Breathing rate is a vital health metric that is an invaluable indicator of
the overall health of a person. In recent years, the non-contact measurement of
health signals such as breathing rate has been a huge area of development, with
a wide range of applications from telemedicine to driver monitoring systems.
This paper presents an investigation into a method of non-contact breathing
rate detection using a motion detection algorithm, optical flow. Optical flow
is used to successfully measure breathing rate by tracking the motion of
specific points on the body. In this study, the success of optical flow when
using different sets of points is evaluated. Testing shows that both chest and
facial movement can be used to determine breathing rate but to different
degrees of success. The chest generates very accurate signals, with an RMSE of
0.63 on the tested videos. Facial points can also generate reliable signals
when there is minimal head movement but are much more vulnerable to noise
caused by head/body movements. These findings highlight the potential of
optical flow as a non-invasive method for breathing rate detection and
emphasize the importance of selecting appropriate points to optimize accuracy.

研究了一种利用运动检测算法光流进行非接触式呼吸速率检测的方法，并评估了在使用不同点集时光流的成功性。研究表明，胸部和面部运动可用于确定呼吸速率，但成功程度有所不同。这些发现凸显了光流作为一种无创呼吸速率检测方法的潜力，并强调了选择适当的点以优化准确性的重要性。

利用光流进行非接触呼吸频率检测

Non-Contact Breathing Rate Detection Using Optical Flow

Conventional sleep monitoring is time-consuming, expensive and uncomfortable,
requiring a large number of contact sensors to be attached to the patient.
Video data is commonly recorded as part of a sleep laboratory assessment. If
accurate sleep staging could be achieved solely from video, this would overcome
many of the problems of traditional methods. In this work we use heart rate,
breathing rate and activity measures, all derived from a near-infrared video
camera, to perform sleep stage classification. We use a deep transfer learning
approach to overcome data scarcity, by using an existing contact-sensor dataset
to learn effective representations from the heart and breathing rate time
series. Using a dataset of 50 healthy volunteers, we achieve an accuracy of
73.4\% and a Cohen's kappa of 0.61 in four-class sleep stage classification,
establishing a new state-of-the-art for video-based sleep staging.

利用视频数据和深层迁移学习的方法，通过心率、呼吸速率和活动度等参数对睡眠进行分类，其在 50 名正常志愿者数据集上实现了 73.4% 的准确率和 Cohen's kappa 系数 0.61，成为基于视频的睡眠分级的最新研究水平。

利用近红外视频摄像头测量的生命体征和活动进行的深度学习睡眠分期

Deep Learning-Enabled Sleep Staging From Vital Signs and Activity  Measured Using a Near-Infrared Video Camera

Non-contact video-based physiological measurement has many applications in
health care and human-computer interaction. Practical applications require
measurements to be accurate even in the presence of large head rotations. We
propose the first end-to-end system for video-based measurement of heart and
breathing rate using a deep convolutional network. The system features a new
motion representation based on a skin reflection model and a new attention
mechanism using appearance information to guide motion estimation, both of
which enable robust measurement under heterogeneous lighting and major motions.
Our approach significantly outperforms all current state-of-the-art methods on
both RGB and infrared video datasets. Furthermore, it allows spatial-temporal
distributions of physiological signals to be visualized via the attention
mechanism.

本文提出了一种使用深度卷积神经网络进行基于视频的非接触式心率和呼吸率测量的新方法，该系统采用一种基于皮肤反射模型的新动作表示和一种使用外观信息引导运动估计的新注意机制，从而实现在异质光照和主要运动下强大的测量，其空间 - 时域分布可通过注意机制进行可视化，能够显著优于所有当前最先进的 RGB 和红外视频数据集上的方法。