| dc.description.abstract | Preterm infants, commonly admitted to hospitals, require intensive and time consuming monitoring. Automated monitoring techniques using video data exist but
are not being applied in practice for monitoring infants. By allowing automated
monitoring through behavioral cue classification, which infants use to communicate their needs, the burden of monitoring can be relieved allowing for improved
health outcomes in preterm infants. This study aims to apply machine learning
techniques for automated behavioral cue classification in preterm infants to infer
their care needs, specifically of hunger and feeding discomfort. A MoViNet model
was trained for this classification problem, selected for their extensive pretraining
and multi-class video classification capabilities. Due to the limited availability of
labeled data, the techniques of few-shot learning and active learning have been applied to investigate if they improve upon baseline performance. Few-shot learning
consists of an initial training phase on similar tasks to allow for quick adjustment
of weights. Active learning incorporates additional data labeling, with instances
gathered using stratified sampling included in the dataset. It was found that the
fully supervised baseline approach was able to successfully uncover patterns in infant behavior. However, few-shot learning resulted in worse performance due to
challenges in generalizing from the source to the target domain. Active learning
performed comparably to the baseline approach and offered additional value as a
labeling tool in the data-scarce setting. The research also revealed the impact of
individual differences in behavior, affecting the generalizability of behaviors to other
infants and hindering performance. Despite these challenges, individual behavioral
differences did not entirely prevent successful classification. By incorporating more
training data from new infants, the generalizability of the results and performance
could be improved. In sum, this research forms a solid foundation for advancing
fully automatic infant monitoring, potentially enabling more individualized care
with beneficial health outcomes. | |
