Data in Brief (Apr 2021)

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

  • 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

Journal volume & issue
Vol. 35
p. 106770

Abstract

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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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