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.

该研究介绍了一种基于视觉的算法，利用婴儿监视器的录像在自然环境中无接触地检测非营养吸吮活动，通过光流和时域卷积网络的综合研究，成功分类和分割婴儿视频，提供了两个标注的婴儿视频数据集。

微妙信号：基于视频的婴儿非营养性吮吸的神经发育暗示

Subtle Signals: Video-based Detection of Infant Non-nutritive Sucking as  a Neurodevelopmental Cue

Drone racing is becoming a popular e-sport all over the world, and beating
the best human drone race pilots has quickly become a new major challenge for
artificial intelligence and robotics. In this paper, we propose a strategy for
autonomous drone racing which is computationally more efficient than navigation
methods like visual inertial odometry and simultaneous localization and
mapping. This fast light-weight vision-based navigation algorithm estimates the
position of the drone by fusing race gate detections with model dynamics
predictions. Theoretical analysis and simulation results show the clear
advantage compared to Kalman filtering when dealing with the relatively low
frequency visual updates and occasional large outliers that occur in fast drone
racing. Flight tests are performed on a tiny racing quadrotor named "Trashcan",
which was equipped with a Jevois smart-camera for a total of 72g. The test
track consists of 3 laps around a 4-gate racing track. The gates spaced 4
meters apart and can be displaced from their supposed position. An average
speed of 2m/s is achieved while the maximum speed is 2.6m/s. To the best of our
knowledge, this flying platform is the smallest autonomous racing drone in the
world and is 6 times lighter than the existing lightest autonomous racing drone
setup (420g), while still being one of the fastest autonomous racing drones in
the world.

本文提出了一种视觉惯性算法，该算法比如视觉惯性测距和同时定位和映射等导航方法更加高效，可以用于计算机无人机竞速，该算法通过将比赛门检测与模型动态预测融合来估计无人机的位置。实际测试中，飞行测试在一架 72g 的迷你无人机上完成，得到了最高速度为 2.6m/s，从而使其成为世界上最小的自主竞速无人机之一。