General movements (GMs) are spontaneous movements of infants up to five months post-term involving the whole body varying in sequence, speed, and amplitude. The assessment of GMs has shown its importance for identifying infants at risk for neuromotor deficits, especially for the detection of cerebral palsy. As the assessment is based on videos of the infant that are rated by trained professionals, the method is time-consuming and expensive. Therefore, approaches based on Artificial Intelligence have gained significantly increased attention in the last years. In this article, we systematically analyze and discuss the main design features of all existing technological approaches seeking to transfer the Prechtl’s assessment of general movements from an individual visual perception to computer-based analysis. After identifying their shared shortcomings, we explain the methodological reasons for their limited practical performance and classification rates. As a conclusion of our literature study, we conceptually propose a methodological solution to the defined problem based on the groundbreaking innovation in the area of Deep Learning.

中文翻译：

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人工智能接近普雷希特尔对一般运动的评估：系统文献综述。

一般运动 (GM) 是婴儿在产后五个月内的自发运动，涉及整个身体，顺序、速度和幅度各不相同。GM 的评估已显示出其对于识别有神经运动缺陷风险的婴儿的重要性，特别是对于检测脑瘫。由于评估是基于由经过培训的专业人员对婴儿进行评分的视频，因此该方法既耗时又昂贵。因此，基于人工智能的方法在过去几年中受到了越来越多的关注。在本文中，我们系统地分析和讨论了所有现有技术方法的主要设计特征，旨在将普雷希特尔对一般运动的评估从个人视觉感知转移到基于计算机的分析。在确定了它们的共同缺点之后，我们解释了它们有限的实际性能和分类率的方法原因。作为我们文献研究的结论，我们基于深度学习领域的突破性创新，概念性地提出了一种针对所定义问题的方法解决方案。