  The assessment of general movements (GMs) in infants is a useful tool in the
early diagnosis of neurodevelopmental disorders. However, its evaluation in
clinical practice relies on visual inspection by experts, and an automated
solution is eagerly awaited. Recently, video-based GMs classification has
attracted attention, but this approach would be strongly affected by irrelevant
information, such as background clutter in the video. Furthermore, for
reliability, it is necessary to properly extract the spatiotemporal features of
infants during GMs. In this study, we propose an automated GMs classification
method, which consists of preprocessing networks that remove unnecessary
background information from GMs videos and adjust the infant's body position,
and a subsequent motion classification network based on a two-stream structure.
The proposed method can efficiently extract the essential spatiotemporal
features for GMs classification while preventing overfitting to irrelevant
information for different recording environments. We validated the proposed
method using videos obtained from 100 infants. The experimental results
demonstrate that the proposed method outperforms several baseline models and
the existing methods.
