 was 106.73 ± 0.05 in site1 in winter, HCHO was 0.148 ± 0.005 in site3 in winter, VOC was 673 ± 0.005mg/m3 in site2 in winter, AQIwas 177.25 ± 0.5 in site2 in winter, RH% was 53.887 in the site3 during the winter and temperature was 32.25°C in site3 in summer. It can be concluded that there is a relationship between relative humidity and temperature that affectedthe gases concentrations as it showed variations in gases and particle matters concentrations which affected the air quality inside the neonatal incubatorsthat in turn affected the health of the neonates, especially increased severity of asthma attacks and low Intelligence quotient (IQ) in children in the future.

Monitoring Baby Incubator Central through Internet of Things (IoT) based on Raspberry Pi Zero W with Computer Monitoring)

Premature infants, with a gestational age of 37 weeks or less and weighing less than 2500 grams, face challenges in adapting to the outside world due to the underdeveloped state of their organ systems. Special care is essential to support their growth and development, including maintaining optimal temperature, humidity, and oxygen levels similar to those experienced in the mother's womb. Baby incubators play a crucial role in providing these conditions. This study aims to develop a convenient monitoring tool for midwives and healthcare workers responsible for multiple baby incubators. The tool operates independently and is compatible with various brands of baby incubators. Leveraging an IoT system, it utilizes ESP32 and Raspberry Pi Zero W modules for data transmission. The tool incorporates a DS18B20 sensor for monitoring skin temperature and a DHT22 sensor for monitoring the temperature inside the incubator chamber. Using a pre-experimental design with an after-only research design, the tool's measurements are compared against an incubator analyzer. The study reveals that the developed module still exhibits some measurement errors. However, despite these limitations, the research is expected to greatly assist medical personnel in simultaneously monitoring multiple baby incubators.
 Keywords : Premature infants, Baby incubators, IoT, Temperature, ESP32, Raspberry Pi Zero W

Sensor Accuracy Analysis on Incubator Analyzer to Measure Noise and Airflow Parameters

Infant incubators are equipment to maintain a stable body temperature for premature babies. Premature babies need room conditioning that is close to conditions in the womb. Room conditioning is carried out in a baby incubator by providing a stable temperature, relative humidity, and measured air flow. This parameter must be controlled so as not to exceed the threshold that will harm the baby. Periodic calibration should be applied to the infant incubator to monitor its function. To ensure the availability of baby incubators according to service standards, it is necessary to conduct test (calibrate) using an incubator analyzer. The purpose of this study is to conduct further research on the incubator analyzer that focuses on discussing the accuracy of noise and airflow sensors with the gold standard. In this study, an experiment was carried out for the sensitivity level of several sensors that had been treated by giving treatment to sensors to choose sensors with good sensitivity to be assembled into one in the incubator analyzer module. The noise sensors (KY-037 and Analog Sound Sensor V2.2) were further compared with the values ​​on the sound level meter and the airflow sensor (D6F-V03A1) was compared with the anemometer. Sensors whose values ​​are close to the comparison values ​​were selected to be integrated into the incubator analyzer module. The incubator analyzer module used Arduino Mega2560 as a data processor and was equipped with an SD Card for the data storage. The built incubator analyzer module was also compared to the Fluke INCU II gold standard for data analysis. The results showed that the Analog Sound Sensor V2.2 had the highest error value (-4.6%) at 32°C and the D6F-V03A1 had the ability to measure sensitivity, where the results were more accurate than INCU II. Based on the error value of the noise sensor, the V2.2 sensor can be applied to measure noise in the baby incubator and the D6F-V03A1 airflow sensor produced an accuracy of up to 3 digits behind the comma which is more accurate than the standard module. The results of the INCU analyzer from this study can be used to calibrate the baby incubator, so that the certainty of the feasibility of the baby incubator is guaranteed. This research can be used as a reference for other researchers who will develop research on incubator analyzers in the future.

