@article{3c9c910144e342c289d68b9508cdd320,
title = "Comparison of End-to-End Neural Network Architectures and Data Augmentation Methods for Automatic Infant Motility Assessment Using Wearable Sensors",
keywords = "classifier architectures, human activity recognition, infant motility, wearable technology, 217 Medical engineering, 3123 Gynaecology and paediatrics",
author = "Manu Airaksinen and Sampsa Vanhatalo and Okko R{\"a}s{\"a}nen",
note = "Funding Information: The research was funded by Academy of Finland grants no. 314602, 314573, 314450, 335778, 332017, and 343498, as well as project grants from Lastentautien tutkimuss{\"a}{\"a}ti{\"o}, Suomen Aivos{\"a}{\"a}ti{\"o} and Sigrid Juselius foundation. Open access funding provided by University of Helsinki. Publisher Copyright: {\textcopyright} 2023 by the authors.",
year = "2023",
month = apr,
doi = "10.3390/s23073773",
language = "English",
volume = "23",
journal = "Sensors",
issn = "1424-8220",
publisher = "MDPI",
number = "7",
}