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

Autor:	Chan M; H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada., Tse EK; H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada., Bao S; H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada., Berger M; H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada., Beyzaei N; H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada., Campbell M; H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada., Garn H; Austrian Institute of Technology, Austria., Hussaina H; H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada., Kloesch G; Department of Neurology, Medical University of Vienna, Vienna, Austria., Kohn B; Austrian Institute of Technology, Austria., Kuzeljevic B; Clinical Research Support Unit, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada., Lee YJ; Department of Mechanical Engineering, Faculty of Applied Science, University of British Columbia, Vancouver, British Columbia, Canada., Maher KS; H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada., Carson N; H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada., Jeyaratnam J; H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada., McWilliams S; H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada., Spruyt K; Institute National de la Santé et de la Recherche Médicale (INSERM), Paris, France., Van der Loos HFM; Department of Mechanical Engineering, Faculty of Applied Science, University of British Columbia, Vancouver, British Columbia, Canada., Kuo C; School of Kinesiology, Faculty of Education and Department of Computer Science, Faculty of Science, University of British Columbia, Vancouver, British Columbia, Canada., Ipsiroglu O; H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada.; Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
Jazyk:	angličtina
Zdroj:	Data in brief [Data Brief] 2021 Jan 17; Vol. 35, pp. 106770. Date of Electronic Publication: 2021 Jan 17 (Print Publication: 2021).
DOI:	10.1016/j.dib.2021.106770
Abstrakt:	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. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article. (Crown Copyright © 2021 Published by Elsevier Inc.)
Databáze:	MEDLINE
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