Smart Health Monitoring System for Pregnant Women

Ansari, Saniya M.; Ansari, Murtuza

doi:10.35940/ijeat.d7114.049420

Cited by 5 publications

(2 citation statements)

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“…Instead of ultra sound scan which is costlier practice to examine baby movement, accelerometer is deployed to derive baby movements in name of "kicks". Ansari and Sainya Ansari [20] worked together and brought an IoT based system to track healthiness of pregnant ladies. For evaluating the state of patient, the mandatory sensors used are heart rate, weight, BP and temperature.…”

Section: A Related Workmentioning

confidence: 99%

A Smart Remote Monitoring System for Prenatal Care in Rural Areas

Dhanwanth¹,

Saravanakumar²,

Tamilselvi³

et al. 2023

IJRITCC

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The complications in maternity especially the women lives in rural sector can be reduced through regular monitoring of their vitals like blood pressure, SpO2 and fetal growth. The internet of things (IoT) is the modern technology bridges the gap between the traditional clinical setting with its consumers as well promotes the telemedicine industry into great levels of accessing proactive healthcare facilities. The predominant aim of this work is to bring a remote monitoring device which assesses the significant health indicators of the pregnant women and their fetus status cost effectively. In order to build such kit, the biosensors like heart rate, SpO2, pressure, temperature and load cell which gives the weight of the fetus are integrated into Arudino board. The sensor readings are processed through ThingSpeak. The timely medical attention is proposed upon observing abnormal physiological vitals of the women which is implemented through a buzzer system in this device. Like such devices in realism help to predict the pregnancy risk and decrease the mortality rate.

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Section: A Related Workmentioning

confidence: 99%

A Smart Remote Monitoring System for Prenatal Care in Rural Areas

Dhanwanth¹,

Saravanakumar²,

Tamilselvi³

et al. 2023

IJRITCC

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“…These researchers utilize machine learning in diagnosis and medical prediction, including decision tree classification methods C4.5 and Naïve Bayes Kernel, which can predict and display gestational risk [11][12] as well as normal or abnormal conditions of pregnancy [13].…”

Section: Introductionmentioning

confidence: 99%

Model Structure of Fetal Health Status Prediction

Bahar

Arimbi

et al. 2022

JUITA

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One of the issues of pregnant mothers in Indonesia is their access speed and accuracy services availability towards the prediction of fetus or baby conceived during pregnancy. Thus, the research aimed to obtain the ability to predict three ranges of a fetal target, namely normal, risk, and abnormal condition. This research emphasized the modeling aspect of supervised learning using seven different algorithms to obtain an optimal working score. Those are Decision Tree, Gradient Boosting, Random Forest, SVM, k-NN, AdaBoost, and Stochastic Gradient Descent (SGD). The structure process is mainly divided into two steps, pre-process model and the prediction model. An early data pre-process is needed before executing. Prediction output indicated that dataset test is valid, and can be proven by comparing between the testing data table and prediction and testing table diagram. The resulting model has described the sequence for simulating the training and testing data model to produce the highest working score from the seven selected algorithms. The simulated data based on the model created is proved its validity thru three main filter processes, which are missing data solution, outlier data control, and data normalization. The result obtained a working score that has data proximity with a low score range of 0.063 from model evaluation, confusion matrix, and prediction output.

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Smart Fetal Health Monitor

Shilvya¹,

Jose²

2022

Lecture Notes in Electrical Engineering

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Smart Health Monitoring System for Pregnant Women

Cited by 5 publications

References 0 publications

A Smart Remote Monitoring System for Prenatal Care in Rural Areas

A Smart Remote Monitoring System for Prenatal Care in Rural Areas

Model Structure of Fetal Health Status Prediction

Smart Fetal Health Monitor

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