  To make the earlier medical intervention of infants' cerebral palsy (CP),
early diagnosis of brain damage is critical. Although general movements
assessment(GMA) has shown promising results in early CP detection, it is
laborious. Most existing works take videos as input to make fidgety
movements(FMs) classification for the GMA automation. Those methods require a
complete observation of videos and can not localize video frames containing
normal FMs. Therefore we propose a novel approach named WO-GMA to perform FMs
localization in the weakly supervised online setting. Infant body keypoints are
first extracted as the inputs to WO-GMA. Then WO-GMA performs local
spatio-temporal extraction followed by two network branches to generate pseudo
clip labels and model online actions. With the clip-level pseudo labels, the
action modeling branch learns to detect FMs in an online fashion. Experimental
results on a dataset with 757 videos of different infants show that WO-GMA can
get state-of-the-art video-level classification and cliplevel detection
results. Moreover, only the first 20% duration of the video is needed to get
classification results as good as fully observed, implying a significantly
shortened FMs diagnosis time. Code is available at:
https://github.com/scofiedluo/WO-GMA.
