论文标题
自动评估婴儿的面部和上身对称性作为torticollis的早期迹象
Automatic Assessment of Infant Face and Upper-Body Symmetry as Early Signs of Torticollis
论文作者
论文摘要
我们将计算机视觉姿势姿势估计技术明确开发了为数据筛选婴儿领域开发的，这是corticollis的研究，这是婴儿早期鉴定和治疗至关重要的婴儿的常见状况。具体而言，我们结合了为婴儿设计的面部地标和人体关节估计技术，以估计与面部和上身对称性有关的一系列几何措施，这些措施来自torticolis中物理疗法和眼科研究文献的一系列来源。我们通过一系列指标来衡量性能，并表明大多数这些几何措施的估计是成功的，从而使Spearman与地面真实价值的相关性非常强。此外，我们表明，这些估计值源自专为婴儿领域设计的姿势估计神经网络，其表现优于源自为成人领域设计的更广为人知的网络得出的估计值
We apply computer vision pose estimation techniques developed expressly for the data-scarce infant domain to the study of torticollis, a common condition in infants for which early identification and treatment is critical. Specifically, we use a combination of facial landmark and body joint estimation techniques designed for infants to estimate a range of geometric measures pertaining to face and upper body symmetry, drawn from an array of sources in the physical therapy and ophthalmology research literature in torticollis. We gauge performance with a range of metrics and show that the estimates of most these geometric measures are successful, yielding strong to very strong Spearman's $ρ$ correlation with ground truth values. Furthermore, we show that these estimates, derived from pose estimation neural networks designed for the infant domain, cleanly outperform estimates derived from more widely known networks designed for the adult domain
