An infant’s risk of developing neuromotor impairment is primarily assessed through visual examination by specialized clinicians. Therefore, many infants at risk for impairment go undetected, particularly in under-resourced environments. There is thus a need to develop automated, clinical assessments based on quantitative measures from widely-available sources, such as videos recorded on a mobile device. Here, we automatically extract body poses and movement kinematics from the videos of at-risk infants (N = 19). For each infant, we calculate how much they deviate from a group of healthy infants (N = 85 online videos) using a Naïve Gaussian Bayesian Surprise metric. After pre-registering our Bayesian Surprise calculations, we find that infants who are at high risk for impairments deviate considerably from the healthy group. Our simple method, provided as an open-source toolkit, thus shows promise as the basis for an automated and low-cost assessment of risk based on video recordings.

中文翻译：

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计算机视觉自动评估婴儿神经运动风险


婴儿发生神经运动障碍的风险主要由专业临床医生通过目视检查来评估。因此，许多面临损伤风险的婴儿未被发现，特别是在资源贫乏的环境中。因此，需要根据广泛可用的来源（例如移动设备上录制的视频）的定量测​​量来开发自动化的临床评估。在这里，我们自动从高危婴儿的视频中提取身体姿势和运动运动学（N = 19）。对于每个婴儿，我们使用朴素高斯贝叶斯惊喜指标计算他们与一组健康婴儿（N = 85 个在线视频）的偏差程度。在预先注册我们的贝叶斯惊喜计算后，我们发现损伤风险高的婴儿与健康组的差异很大。因此，我们作为开源工具包提供的简单方法有望成为基于视频记录的自动化、低成本风险评估的基础。