Baby Incubator Monitoring Center Using Wi-Fi Network for Data Transmission

Premature babies born before 38 weeks have a high risk of dying, requiring urgent care. Premature babies receiving intensive care in incubators need certain temperature, humidity, and a quiet room. In addition, the baby should also be ensured that his body temperature remains normal during the treatment period. Based on the condition of these vulnerable babies, it is very necessary to have a temperature monitoring system in the incubator. Therefore, the purpose of this research is to develop a baby incubator monitoring system that still uses the manual method to make it faster, easier, and more practical. This module is a tool that works independently and is not part of the baby incubator. Therefore, any brand of the incubator can be monitored using this module. The system in this module is wireless and uses a Wi-Fi network for data transmission. By using several ESP32 modules assembled into a central system, the data obtained from monitoring each sensor will be processed by the ESP32 which functions as a client, and collected at the central point of the ESP32 which functions as a server using the internal wifi network of the ESP32 itself as data transmission. The data from the server will subsequently be shown on the TFT Nextion display. Based on the overall measurement results using 2 baby incubators, the highest error values ​​were 1.387% for the incubator temperature parameter, 3.911% for the skin temperature parameter, 10.5% for the humidity parameter, and 25.692% for the noise parameter. The results showed that the module that was made still contained errors in each measurement. The results of this study will assist nurses in more simply and swiftly monitoring premature newborns.

Analysis Of Baby Incubator Humidity Based PID with Kangaroo Mode

The main factor that affects the parameters of the workings of humidity in the baby incubator is the sensor used to read the humidity in the room. The purpose of this study is to analyze the humidity sensor in the baby incubator using 2 humidity sensors (DHT11 and DHT22) in the different location. The manufacture of this device used an after-only design, with a comparison device of INCU Analyzer, DHT11, and DHT22 sensors. Based on the measurement, DHT11 produced a value of 46%, while DHT22 produced a value of 55.45% with BPFK standards of 50%-70%. Based on the results of measurements using the INCU Analyzer, the average error value for DHT11 is 16.05%, while DHT22 is 3.47%. Therefore, the results showed that the DHT22 sensor was more accurate to be used in baby incubators because the measurement results were under BPFK standards and produced a low error value. This can be further implemented in a baby incubator making to improve the health and safety of the babies

Humidity monitoring of neonatal intensive care unit based on programmable system on chip

A premature baby's treatment takes place in the Neonatal Intensive Care Unit (NICU). The NICU is an isolated room and it consists of a number of baby incubators and measuring, monitoring devices such as incubator, overhead heater, monitors, ambient oxygen analyser, intravenous drip, feeding pump and tubes, power supply, ventilator monitor, ventilator, etc. The measuring and controlling parameters are temperature of baby and baby incubator, oxygen level, CO2 level, pulse rates, humidity, light intensity etc. This research paper deals with monitoring the humidity of baby incubators. The increase or decrease of humidity levels causes an effect on the baby's health. High humidity creates problems such as heat exhaustion, heat stroke and an overproduction of mold causes allergies. The monitoring and controlling of humidity are the most important parameters in NICU. The humidity of the baby incubator is monitored using a humidity sensor. The sensing data is given to the Programmable System on Chip (PSoC). The system under investigation is designed successfully and reported in this paper.

Development and Performance Study of Temperature and Humidity Regulator in Baby Incubator Using Fuzzy-PID Hybrid Controller

Technological developments in the health sector for the safety of neonates are essential. Such efforts are needed to curb the increase in premature infant mortality cases caused by bacteria, asphyxia, infections, and poor management of facility equipment. Furthermore, preterm and other at-risk babies have low ability to regulate temperature and produce body heat as characterized by their dry skin conditions; hence, the need for baby incubators. For their operation, these baby incubators provide strict regulated energy change that is influenced by heat transfer caused by the surrounding atmospheric temperature and humidity. This paper presents the design, construction, and performance study of a proposed Fuzzy-PID hybrid control system for regulating temperature and humidity in a baby incubator. To accomplish its goal, the proposed controller must be able to distribute heat and maintain humidity in the incubator under fluctuating atmospheric conditions to keep the baby’s body warm. Performance tests of the proposed hybrid controller were conducted by comparing temperature and humidity outputs in the baby incubator against predetermined expected values. Results show that the proposed controller is able to successfully achieve and maintain the temperature and humidity set points. Further examination also suggests that the proposed Fuzzy-PID hybrid control offers an improved overall system response performance compared to the PID controller.

