Abnormal Infant Movements Classification With Deep Learning on Pose-Based Features

@article{McCay2020AbnormalIM,
  title={Abnormal Infant Movements Classification With Deep Learning on Pose-Based Features},
  author={Kevin D. McCay and Edmond S. L. Ho and Hubert P. H. Shum and Gerhard Fehringer and Claire Marcroft and Nicholas D. Embleton},
  journal={IEEE Access},
  year={2020},
  volume={8},
  pages={51582-51592}
}
The pursuit of early diagnosis of cerebral palsy has been an active research area with some very promising results using tools such as the General Movements Assessment (GMA). In our previous work, we explored the feasibility of extracting pose-based features from video sequences to automatically classify infant body movement into two categories, normal and abnormal. The classification was based upon the GMA, which was carried out on the video data by an independent expert reviewer. In this… 
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