Chan Melvin, Tse Emmanuel K, Bao Seraph, Berger Mai, Beyzaei Nadia, Campbell Mackenzie, Garn Heinrich, Hussaina Hebah, Kloesch Gerhard, Kohn Bernhard, Kuzeljevic Boris, Lee Yi Jui, Maher Khaola Safia, Carson Natasha, Jeyaratnam Jecika, McWilliams Scout, Spruyt Karen, Van der Loos Hendrik F Machiel, Kuo Calvin, Ipsiroglu Osman
H-Behaviours Research Lab, BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada.
Austrian Institute of Technology, Austria.
Data Brief. 2021 Jan 17;35:106770. doi: 10.1016/j.dib.2021.106770. eCollection 2021 Apr.
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 (). 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 (). 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.
西方注意力缺陷多动障碍（ADHD）诊断的标志——卡通片《坐立不安的菲利普》，描绘了一个“坐立不安”的孩子，在坐着时表现出多动行为，具有过度觉醒和/或过度运动性不安（H行为）。为了弥合鉴别诊断考量与现代计算方法之间的差距，我们开发了一种非解释性、中性的象形图引导表型语言（PG-PL），用于描述坐着时身体各部位的运动（）。为了开发PG-PL，七名研究助理对三幅原始的《坐立不安的菲利普》卡通画进行了标注。他们的标注通过描述性统计进行分析。为了评估PG-PL的性能，同样的七名研究助理先用徒手标注对12张快照进行标注，然后使用PG-PL进行标注，每次标注顺序随机，且分两次进行。在观察者间达成令人满意的一致性后，PG-PL标注软件被用于审查视频，这七名研究助理对12个一分钟长的视频片段进行了标注。视频片段标注最终被用于开发一种用于自动运动检测的机器学习算法（）。这些数据共同证明了PG-PL在手动标注人类运动模式方面的价值。研究人员能够重复使用这些数据以及机器学习算法的第一版，以进一步开发和完善用于区分运动模式的算法。
