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Fidgety Philip and the Suggested Clinical Immobilization Test: Annotation data for developing a machine learning algorithm

The cartoon Fidgety Philip, the banner of Western-ADHD diagnosis, depicts a ‘restless’ child exhibiting hyperactive-behaviors with hyper-arousability and/or hypermotor-restlessness (H-behaviors) during sitting. To overcome the gaps between differential diagnostic considerations and modern computing...

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Autores principales: Chan, Melvin, Tse, Emmanuel K., Bao, Seraph, Berger, Mai, Beyzaei, Nadia, Campbell, Mackenzie, Garn, Heinrich, Hussaina, Hebah, Kloesch, Gerhard, Kohn, Bernhard, Kuzeljevic, Boris, Lee, Yi Jui, Maher, Khaola Safia, Carson, Natasha, Jeyaratnam, Jecika, McWilliams, Scout, Spruyt, Karen, Van der Loos, Hendrik F. Machiel, Kuo, Calvin, Ipsiroglu, Osman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7851356/
https://www.ncbi.nlm.nih.gov/pubmed/33553523
http://dx.doi.org/10.1016/j.dib.2021.106770
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author Chan, Melvin
Tse, Emmanuel K.
Bao, Seraph
Berger, Mai
Beyzaei, Nadia
Campbell, Mackenzie
Garn, Heinrich
Hussaina, Hebah
Kloesch, Gerhard
Kohn, Bernhard
Kuzeljevic, Boris
Lee, Yi Jui
Maher, Khaola Safia
Carson, Natasha
Jeyaratnam, Jecika
McWilliams, Scout
Spruyt, Karen
Van der Loos, Hendrik F. Machiel
Kuo, Calvin
Ipsiroglu, Osman
author_facet Chan, Melvin
Tse, Emmanuel K.
Bao, Seraph
Berger, Mai
Beyzaei, Nadia
Campbell, Mackenzie
Garn, Heinrich
Hussaina, Hebah
Kloesch, Gerhard
Kohn, Bernhard
Kuzeljevic, Boris
Lee, Yi Jui
Maher, Khaola Safia
Carson, Natasha
Jeyaratnam, Jecika
McWilliams, Scout
Spruyt, Karen
Van der Loos, Hendrik F. Machiel
Kuo, Calvin
Ipsiroglu, Osman
author_sort Chan, Melvin
collection PubMed
description The cartoon Fidgety Philip, the banner of Western-ADHD diagnosis, depicts a ‘restless’ child exhibiting hyperactive-behaviors with hyper-arousability and/or hypermotor-restlessness (H-behaviors) during sitting. To overcome the gaps between differential diagnostic considerations and modern computing methodologies, we have developed a non-interpretative, neutral pictogram-guided phenotyping language (PG-PL) for describing body-segment movements during sitting (Journal of Psychiatric Research). To develop the PG-PL, seven research assistants annotated three original Fidgety Philip cartoons. Their annotations were analyzed with descriptive statistics. To review the PG-PL's performance, the same seven research assistants annotated 12 snapshots with free hand annotations, followed by using the PG-PL, each time in randomized sequence and on two separate occasions. After achieving satisfactory inter-observer agreements, the PG-PL annotation software was used for reviewing videos where the same seven research assistants annotated 12 one-minute long video clips. The video clip annotations were finally used to develop a machine learning algorithm for automated movement detection (Journal of Psychiatric Research). These data together demonstrate the value of the PG-PL for manually annotating human movement patterns. Researchers are able to reuse the data and the first version of the machine learning algorithm to further develop and refine the algorithm for differentiating movement patterns.
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spelling pubmed-78513562021-02-05 Fidgety Philip and the Suggested Clinical Immobilization Test: Annotation data for developing a machine learning algorithm Chan, Melvin Tse, Emmanuel K. Bao, Seraph Berger, Mai Beyzaei, Nadia Campbell, Mackenzie Garn, Heinrich Hussaina, Hebah Kloesch, Gerhard Kohn, Bernhard Kuzeljevic, Boris Lee, Yi Jui Maher, Khaola Safia Carson, Natasha Jeyaratnam, Jecika McWilliams, Scout Spruyt, Karen Van der Loos, Hendrik F. Machiel Kuo, Calvin Ipsiroglu, Osman Data Brief Data Article The cartoon Fidgety Philip, the banner of Western-ADHD diagnosis, depicts a ‘restless’ child exhibiting hyperactive-behaviors with hyper-arousability and/or hypermotor-restlessness (H-behaviors) during sitting. To overcome the gaps between differential diagnostic considerations and modern computing methodologies, we have developed a non-interpretative, neutral pictogram-guided phenotyping language (PG-PL) for describing body-segment movements during sitting (Journal of Psychiatric Research). To develop the PG-PL, seven research assistants annotated three original Fidgety Philip cartoons. Their annotations were analyzed with descriptive statistics. To review the PG-PL's performance, the same seven research assistants annotated 12 snapshots with free hand annotations, followed by using the PG-PL, each time in randomized sequence and on two separate occasions. After achieving satisfactory inter-observer agreements, the PG-PL annotation software was used for reviewing videos where the same seven research assistants annotated 12 one-minute long video clips. The video clip annotations were finally used to develop a machine learning algorithm for automated movement detection (Journal of Psychiatric Research). These data together demonstrate the value of the PG-PL for manually annotating human movement patterns. Researchers are able to reuse the data and the first version of the machine learning algorithm to further develop and refine the algorithm for differentiating movement patterns. Elsevier 2021-01-17 /pmc/articles/PMC7851356/ /pubmed/33553523 http://dx.doi.org/10.1016/j.dib.2021.106770 Text en Crown Copyright © 2021 Published by Elsevier Inc. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Chan, Melvin
Tse, Emmanuel K.
Bao, Seraph
Berger, Mai
Beyzaei, Nadia
Campbell, Mackenzie
Garn, Heinrich
Hussaina, Hebah
Kloesch, Gerhard
Kohn, Bernhard
Kuzeljevic, Boris
Lee, Yi Jui
Maher, Khaola Safia
Carson, Natasha
Jeyaratnam, Jecika
McWilliams, Scout
Spruyt, Karen
Van der Loos, Hendrik F. Machiel
Kuo, Calvin
Ipsiroglu, Osman
Fidgety Philip and the Suggested Clinical Immobilization Test: Annotation data for developing a machine learning algorithm
title Fidgety Philip and the Suggested Clinical Immobilization Test: Annotation data for developing a machine learning algorithm
title_full Fidgety Philip and the Suggested Clinical Immobilization Test: Annotation data for developing a machine learning algorithm
title_fullStr Fidgety Philip and the Suggested Clinical Immobilization Test: Annotation data for developing a machine learning algorithm
title_full_unstemmed Fidgety Philip and the Suggested Clinical Immobilization Test: Annotation data for developing a machine learning algorithm
title_short Fidgety Philip and the Suggested Clinical Immobilization Test: Annotation data for developing a machine learning algorithm
title_sort fidgety philip and the suggested clinical immobilization test: annotation data for developing a machine learning algorithm
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7851356/
https://www.ncbi.nlm.nih.gov/pubmed/33553523
http://dx.doi.org/10.1016/j.dib.2021.106770
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