Numerical model development to predict the behaviour of infant/neonate crash dummy restrained inside of an incubator under deceleration

In this paper, advanced finite element (FE) methods are developed to investigate the effect of deceleration on the crash dummy test complied with British Standard Engineering (BS EN 1789). These techniques, which are related to material modelling, joints and contacts, offer an advanced numerical model representing an infant incubator with all complex boundary conditions and design contents. It is shown that the response of an infant incubator is a function of the ratchet straps, the tension on the belts, the belt type and the distance of the belts from the edges of the incubator, which can significantly affect the experienced acceleration, by the infant. The validation process is performed against experimental studies and various case parameters such as crash dummy mass and negative acceleration impulse are discussed in detail. The developed numerical model is capable to predict the behaviour of the crash dummy and the incubator in terms of acceleration, trajectory and kinematics by less than 8% error.

Solar Powered Infant Incubator for Assessment in Healthcare System

Monitoring baby health status in incubator is a necessary task and need more attention. Huge number of issues was happened previously like improper oxygen supply, theft of child and mishandling in continuous monitoring. Hence a proper automatic system should be needed for monitoring a baby in incubator with reduced human interaction. This can be achieved through sensors and projected as proposed system. Here temperature, humidity, heartbeat, pressure and accelerometer sensor are used to check baby body condition and if any drastic change is identified in it will be automatically intimated through buzzer. If any unauthorized person is picking up the child it will be detected by IR sensor and automatically door will be closed. Another major issue is the current status of a child is not transparent to respective parent here all the details gathered from the sensors will be automatically loaded in cloud through IoT which can viewed by their parent as well as doctor to analyze current status of the baby. The whole process is controlled by Raspberry PI and for continuous monitoring solar panel is added that provide power supply for our system sequentially without any interrupt. Hence it clearly shows that our proposed system achieves the objective of our work and protects baby both in physical and health based parameters.

A Low Cost Baby Incubator Design Equipped with Vital Sign Parameters

A baby incubator is a heating device for premature babies so that the baby's temperature matches the temperature in the mother's womb. Premature babies are babies born in abnormal conditions where the baby's body size is below average or the gestational age has not reached 9 months. The aim of this study was to create a compact system at a low cost in an infant incubator. Apart from stabilizing the temperature, it can also calculate the premature baby's heart rate and oxygen saturation value. The contribution of the system being built can detect the early symptoms of congenital abnormalities so that abnormalities detection becomes more practical. The temperature stability in this system uses a threshold system, where the sensor readings are compared with the set point value. Meanwhile, the SpO2 system uses a finger sensor attached to the baby's finger to detect oxygen saturation and heart rate. The signal from the sensor is processed in a signal conditioning circuit consisting of an astable circuit, filter and amplifier, LPF, and demultiplexer. Based on the results of testing and measurement, the difference produced by this tool is only 0.13 - 0.182 ° C. The biggest error in the system is 0.517%. Measurement of skin temperature was carried out on 10 respondents with an average value of 34.825 ° C. so that the difference generated from this tool is only 0.175 ° C, the difference on the Humidity parameter is 0.21%. The average BPM reading of respondent 1 was 81 beats per minute and the average SpO2 value was 97%. The average BPM reading of respondent 2 was 83 beats per minute and the average SpO2 value was 98%. You can see that the module with the Arduino control system can work automatically and is able to adjust the stability of the baby incubator according to the settings. In addition, the BPM and SpO2 systems are within tolerance so they can be used for patient measurements

Analysis of Temperature on Baby Incubator Control System

Premature babies who are born alive before 37th week of gestation or babies born with weight criteria of less than 2500 grams need to be treated in an infant incubator because they are very susceptible to viruses in open spaces and will provide protection to newborns by providing premature babies stable temperature and humidity. Stable temperature regulation is needed to prevent hypothermia or hyperthermia that will occur in premature babies. Care of the baby in an infant incubator causes the separation of mother and baby. Mothers who have premature babies are found to lack trust in caring for their babies compared to mothers who have term babies. The purpose of this study was to analysis of temperature on baby incubator control system. In order to get a stable and precise temperature, the temperature sensor is positioned according to the standard of the baby incubator at the hospital. This research uses LM35 and DS18B20 temperature sensors which will be processed using Atmega 328 Microcontroller and will be displayed on a PC (personal computer). Based on the measurement results obtained an average value of min difference for sensor readings on the LM35 has the largest value of 0.22°C and the average max value difference has the largest value of 0.38°C and the average value of the min difference for the sensor readings on the LM35 has a value the largest is 0.39°C and the average difference in the value of max has the largest value that is 0.55°C. It can be concluded that the LM35 sensor is better than the DS18B20 sensor because it has smaller oscillations and also good accuracy. The results of this study can be implemented in infant incubators in accordance with established standards and can be produced at affordable prices in the domestic market.

Cry Recognition for Infant Incubator Monitoring System Based on Internet of Things using Machine Learning

With the current technology trend of IoT and Smart Device, there is a possibility for the improvement of our infant incubator in responding to the real baby’s condition. This work is trying to see that possibility. First is by analyzing of open baby voice database. From there, a procedure to find out baby cry classification will be explained. The approach was starting with an analysis of sound’s power from that WAV files before going further into the 2D pattern, which will have features for the machine learning. From this work, around 85% accuracy could be achieved. Then together with sensors, it would be useful for infant incubator’s innovation by utilizing this proposed configuration.

Carbon Dioxide Level in Neonatal Incubator: A Comparative Study of Two Baby Air Flow Input Methods in Incubator Chamber

Preterm neonates often have to spend a long time in incubator which simulates necessary enviromental conditions to maintain patients in stable condition. Authors have found neglected problem with Carbon Dioxide (CO2) concentration in newborn inhaled gas when using “oxygen hood” into infant incubator that has not been studied very closely. Methods in this experiement were carried out as condition monitoring while changing some parameters and positions. Results have shown that CO2 level is letally high in some aspects. Situation is alarming as CO2 level in incubator is not limited by current regulation, as IEC60601-2-19: 2009 +A1:2016. Currently the Incubator manufacturers declare that CO2 maximal concentration should be periodically checked by users, which are hospitals. Some commercially available incubators have the maximum allowed CO2 level 8000 ppm, which is much higher than recommended 2000 ppm CO2 level in indoor working place. There is no limitation of maximal level of CO2 using “oxygen hood”, which are specified in incubator operation manual. This paper goal is to show the correlation of CO2 level versus gas flow into oxygen hood.