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Writhing Movement Detection in Newborns on the Second and Third Day of Life Using Pose-Based Feature Machine Learning Classification
Observation of neuromotor development at an early stage of an infant’s life allows for early diagnosis of deficits and the beginning of the therapeutic process. General movement assessment is a method of spontaneous movement observation, which is the foundation for contemporary attempts at objectifi...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7660095/ https://www.ncbi.nlm.nih.gov/pubmed/33105787 http://dx.doi.org/10.3390/s20215986 |
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author | Doroniewicz, Iwona Ledwoń, Daniel J. Affanasowicz, Alicja Kieszczyńska, Katarzyna Latos, Dominika Matyja, Małgorzata Mitas, Andrzej W. Myśliwiec, Andrzej |
author_facet | Doroniewicz, Iwona Ledwoń, Daniel J. Affanasowicz, Alicja Kieszczyńska, Katarzyna Latos, Dominika Matyja, Małgorzata Mitas, Andrzej W. Myśliwiec, Andrzej |
author_sort | Doroniewicz, Iwona |
collection | PubMed |
description | Observation of neuromotor development at an early stage of an infant’s life allows for early diagnosis of deficits and the beginning of the therapeutic process. General movement assessment is a method of spontaneous movement observation, which is the foundation for contemporary attempts at objectification and computer-aided diagnosis based on video recordings’ analysis. The present study attempts to automatically detect writhing movements, one of the normal general movement categories presented by newborns in the first weeks of life. A set of 31 recordings of newborns on the second and third day of life was divided by five experts into videos containing writhing movements (with occurrence time) and poor repertoire, characterized by a lower quality of movement in relation to the norm. Novel, objective pose-based features describing the scope, nature, and location of each limb’s movement are proposed. Three machine learning algorithms are evaluated in writhing movements’ detection in leave-one-out cross-validation for different feature extraction time windows and overlapping time. The experimental results make it possible to indicate the optimal parameters for which 80% accuracy was achieved. Based on automatically detected writhing movement percent in the video, infant movements are classified as writhing movements or poor repertoire with an area under the ROC (receiver operating characteristics) curve of 0.83. |
format | Online Article Text |
id | pubmed-7660095 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-76600952020-11-13 Writhing Movement Detection in Newborns on the Second and Third Day of Life Using Pose-Based Feature Machine Learning Classification Doroniewicz, Iwona Ledwoń, Daniel J. Affanasowicz, Alicja Kieszczyńska, Katarzyna Latos, Dominika Matyja, Małgorzata Mitas, Andrzej W. Myśliwiec, Andrzej Sensors (Basel) Article Observation of neuromotor development at an early stage of an infant’s life allows for early diagnosis of deficits and the beginning of the therapeutic process. General movement assessment is a method of spontaneous movement observation, which is the foundation for contemporary attempts at objectification and computer-aided diagnosis based on video recordings’ analysis. The present study attempts to automatically detect writhing movements, one of the normal general movement categories presented by newborns in the first weeks of life. A set of 31 recordings of newborns on the second and third day of life was divided by five experts into videos containing writhing movements (with occurrence time) and poor repertoire, characterized by a lower quality of movement in relation to the norm. Novel, objective pose-based features describing the scope, nature, and location of each limb’s movement are proposed. Three machine learning algorithms are evaluated in writhing movements’ detection in leave-one-out cross-validation for different feature extraction time windows and overlapping time. The experimental results make it possible to indicate the optimal parameters for which 80% accuracy was achieved. Based on automatically detected writhing movement percent in the video, infant movements are classified as writhing movements or poor repertoire with an area under the ROC (receiver operating characteristics) curve of 0.83. MDPI 2020-10-22 /pmc/articles/PMC7660095/ /pubmed/33105787 http://dx.doi.org/10.3390/s20215986 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Doroniewicz, Iwona Ledwoń, Daniel J. Affanasowicz, Alicja Kieszczyńska, Katarzyna Latos, Dominika Matyja, Małgorzata Mitas, Andrzej W. Myśliwiec, Andrzej Writhing Movement Detection in Newborns on the Second and Third Day of Life Using Pose-Based Feature Machine Learning Classification |
title | Writhing Movement Detection in Newborns on the Second and Third Day of Life Using Pose-Based Feature Machine Learning Classification |
title_full | Writhing Movement Detection in Newborns on the Second and Third Day of Life Using Pose-Based Feature Machine Learning Classification |
title_fullStr | Writhing Movement Detection in Newborns on the Second and Third Day of Life Using Pose-Based Feature Machine Learning Classification |
title_full_unstemmed | Writhing Movement Detection in Newborns on the Second and Third Day of Life Using Pose-Based Feature Machine Learning Classification |
title_short | Writhing Movement Detection in Newborns on the Second and Third Day of Life Using Pose-Based Feature Machine Learning Classification |
title_sort | writhing movement detection in newborns on the second and third day of life using pose-based feature machine learning classification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7660095/ https://www.ncbi.nlm.nih.gov/pubmed/33105787 http://dx.doi.org/10.3390/s20215986 |
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