I. Jeya Daisy, G. Diyaneshwaran, K. Ravivarmaa, S. Shobana, M. Sneha, N. S. Monessha
{"title":"Review On Foetal Position Detection Using Different Techniques","authors":"I. Jeya Daisy, G. Diyaneshwaran, K. Ravivarmaa, S. Shobana, M. Sneha, N. S. Monessha","doi":"10.1109/ICDT57929.2023.10150712","DOIUrl":null,"url":null,"abstract":"Modern obstetrics places a high priority on foetal health monitoring. Although foetal movement is frequently used as a proxy for foetal health, it is difficult to accurately monitor foetal movement over an extended period of time without causing any harm. In high-risk pregnancies and in high-risk moms who have previously experienced miscarriages, it is highly helpful to determine the foetus position because, in the majority of cases, an incorrect foetal position results in both foetal and maternal mortality. Pregnant women may benefit from the design and construction of a device that can accurately identify the location of the foetus. Recent years have seen the development of a few accelerometer-based systems to address frequent problems with ultrasound measurement and allow for remote, self-managed monitoring of foetal movement throughout pregnancy. The optimum design for body-worn accelerometers, data processing, and deep learning methods used to identify foetal movement. This study will explore four alternative techniques for determining the location of the foetus. Ultrasonograms are the most popular methods for foetal position detection. The wearable ambulatory device known as Femom, which has been made available to women on home prescription, can also be used to determine the location of the foetus. Deep learning techniques and thermal imaging cameras are also utilised to determine the position of the foetus.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Disruptive Technologies (ICDT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDT57929.2023.10150712","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Modern obstetrics places a high priority on foetal health monitoring. Although foetal movement is frequently used as a proxy for foetal health, it is difficult to accurately monitor foetal movement over an extended period of time without causing any harm. In high-risk pregnancies and in high-risk moms who have previously experienced miscarriages, it is highly helpful to determine the foetus position because, in the majority of cases, an incorrect foetal position results in both foetal and maternal mortality. Pregnant women may benefit from the design and construction of a device that can accurately identify the location of the foetus. Recent years have seen the development of a few accelerometer-based systems to address frequent problems with ultrasound measurement and allow for remote, self-managed monitoring of foetal movement throughout pregnancy. The optimum design for body-worn accelerometers, data processing, and deep learning methods used to identify foetal movement. This study will explore four alternative techniques for determining the location of the foetus. Ultrasonograms are the most popular methods for foetal position detection. The wearable ambulatory device known as Femom, which has been made available to women on home prescription, can also be used to determine the location of the foetus. Deep learning techniques and thermal imaging cameras are also utilised to determine the position of the foetus.