WO2022227044A1 - 基于摄像头的自适应多尺度呼吸监测方法 - Google Patents

基于摄像头的自适应多尺度呼吸监测方法 Download PDF

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WO2022227044A1
WO2022227044A1 PCT/CN2021/091628 CN2021091628W WO2022227044A1 WO 2022227044 A1 WO2022227044 A1 WO 2022227044A1 CN 2021091628 W CN2021091628 W CN 2021091628W WO 2022227044 A1 WO2022227044 A1 WO 2022227044A1
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scale
respiration
local
monitoring
camera
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徐永
黄玉来
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深圳市爱贝宝移动互联科技有限公司
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Priority to US17/637,903 priority Critical patent/US20230017172A1/en
Priority to PCT/CN2021/091628 priority patent/WO2022227044A1/zh
Publication of WO2022227044A1 publication Critical patent/WO2022227044A1/zh

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/113—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb occurring during breathing
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A61B5/0816—Measuring devices for examining respiratory frequency
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1126—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb using a particular sensing technique
    • A61B5/1128—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb using a particular sensing technique using image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G06T7/0014—Biomedical image inspection using an image reference approach
    • G06T7/0016—Biomedical image inspection using an image reference approach involving temporal comparison
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2503/00—Evaluating a particular growth phase or type of persons or animals
    • A61B2503/04—Babies, e.g. for SIDS detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20016—Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20021—Dividing image into blocks, subimages or windows

