Junshen Xu1, Esra Abaci Turk2, Borjan Gagoski2, Polina Golland3, P. Ellen Grant2,4, and Elfar Adalsteinsson5
1Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, 2Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States, 3Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States, 4Harvard Medical School, Boston, MA, United States, 5Massachusetts Institute of Technology, Cambridge, MA, United States
1Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, 2Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States, 3Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States, 4Harvard Medical School, Boston, MA, United States, 5Massachusetts Institute of Technology, Cambridge, MA, United States
We proposed a framework for fetal motion analysis using a deep fetal pose estimator and used it to study the effects of gestational age and maternal position on fetal motion.
Figure 3. The examples of extracted fetal
pose of two different subjects in the dataset and the plots of the knee and
elbow angles and the corresponding angular velocities.
Figure 4. Scatter plots of the three motion
metrics vs. gestational age.