Fetal Movement Identification Using Spectrograms with Attention Aided Models and Identifying a Set of Correlating Parameters with Gestational Age

Fetal movement monitoring is an essential part to ensure the safety of a fetus. Counting the number of kicks regularly and keeping track of the pattern is a well known method of evaluating fetal health. This process can be done at home or using a clinical setup but both these methods respectively have limitations such as low accuracy and unavailability to access equipment regularly. Therefore, devices that can be used in a home environment to record fetal movements are considered and algorithms that can identify fetal movements from such recordings are experimented. As attention mechanisms have shown the ability to better identify long time series events, the possibility of better identifying fetal movement occurrence using such models has been discussed in this paper as a novel use case and a novel attempt to correlate statistical parameters with the gestational age have also been discussed so that a fetus with irregularities such as decreased growth can be identified beforehand.