Definitions

  • the invention relates to the technical field of video image signal recognition and processing.
  • Respiratory rate is a sensitive indicator of acute respiratory dysfunction, and also an important indicator to measure whether a person's heart function is good or not and whether gas exchange is normal. Normal adults breathe about 12-20 times per minute, and children breathe faster than adults, up to 20 to 30 times per minute; the respiratory rate of newborns can reach 44 times per minute; the ratio of respiration to pulse is 1:4, that is For every breath, the pulse beats 4 times.
  • the two basic monitoring methods of respiratory rate are: direct monitoring method and indirect monitoring method.
  • Direct monitoring methods include impedance method, temperature sensor method, pressure sensor method, carbon dioxide method, breath sound method and ultrasound method; indirect monitoring method includes monitoring respiratory rate through ECG, blood pressure, electromyography, and photoplethysmography.
  • the non-contact method of monitoring breathing based on cameras has emerged in recent years.
  • the breathing signal can be monitored without touching the body of the subject, which reduces the discomfort and inconvenience caused by wearable devices, improves the user experience, and simplifies the monitoring process.
  • Camera-based breathing monitoring mainly adopts three principles: (1) blood volume change; (2) nasal temperature change; (3) chest/abdominal breathing movement.
  • the method (3) is more commonly used because of its strong reproducibility; however, the current breathing monitoring based on chest/abdominal breathing motion adopts a preset fixed scale according to the image resolution, and performs breathing signal extraction at a single image scale, while A single image scale cannot achieve the optimal breathing signal extraction effect.
  • the area with more obvious local texture needs a smaller image scale to extract the breathing signal to achieve a better sensitivity, and the preset fixed scale is not necessarily the most suitable scale;
  • the local texture Inconspicuous regions require larger image scales to extract signals to include more texture information for more accurate breathing motion extraction.
  • the local texture of the breathing monitoring object is bound to be different, such as: clothing texture and wrinkles, uneven lighting, etc. Therefore, it is impossible to obtain the locally optimal breathing signal and the global optimal breathing signal from a single image scale.
  • the purpose of the present invention is to solve the technical deficiencies that the local optimal breathing signal and the global optimal breathing signal cannot be obtained by using a single image scale, and propose a camera-based adaptive multi-scale breathing monitoring method. .
  • the camera is used to collect respiratory monitoring objects in real time
  • the technical scheme further limited as the present invention includes:
  • step (3) when there are more than two target regions at the same scale, the local breathing signals extracted from the two target regions with the highest pixel position coincidence at two different scales are compared; under the optimal segmentation scale The multiple partial respiration signals extracted from the multiple target regions are then integrated or compared to obtain an optimal partial respiration as the monitoring respiration signal output.
  • the pixels of the local area are segmented irregularly at a single scale through the guidance of the local features of the image content, and a number of unit areas with similar pixel characteristics are segmented, and Each unit area is separately identified and extracted for local breathing signals; the unit area with local breathing signal output is defined as the target area; multiple local breathing signals extracted from multiple target areas are then integrated or compared to obtain an optimal local breathing as monitoring. Respiratory signal output.
  • the present invention performs multi-scale rule pre-segmentation on the video image collected by the camera, adaptively determines the optimal segmentation scale according to the quality of the breathing signal, and divides the local breathing signal extracted from the target area under the optimal segmentation scale
  • the monitoring breathing signal output it can accurately obtain the optimal breathing area and the global optimal breathing signal from the monitoring video of the breathing monitoring object, improve the reliability of the non-contact monitoring breathing signal of the camera, and realize intelligent monitoring.
  • Fig. 1 is the working flow chart when the present invention adopts video image to carry out multi-scale rule pre-segmentation
  • FIG. 2 is a schematic diagram of the present invention using a single-scale irregular segmentation.
  • a camera-based adaptive multi-scale breathing monitoring method disclosed in the present invention includes the following steps:
  • the camera is used to collect the breathing monitoring object in real time.
  • Respiratory monitoring objects are mainly children and newborns, because children and newborns are relatively immature and are not suitable for contact monitoring of breathing; in addition, children and newborns are also high-risk monitoring objects, and they are high-risk groups of acute respiratory dysfunction.
  • people generally use cameras to collect video and audio from the crib, and perform motion detection on the collected video to prevent infants and young children from falling during sleep without being guarded due to movements such as turning over, crawling, etc.
  • Accidents of getting out of bed endanger the personal safety of infants and young children, and when the monitored object moves significantly, or when crying is recognized in the collected audio, the camera's processor automatically generates alarm information, which is sent through the guardian's smartphone.
  • the camera used in the present invention can be based on the motion detection and crying voice recognition functions of the above-mentioned traditional camera, and can also recognize the subtle periodicity of the chest, abdomen, neck or face of the monitoring object during the breathing process.
  • the image segmentation in the present invention preferably adopts multi-scale regular segmentation, that is, Image segmentation is carried out using multiple rules of different scales, similar to grid segmentation, so as to obtain several unit regions.
  • the local respiratory signal identification and extraction are performed on each unit area respectively.
  • the specific identification and extraction process may include image grayscale and histogram equalization, image normalization, video frame matching, image whitening, and removal of singularities.
  • each unit area corresponding to the chest and abdomen will generate obvious periodic continuous motion signals.
  • the periodic continuous motion signal can identify and extract the local breathing signal, and use relatively large-scale regular segmentation to continuously and stably extract the local breathing signal.
  • step (3) Compare the local breathing signals extracted from the target area pre-segmented at each scale, determine the optimal segmentation scale according to the quality of the local breathing signal, and use the local breathing signal extracted from the target area under the optimal segmentation scale as monitoring Respiratory signal output.
  • the local breathing signals extracted from the two target regions with the highest pixel position coincidence at two different scales are compared; Multiple local breathing signals are then compared to obtain an optimal local breathing signal as the monitoring breathing signal output; or, multiple target areas under the optimal segmentation scale are combined into a target area, and a global breathing signal is comprehensively output.
  • the segmentation scale parameter setting is used in subsequent respiratory monitoring.
  • step (2) is repeated to re-determine the optimal segmentation scale parameters, and adaptive multi-scale respiration monitoring is performed.
  • the relatively weak periodic continuous motion signal of the neck or face of the breathing monitoring object may still not be able to be extracted to satisfy the requirements.
  • the local breathing signal of the preset value is used; as shown in FIG. 2 , the pixels of the local area are irregularly segmented at a single scale through the guidance of the local features of the image content, and a number of unit areas with similar pixel characteristics are segmented. Partial respiration signal identification and extraction are carried out in each area; the unit area with local respiration signal output is defined as the target area; multiple partial respiration signals extracted from multiple target areas are synthesized or compared to obtain an optimal partial respiration as the monitoring respiration signal output.
  • Single-scale irregular segmentation is a segmentation method based on mean shift.
  • the color clustering of the feature space is realized through the gradient of the pattern space density function, so as to achieve the purpose of image segmentation.
  • Segmentation is based on image edge detection, which is beneficial to the relative neck or face. Weak periodic continuous motion signal capture.
  • the invention adopts camera non-contact, multi-scale image segmentation and breathing signal extraction, finds the optimal breathing area and global optimal breathing signal under multi-scale, realizes breathing signal monitoring, and efficiently and accurately meets the needs of vital sign monitoring and early warning.

