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
Affiliations
Melvin Chan
H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada
Emmanuel K. Tse
H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada
Seraph Bao
H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada
Mai Berger
H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada
Nadia Beyzaei
H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada
Mackenzie Campbell
H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada
Heinrich Garn
Austrian Institute of Technology, Austria
Hebah Hussaina
H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada
Gerhard Kloesch
Department of Neurology, Medical University of Vienna, Vienna, Austria
Bernhard Kohn
Austrian Institute of Technology, Austria
Boris Kuzeljevic
Clinical Research Support Unit, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada
Yi Jui Lee
Department of Mechanical Engineering, Faculty of Applied Science, University of British Columbia, Vancouver, British Columbia, Canada
Khaola Safia Maher
H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada
Natasha Carson
H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada
Jecika Jeyaratnam
H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada
Scout McWilliams
H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada
Karen Spruyt
Institute National de la Santé et de la Recherche Médicale (INSERM), Paris, France
Hendrik F. Machiel Van der Loos
Department of Mechanical Engineering, Faculty of Applied Science, University of British Columbia, Vancouver, British Columbia, Canada
Calvin Kuo
School of Kinesiology, Faculty of Education and Department of Computer Science, Faculty of Science, University of British Columbia, Vancouver, British Columbia, Canada
Osman Ipsiroglu
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; 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.