Abstract:

The breathing techniques are important during pregnancy and labor, playing a signification role when the person is panicked, stressed or when the level of oxygen decreases inside the body. The medical students and graduates who have their residency program in obstetrics and gynecology analyze the breathing techniques for enhancing the life of pregnant women. The interpretation is done by them for eliminating the possible pregnancy complications and to decrease the anxiety of their patients.

The scope of the current paper is to present an online 3D instructor that is designed to improve the treatment based on breathing techniques of the targeted medical students and graduates. Moreover, the online application monitors remotely the pregnant women who are in the care of the associated medical staff. The non-invasive technique consists in classifying the person's inhale and exhale sounds which are gathered if the user allows the software to have access to the device's microphone.

The medical students use the sound recordings which are classified based on machine learning algorithms, namely the convolutional neural network (a) and the support vector machine (b) for three cases: panic (1), stress (2) and labor (3). Audio instructions are provided by the online 3D instructor according to the detected case and the sound classification is performed again. The process repeats until the user's state improves. There has been found that the sound classification is good for the both machine learning algorithms in order to determine the panic, stress and labor states of pregnant women. The outcomes help the medical students and obstetrics and gynecology residency graduates to use the most efficient breathing techniques.

Keywords:

3D application, student education, pregnant women, sound, machine learning