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Abstract

基于摄像头的自适应多尺度呼吸监测方法，涉及到视频图像信号识别处理技术领域。解决现有采用单一图像尺度无法获得局部最优的呼吸信号和全局最优的呼吸信号的技术不足，步骤：（1）、采用摄像头实时采集呼吸监测对象；（2）、对摄像头采集的视频图像进行多尺度规则预分割，在每一尺度下预分割的各单元区域分别进行局部呼吸信号识别提取；将具有局部呼吸信号输出的单元区域定义为目标区域；（3）、对各尺度预分割的目标区域提取的局部呼吸信号进行比对，根据局部呼吸信号质量确定最优分割尺度，并将最优分割尺度下的从目标区域提取的局部呼吸信号作为监控呼吸信号输出。根据呼吸信号质量自适应确定最优分割尺度，从而精确地从呼吸监测对象监控视频中获得多尺度下最优的呼吸区域和全局最优呼吸信号，提升摄像头非接触监测呼吸信号可靠性。

Description

基于摄像头的自适应多尺度呼吸监测方法 技术领域
本发明涉及到视频图像信号识别处理技术领域。
 背景技术
呼吸频率是急性呼吸功能障碍的敏感指标，也是衡量人心脏功能好坏和气体交换是否正常的重要指标。正常成年人每分钟呼吸大约12-20次，小儿呼吸比成人快，每分钟可达20～30次；新生儿的呼吸频率可达每分钟44次；呼吸与脉搏的比是1：4，即每呼吸1次，脉搏搏动4次。目前，呼吸频率的两种基本监测方法为：直接监测法和间接监测法。直接监测法包括阻抗法、温度传感器法、压力传感器法、二氧化碳法、呼吸音法和超声法；间接监测法包括通过心电、血压、肌电、光电容积脉搏波中监测呼吸频率的方法。
基于摄像头非接触监测呼吸的方法在近年来兴起，无需接触受试者的身体即可监测呼吸信号，减少了穿戴设备带来的不适和不便，提高用户体验，简化监测流程。基于摄像头的呼吸监测主要采用三种原理：（1）血液体积量变化；（2）鼻腔温度变化；（3）胸部/腹部呼吸运动。其中，方式（3）因其可复现性强更为常用；但是，目前基于胸部/腹部呼吸运动的呼吸监测根据图像分辨率采用预设的固定尺度，进行单一图像尺度进行呼吸信号提取，而单一图像尺度无法达到最优的呼吸信号提取效果。原因主要为：（1）局部纹理更明显的区域需要较小的图像尺度提取呼吸信号，以达到更好的敏感程度，而预设固定尺度并不一定是最适合的尺度；（2）局部纹理不明显的区域需要较大的图像尺度提取信号，以包括更多的纹理信息使呼吸运动的提取更加精确。而呼吸监测对象的局部纹理必然会有差别，比如：衣服纹理和褶皱，光照不均匀等，因此，无法从单一图像尺度上获得局部最优的呼吸信号和全局最优的呼吸信号。
 技术问题
综上所述，本发明的目的在于解决现有采用单一图像尺度无法获得局部最优的呼吸信号和全局最优的呼吸信号的技术不足，而提出一种基于摄像头的自适应多尺度呼吸监测方法。
 技术解决方案
为解决本发明所提出的技术问题，采用的技术方案为：
基于摄像头的自适应多尺度呼吸监测方法，其特征在于所述的所述方法包括有如下步骤：
（1）、采用摄像头实时采集呼吸监测对象；
（2）、对摄像头采集的视频图像进行多尺度规则预分割，在每一尺度下预分割的各单元区域分别进行局部呼吸信号识别提取；将具有局部呼吸信号输出的单元区域定义为目标区域；
（3）、对各尺度预分割的目标区域提取的局部呼吸信号进行比对，根据局部呼吸信号质量确定最优分割尺度，并将最优分割尺度下的从目标区域提取的局部呼吸信号作为监控呼吸信号输出。
作为本发明作进一步限定的技术方案包括有：
在步骤（3）中，当同一尺度下目标区域为两个以上时，将两个不同尺度下像素位置重合度最高的两个目标区域提取的局部呼吸信号进行比对；在最优分割尺度下的多个目标区域提取的多个局部呼吸信号再进行综合或比对出一个最优局部呼吸作为监控呼吸信号输出。
在步骤（2）无法提取到满足预设值的局部呼吸信号时；通过图像内容的局部特征的引导对局部区域的像素进行单一尺度不规则分割，分割出若干具有近似像素特征的单元区域，并各单元区域分别进行局部呼吸信号识别提取；将具有局部呼吸信号输出的单元区域定义为目标区域；多个目标区域提取的多个局部呼吸信号再进行综合或比对出一个最优局部呼吸作为监控呼吸信号输出。
 有益效果
本发明的有益效果为：本发明对摄像头采集的视频图像进行多尺度规则预分割，根据呼吸信号质量自适应确定最优分割尺度，并将最优分割尺度下的从目标区域提取的局部呼吸信号作为监控呼吸信号输出，从而精确地从呼吸监测对象监控视频中获得多尺度下最优的呼吸区域和全局最优呼吸信号，提升摄像头非接触监测呼吸信号可靠性，实现智能监控。
 附图说明
