Non-nutritive sucking (NNS), which refers to the act of sucking on a pacifier, finger, or similar object without nutrient intake, plays a crucial role in assessing healthy early development. In the case of preterm infants, NNS behavior is a key component in determining their readiness for feeding. In older infants, the characteristics of NNS behavior offer valuable insights into neural and motor development. Additionally, NNS activity has been proposed as a potential safeguard against sudden infant death syndrome (SIDS). However, the clinical application of NNS assessment is currently hindered by labor-intensive and subjective finger-in-mouth evaluations. Consequently, researchers often resort to expensive pressure transducers for objective NNS signal measurement. To enhance the accessibility and reliability of NNS signal monitoring for both clinicians and researchers, we introduce a vision-based algorithm designed for non-contact detection of NNS activity using baby monitor footage in natural settings. Our approach involves a comprehensive exploration of optical flow and temporal convolutional networks, enabling the detection and amplification of subtle infant-sucking signals. We successfully classify short video clips of uniform length into NNS and non-NNS periods. Furthermore, we investigate manual and learning-based techniques to piece together local classification results, facilitating the segmentation of longer mixed-activity videos into NNS and non-NNS segments of varying duration. Our research introduces two novel datasets of annotated infant videos, including one sourced from our clinical study featuring 19 infant subjects and 183 hours of overnight baby monitor footage.

翻译：非营养性吸吮（NNS），即吸吮安抚奶嘴、手指或类似物体但不摄入营养的行为，在评估健康早期发育中起着关键作用。对于早产儿而言，NNS行为是决定其喂养准备状态的关键组成部分。在较大婴儿中，NNS行为的特征为神经和运动发育提供了宝贵见解。此外，NNS活动已被提议作为预防婴儿猝死综合征（SIDS）的潜在保护措施。然而，NNS评估的临床应用目前受到费时且主观的手指入口评价方法的限制。因此，研究人员常使用昂贵的压力传感器进行客观NNS信号测量。为提高临床医生和研究人员获取NNS信号监测的可及性和可靠性，我们提出了一种基于视觉的算法，用于在自然环境中利用婴儿监控视频进行非接触式NNS活动检测。我们的方法涉及对光流和时序卷积网络的全面探索，以检测和放大微弱的婴儿吸吮信号。我们成功将等长短视频片段分类为NNS和非NNS时段。此外，我们研究了手动和基于学习的技术来拼接局部分类结果，从而将更长的混合活动视频分割为不同时长的NNS和非NNS片段。本研究引入了两个标注婴儿视频的新数据集，其中一个来自我们的临床研究，包含19名婴儿受试者和183小时的夜间婴儿监控录像。