RANCANG BANGUN SISTEM PEMUTUS ARUS SEBAGAI PENGAMAN TEMPERATUR BERLEBIH PADA INKUBATOR BAYI

Several countries in the world, including Indonesia, have reported baby-care accidents in the baby incubator due to the failure of the temperature control system to maintain a stable temperature. Therefore, this study reports the design of an circuit breaker system as an over-temperature safety in a baby incubator. Experimentally, the prototype consists of hardware design, software design, and testing system design. The hardware design has four main part, sensing layer using PT1000 as a temperature sensor, data processing layer using an ESP32 microcontroller, a circuit breaker system using a solid-state relay as an electronic switching, and LCD as a display. System testing has been done by evaluating the temperature readings on a prototype against the HTC-02 digital thermometer instrument and carried out in the electromedical engineering laboratory. The results show the accuracy of the temperature parameter is 99% and the circuit breaker system is able to cut the current from the power supply to the appliance when the temperature is more than 39°C which is indicated by an incandescent lamp "off" where this is equivalent to a heater at the baby incubator is in the "off" condition. From the results of this test, it can be concluded that the circuit breaker system as a safety over temperature in the baby incubator has functioned according to the design.Recommended the development of this prototype needs to be followed up by testing on low-tech baby incubators available at basic health care facilities so as to improve technology readiness to be effective in the community.

Design of an IoT-based smart incubator that listens to the baby

In the industrial era 4.0, domestic baby incubator producers are facing the challenge of free trade of foreign products that will compete in innovation with the application of IoT technology. One of the opportunities that arise consciously or unconsciously at the NICU (Neonatal Intensive Care Unit) unit in Hospitals, in general, is that there are no facilities for parents to monitor the baby’s condition inside the incubator directly. The purpose of this research project is to build a prototype of an internet-based baby incubator monitoring system based on things equipped with various sensors that will send data to the server in real-time and mobile apps for monitoring facilities for parents, including the voice of the baby. This study focused on how to develop baby incubators that can listen to baby’s crying, capture the voice, and interpret it using artificial intelligence. More than 40 (forty) voice datasets were used and successfully classified into five possible terms of the baby’s condition: burping, sleepy, hungry, uncomfortable, and pain by energy signal and spectrum analysis. The benefit of this research is as a driver of innovation for baby incubator products that will support the national medical devices manufacturing company.

Development of Incubator Analyzer Using Personal Computer Equiped With Measurement Certificate

Baby incubators are used for premature babies where babies are born prematurely. To ensure the accuracy of medical devices, periodic tests and controls need to be carried out aimed at reducing the risk of measurement. The baby incubator can be tested with a calibration device which is a device to calibrate temperature, noise, humidity, and airflow so that conditions remain stable and within normal limits. The purpose of this study was the development of a PC (Personal Computer) performing Incubator Analyzer with noise and airflow parameters. This type of research uses after only design. The standard incubator analyzer is not equipped with a PC and data processing via Excel, so the researchers created an Incubator Analyzer device that has four parameters to measure temperature, noise, humidity, and airflow. Using the Atmega328 Microcontroller as a data processor, equipped with sending data via Bluetooth HC-05 with data storage and output results will be displayed on a PC. Noise parameters using the Analog Sound Sensor V2 sensor and have the biggest error at 37oC setting temperature that is equal to 0.17%. While the airflow parameter uses an airflow sensor with type D6F-V3A01 and has the biggest error that is 0.5% at a temperature setting of 36oC and 37oC. The use of displays on personal computers and data processing using Excel, allows users to monitor calibration and data processing. The feasibility of this device is proven. Therefire, this design can be used for baby incubator calibration.