@article{ref1,
title="Is Fidgety Philip's ground truth also ours? The creation and application of a machine learning algorithm",
journal="Journal of psychiatric research",
year="2020",
author="Beyzaei, Nadia and Bao, Seraph and Bu, Yanyun and Hung, Linus and Hussaina, Hebah and Maher, Khaola Safia and Chan, Melvin and Garn, Heinrich and Kloesch, Gerhard and Kohn, Bernhard and Kuzeljevic, Boris and McWilliams, Scout and Spruyt, Karen and Tse, Emmanuel and Machiel Van der Loos, Hendrik F. and Kuo, Calvin and Ipsiroglu, Osman S.",
volume="131",
number="",
pages="144-151",
abstract="BACKGROUND: Behavioral observations support clinical in-depth phenotyping but phenotyping and pattern recognition are affected by training background. As Attention Deficit Hyperactivity Disorder, Restless Legs syndrome/Willis Ekbom disease and medication induced activation syndromes (including increased irritability and/or akathisia), present with hyperactive-behaviors with hyper-arousability and/or hypermotor-restlessness (H-behaviors), we first developed a non-interpretative, neutral pictogram-guided phenotyping language (PG-PL) for describing body-segment movements during sitting (Data in Brief).  METHODOLOGY & RESULTS: The PG-PL was applied for annotating 12 1-min sitting-videos (inter-observer agreements >85%->97%) and these manual annotations were used as a ground truth to develop an automated algorithm using OpenPose, which locates skeletal landmarks in 2D video. We evaluated the algorithm's performance against the ground truth by computing the area under the receiver operator curve (>0.79 for the legs, arms, and feet, but 0.65 for the head). While our pixel displacement algorithm performed well for the legs, arms, and feet, it predicted head motion less well, indicating the need for further investigations.  CONCLUSION: This first automated analysis algorithm allows to start the discussion about distinct phenotypical characteristics of H-behaviors during structured behavioral observations and may support differential diagnostic considerations via in-depth phenotyping of sitting behaviors and, in consequence, of better treatment concepts.<p /> <p>Language: en</p>",
language="en",
issn="0022-3956",
doi="10.1016/j.jpsychires.2020.08.033",
url="http://dx.doi.org/10.1016/j.jpsychires.2020.08.033"
}