Fetal Movement Detection Dataset Recorded Using MPU9250 Tri-Axial Accelerometer

Delay, Upekha; Dissanayake, Sajan; Nawarathne, Thoshara; Wasalaarachchi, W.A.W.S.; Arachchi, Anupama Hetti; Abeywardhana, S.A.Y.; Withanage, Thanushi; Gunarathne, Samitha; Ekanayake, M. P. B.; Godaliyadda, G. M. R. I.; Wijayakulasooriya, J. V.; Rathnayake, R.M.C.J.

doi:10.17632/7svcy4cscy.2

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2021

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“…There is a risk that those may emit harmful radiations to the fetus [14,15]. We found a dataset that comprises accelerometric data obtained in a non-invasive manner [16]. But it was observed that those signals had the tendency to get interfered with external noises.…”

Section: Objectivementioning

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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Section: Objectivementioning

confidence: 99%