首页> 美国卫生研究院文献>Data in Brief >Fidgety Philip and the Suggested Clinical Immobilization Test: Annotation data for developing a machine learning algorithm
【2h】

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

机译:幸福的菲利普和建议的临床固定试验:用于开发机器学习算法的注释数据

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

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.
机译:西方ADHD诊断的旗帜菲利普的动画片诱惑菲利普描绘了一个“不安的”孩子,在坐着时表现出具有超棘宽性和/或高温躁动(H行为)的过度活跃行为。为了克服差异诊断考虑和现代计算方法之间的差距,我们开发了一种非解释性中立象形图引导的表型语言(PG-PL),用于描述坐骑(精神病学研究)期间的体段运动。开发PG-PL,七项研究助手注释了三个原创宠物菲利普漫画。他们的注释与描述性统计分析。要查看PG-PL的表现,同样的七个研究助理注释了12个快速注释的快照,然后使用PG-PL,每次都在随机序列和两个单独的场合。在实现令人满意的观察者间协议之后,PG-PL注释软件用于审查视频,同一七项研究助理注释了12个一分钟的长视频剪辑。最终用于开发用于自动运动检测的机器学习算法(精神病学杂志)的视频剪辑注释。这些数据一起展示了PG-PL的值,以便手动注释人类运动模式。研究人员能够重用数据和第一版本的机器学习算法,以进一步开发和改进用于区分运动模式的算法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号