Overcoming Small Data Limitations in Video-Based Infant Respiration Estimation

TL;DR

This paper introduces a new annotated dataset and reproducible methods for contactless infant respiration estimation using video, addressing the lack of infant-specific data and algorithms in this domain.

Contribution

It provides the first public infant respiration dataset (AIR-400), along with reproducible pipelines and benchmarks for vision-based infant respiration estimation.

Findings

AIR-400 dataset with 275 annotated videos

Reproducible pipelines based on infant-specific ROI detection

Benchmark results establishing state-of-the-art performance

Abstract

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.