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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| Format: | Article |
| Language: | English |
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Elsevier
2021-04-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340921000548 |
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Article |
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DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Melvin Chan Emmanuel K. Tse Seraph Bao Mai Berger Nadia Beyzaei Mackenzie Campbell Heinrich Garn Hebah Hussaina Gerhard Kloesch Bernhard Kohn Boris Kuzeljevic Yi Jui Lee Khaola Safia Maher Natasha Carson Jecika Jeyaratnam Scout McWilliams Karen Spruyt Hendrik F. Machiel Van der Loos Calvin Kuo Osman Ipsiroglu |
| spellingShingle |
Melvin Chan Emmanuel K. Tse Seraph Bao Mai Berger Nadia Beyzaei Mackenzie Campbell Heinrich Garn Hebah Hussaina Gerhard Kloesch Bernhard Kohn Boris Kuzeljevic Yi Jui Lee Khaola Safia Maher Natasha Carson Jecika Jeyaratnam Scout McWilliams Karen Spruyt Hendrik F. Machiel Van der Loos Calvin Kuo Osman Ipsiroglu Fidgety Philip and the Suggested Clinical Immobilization Test: Annotation data for developing a machine learning algorithm Data in Brief Movement disorders Sleep-related movement disorders Misdiagnosis Over-medication Adverse drug reactions |
| author_facet |
Melvin Chan Emmanuel K. Tse Seraph Bao Mai Berger Nadia Beyzaei Mackenzie Campbell Heinrich Garn Hebah Hussaina Gerhard Kloesch Bernhard Kohn Boris Kuzeljevic Yi Jui Lee Khaola Safia Maher Natasha Carson Jecika Jeyaratnam Scout McWilliams Karen Spruyt Hendrik F. Machiel Van der Loos Calvin Kuo Osman Ipsiroglu |
| author_sort |
Melvin Chan |
| title |
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_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_sort |
fidgety philip and the suggested clinical immobilization test: annotation data for developing a machine learning algorithm |
| publisher |
Elsevier |
| series |
Data in Brief |
| issn |
2352-3409 |
| publishDate |
2021-04-01 |
| 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. |
| topic |
Movement disorders Sleep-related movement disorders Misdiagnosis Over-medication Adverse drug reactions |
| url |
http://www.sciencedirect.com/science/article/pii/S2352340921000548 |
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doaj-992cf8bb71fd4cdcadadfbb27eddc5132021-04-26T05:55:59ZengElsevierData in Brief2352-34092021-04-0135106770Fidgety Philip and the Suggested Clinical Immobilization Test: Annotation data for developing a machine learning algorithmMelvin Chan0Emmanuel K. Tse1Seraph Bao2Mai Berger3Nadia Beyzaei4Mackenzie Campbell5Heinrich Garn6Hebah Hussaina7Gerhard Kloesch8Bernhard Kohn9Boris Kuzeljevic10Yi Jui Lee11Khaola Safia Maher12Natasha Carson13Jecika Jeyaratnam14Scout McWilliams15Karen Spruyt16Hendrik F. Machiel Van der Loos17Calvin Kuo18Osman Ipsiroglu19H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, CanadaH-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, CanadaH-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, CanadaH-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, CanadaH-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, CanadaH-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, CanadaAustrian Institute of Technology, AustriaH-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, CanadaDepartment of Neurology, Medical University of Vienna, Vienna, AustriaAustrian Institute of Technology, AustriaClinical Research Support Unit, BC Children's Hospital Research Institute, Vancouver, British Columbia, CanadaDepartment of Mechanical Engineering, Faculty of Applied Science, University of British Columbia, Vancouver, British Columbia, CanadaH-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, CanadaH-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, CanadaH-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, CanadaH-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, CanadaInstitute National de la Santé et de la Recherche Médicale (INSERM), Paris, FranceDepartment of Mechanical Engineering, Faculty of Applied Science, University of British Columbia, Vancouver, British Columbia, CanadaSchool of Kinesiology, Faculty of Education and Department of Computer Science, Faculty of Science, University of British Columbia, Vancouver, British Columbia, CanadaH-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; Corresponding author at: H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada.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.http://www.sciencedirect.com/science/article/pii/S2352340921000548Movement disordersSleep-related movement disordersMisdiagnosisOver-medicationAdverse drug reactions |