Non Invasive Wearable Device for Fetal Movement Detection

Delay, Upekha; Nawarathne, B.M.T.M.; Dissanayake, Don W; Ekanayake, M. P. B.; Godaliyadda, G. M. R. I.; Wijayakulasooriya, J. V.; Rathnayake, R.M.C.J.

doi:10.1109/iciis51140.2020.9342662

Cited by 17 publications

(8 citation statements)

References 10 publications

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“…Therefore, the possibility of successfully implementing a simple algorithm is very low. Moreover, several studies were conducted previously to the same data set utilizing simple algorithms such as eigen factorization and simple neural network [15,27]. However, the accuracies reached by these algorithms were quite low.…”

Section: � Wh ð1þmentioning

confidence: 99%

Novel non-invasive in-house fabricated wearable system with a hybrid algorithm for fetal movement recognition

et al. 2021

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Fetal movement count monitoring is one of the most commonly used methods of assessing fetal well-being. While few methods are available to monitor fetal movements, they consist of several adverse qualities such as unreliability as well as the inability to be conducted in a non-clinical setting. Therefore, this research was conducted to design a complete system that will enable pregnant mothers to monitor fetal movement at home. This system consists of a non-invasive, non-transmitting sensor unit that can be fabricated at a low cost. An accelerometer was utilized as the primary sensor and a micro-controller based circuit was implemented. Clinical testing was conducted utilizing this sensor unit. Two phases of clinical testing procedures were done and during the first phase readings from 120 mothers were taken while during the second phase readings from 15 mothers were taken. Validation was done by conducting an abdominal ultrasound scan which was utilized as the ground truth during the second phase of the clinical testing procedure. A clinical survey was also conducted in parallel with clinical testings in order to improve the sensor unit as well as to improve the final system. Four different signal processing algorithms were implemented on the data set and the performance of each was compared with each other. Out of the four algorithms three algorithms were able to obtain a true positive rate around 85%. However, the best algorithm was selected on the basis of minimizing the false positive rate. Consequently, the most feasible as well as the best performing algorithm was determined and it was utilized in the final system. This algorithm have a true positive rate of 86% and a false positive rate of 7% Furthermore, a mobile application was also developed to be used with the sensor unit by pregnant mothers. Finally, a complete end to end method to monitor fetal movement in a non-clinical setting was presented by the proposed system.

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Section: � Wh ð1þmentioning

confidence: 99%

Novel non-invasive in-house fabricated wearable system with a hybrid algorithm for fetal movement recognition

et al. 2021

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“…Wasalaarachchi et al [ 38 ] proposed an automatic fetal movement counting algorithm based on nonnegative matrix factorization (NMF) and spectral clustering, combined with a home-based wearable device. Delay et al [ 39 , 47 ] developed a noninvasive fetal movement recognition system incorporating a convolutional neural network (CNN) hybrid algorithm. Morita et al [ 40 ] used accelerometers to count fetal movements in small for gestational age (SGA) infants and determined that SGA was associated with decreased fetal movements.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, with the rapid development of intelligent sensing devices and the advancement of modern digital information processing technology, automatic recognition of fetal movements using microacceleration sensors and efficient signal processing algorithms has received wide attention [29][30][31][32][33][34][35][36][37][38][39][40][41][42]. e accelerometer sensor is embedded in a wearable device and worn on the abdomen of pregnant women to detect a series of micromovements on the surface of the abdomen.…”

Section: Introductionmentioning

confidence: 99%

Passive Fetal Movement Recognition Approaches Using Hyperparameter Tuned LightGBM Model and Bayesian Optimization

Liang

Peng

et al. 2021

Computational Intelligence and Neuroscience

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Fetal movement is an important clinical indicator to assess fetus growth and development status in the uterus. In recent years, a noninvasive intelligent sensing fetal movement detection system that can monitor high-risk pregnancies at home has received a lot of attention in the field of wearable health monitoring. However, recovering fetal movement signals from a continuous low-amplitude background that is heavily contaminated with noise and recognizing real fetal movements is a challenging task. In this paper, fetal movement can be efficiently recognized by combining the strength of Kalman filtering, time and frequency domain and wavelet domain feature extraction, and hyperparameter tuned Light Gradient Boosting Machine (LightGBM) model. Firstly, the Kalman filtering (KF) algorithm is used to recover the fetal movement signal in a continuous low-amplitude background contaminated by noise. Secondly, the time domain, frequency domain, and wavelet domain (TFWD) features of the preprocessed fetal movement signal are extracted. Finally, the Bayesian Optimization algorithm (BOA) is used to optimize the LightGBM model to obtain the optimal hyperparameters. Through this, the accurate prediction and recognition of fetal movement are successfully achieved. In the performance analysis of the Zenodo fetal movement dataset, the proposed KF + TFWD + BOA-LGBM approach’s recognition accuracy and F1-Score reached 94.06% and 96.85%, respectively. Compared with 8 existing advanced methods for fetal movement signal recognition, the proposed method has better accuracy and robustness, indicating its potential medical application in wearable smart sensing systems for fetal prenatal health monitoring.

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“…One of the most current areas of interest in medicine is ensuring the fetus's health by discerning the activeness of the baby inside the womb to avoid miscarriage and intrauterine growth restrictions. Currently, there are several types of research going on to detect fetal movements from the spectral images constructed utilizing the signals obtained from the pregnant mother's abdomen [2][3][4][5][6]. However, due to the methods utilized to obtain these data, there is a tendency of the original signals being interfered by external noises, such as the addition of mother's movements, and the effect from the materials used in the wearable device.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Study on Denoising of Medical Images Utilizing Neural Network-Based Autoencoder

Nawarathne

Withanage

Gunarathne

et al. 2021

Advances in Intelligent Systems and Computing

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Fetal motion discernment utilizing spectral images extracted from accelerometric data incident on pregnant mothers abdomen has gained substantial attention in the state-of-the-art research. It is an essential practice to avoid adverse scenarios such as stillbirths and intrauterine growth restrictions. However, this endeavor of ensuring fetus safety has been arduous due to the existence of random noise in medical images. This novel research is an in-depth approach to analyze how the interference of different noise variations affects the retrieval of information in those

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Non Invasive Wearable Device for Fetal Movement Detection

Cited by 17 publications

References 10 publications

Novel non-invasive in-house fabricated wearable system with a hybrid algorithm for fetal movement recognition

Novel non-invasive in-house fabricated wearable system with a hybrid algorithm for fetal movement recognition

Passive Fetal Movement Recognition Approaches Using Hyperparameter Tuned LightGBM Model and Bayesian Optimization

Comprehensive Study on Denoising of Medical Images Utilizing Neural Network-Based Autoencoder

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