Overcoming Small Data Limitations in Video-Based Infant Respiration Estimation
This enables early detection of breathing irregularities in infants, which could help prevent conditions like SIDS, though it is incremental as it builds on existing adult respiration estimation methods.
The authors tackled the lack of contactless respiration monitoring for infants by creating a new annotated video dataset (AIR-400) and developing the first reproducible pipelines for infant respiration estimation, establishing the first reproducible benchmarks for this task.
The development of contactless respiration monitoring for infants could enable advances in the early detection and treatment of breathing irregularities, which are associated with neurodevelopmental impairments and conditions like sudden infant death syndrome (SIDS). But while respiration estimation for adults is supported by a robust ecosystem of computer vision algorithms and video datasets, only one small public video dataset with annotated respiration data for infant subjects exists, and there are no reproducible algorithms which are effective for infants. We introduce the annotated infant respiration dataset of 400 videos (AIR-400), contributing 275 new, carefully annotated videos from 10 recruited subjects to the public corpus. We develop the first reproducible pipelines for infant respiration estimation, based on infant-specific region-of-interest detection and spatiotemporal neural processing enhanced by optical flow inputs. We establish, through comprehensive experiments, the first reproducible benchmarks for the state-of-the-art in vision-based infant respiration estimation. We make our dataset, code repository, and trained models available for public use.