图1为本发明采用视频图像进行多尺度规则预分割时的工作流程图；
图2为本发明采用单一尺度不规则分割原理图。
 本发明的最佳实施方式
以下结合附图和本发明优选的具体实施例对本发明的方法作进一步地说明。
参照图1中所示，本发明所公开的一种基于摄像头的自适应多尺度呼吸监测方法，包括有如下步骤：
（1）、采用摄像头实时采集呼吸监测对象。呼吸监测对象主要以小儿和新生儿为主，因为小儿和新生儿身体比较稚嫩，不适于采用接触式进行监测呼吸；还有，小儿和新生儿也是高危监护对象，是急性呼吸功能障碍高发人群。目前，人们一般采用摄像头对着婴儿床进行视频和音频采集，对采集的视频进行移动侦测，避免睡眠中的婴幼小儿因翻身、爬行等动作，未得到守护发生婴幼小儿在睡梦中跌落下床的意外事件，危害婴幼儿的人身安全，而在监测对象出现较大幅度移动时，或者，采集的音频中识别出哭声时，摄像头的处理器自动生产报警信息，通过监护人智能手机进行短信提醒；本发明采用的摄像头可以是在具备上述传统摄像头的移动侦测和哭声语音识别功能基础上，还具备识别监测对象在呼吸过程中胸部、腹部、颈部或面部出现的细微周期性连续运动信号的彩色摄像头，或具有红外夜视功能的监控摄像头。
（2）、对摄像头采集的视频图像进行多尺度规则预分割，在每一尺度下预分割的各单元区域分别进行局部呼吸信号识别提取；将具有局部呼吸信号输出的单元区域定义为目标区域。由于监测对象在呼吸过程中胸部、腹部、颈部或面部出现的周期性连续运动幅度相对于采集的视频图像全局而言极为细微，只有目标区域尺度适合的情况下，才能高效，精准地输出呼吸频率。图像分割是现在各种图像识别处理的常见的处理过程，是将图像划分成若干个具有特征一致性且互不重叠的单元区域的过程，本发明图像分割优选采用多尺度规则分割，也即是采用多次不同尺度规则进行图像分割，类似网格化分割，从而得到若干单元区域。每次图像分割后均对各单元区域分别进行局部呼吸信号识别提取，具体识别提取过程可以包括有图像灰度化和直方图均衡、图像归一化、视频帧间匹配、图像白化处理、去除奇异图像来优化数据集等处理过程；一般情况下，若呼吸监测对象穿着纹理明显衣物，并能被摄像头直接采集时，胸部和腹部对应的各单元区域就会产生明显的周期性连续运动信号，根据该周期性连续运动信号，则可识别提取局部呼吸信号，采用相对较大尺度规则分割，可持续稳定提取出局部呼吸信号；而如果呼吸监测对象穿着衣物色彩单一，或者，穿着衣物被色彩单一的被子遮盖，胸部和腹部对应的各单元区域很难提取到识别提取到呼吸信号，只能从呼吸监测对象颈部或面部相对微弱的周期性连续运动信号中可识别提取局部呼吸信号，只能采用相对较小尺度规则分割，虽然，提取到的局部呼吸信号质量不如胸部和腹部衣物纹理明显时大尺度规则分割提取的局部呼吸信号质量，但至少能保证能提取的局部呼吸信号。
（3）、对各尺度预分割的目标区域提取的局部呼吸信号进行比对，根据局部呼吸信号质量确定最优分割尺度，并将最优分割尺度下的从目标区域提取的局部呼吸信号作为监控呼吸信号输出。当同一尺度下目标区域为两个以上时，将两个不同尺度下像素位置重合度最高的两个目标区域提取的局部呼吸信号进行比对；在最优分割尺度下的多个目标区域提取的多个局部呼吸信号再进行比对出一个最优局部呼吸作为监控呼吸信号输出；或者，将最优分割尺度下的多个目标区域拼合作为一个目标区域，综合输出一个全局呼吸信号。确定局部最优分割尺度后，并在之后的呼吸监测中使用该分割尺度参数设置。在呼吸监测对象内容发生变化，原分割尺度下无法识别提取到满足所需的呼吸信号之后，重复步骤（2），重新确定最优分割尺度参数，自适应多尺度呼吸监测。
  由于在步骤（2）多尺度规则预分割时并不会考虑图像内容，即便是在最优尺度下对呼吸监测对象颈部或面部相对微弱的周期性连续运动信号中仍然有可能无法提取到满足预设值的局部呼吸信号时；如图2中所示，通过图像内容的局部特征的引导对局部区域的像素进行单一尺度不规则分割，分割出若干具有近似像素特征的单元区域，并各单元区域分别进行局部呼吸信号识别提取；将具有局部呼吸信号输出的单元区域定义为目标区域；多个目标区域提取的多个局部呼吸信号再进行综合或比对出一个最优局部呼吸作为监控呼吸信号输出。单一尺度不规则分割是基于均值漂移的分割方法，通过模式空间密度函数的梯度来实现特征空间的颜色聚类，从而达到图像分割的目的，根据图像边缘检测进行分割，有利于颈部或面部相对微弱的周期性连续运动信号捕获。
  本发明采用摄像头非接触，多尺度图像分割和呼吸信号提取，寻找多尺度下最优的呼吸区域和全局最优呼吸信号，实现呼吸信号监测，高效、精准满足生命体征监护和预警需求。

Claims (3)

  1. 基于摄像头的自适应多尺度呼吸监测方法，其特征在于所述方法包括有如下步骤：
    （1）、采用摄像头实时采集呼吸监测对象；
    （2）、对摄像头采集的视频图像进行多尺度规则预分割，在每一尺度下预分割的各单元区域分别进行局部呼吸信号识别提取；将具有局部呼吸信号输出的单元区域定义为目标区域；
    （3）、对各尺度预分割的目标区域提取的局部呼吸信号进行比对，根据局部呼吸信号质量确定最优分割尺度，并将最优分割尺度下的从目标区域提取的局部呼吸信号作为监控呼吸信号输出。
  2. 根据权利要求1所述的基于摄像头的自适应多尺度呼吸监测方法，其特征在于：在步骤（3）中，当同一尺度下目标区域为两个以上时，将两个不同尺度下像素位置重合度最高的两个目标区域提取的局部呼吸信号进行比对；在最优分割尺度下的多个目标区域提取的多个局部呼吸信号再进行综合或比对出一个最优局部呼吸作为监控呼吸信号输出。
  3. 根据权利要求1所述的基于摄像头的自适应多尺度呼吸监测方法，其特征在于：在步骤（2）无法提取到满足预设值的局部呼吸信号时；通过图像内容的局部特征的引导对局部区域的像素进行单一尺度不规则分割，分割出若干具有近似像素特征的单元区域，并各单元区域分别进行局部呼吸信号识别提取；将具有局部呼吸信号输出的单元区域定义为目标区域；多个目标区域提取的多个局部呼吸信号再进行综合或比对出一个最优局部呼吸作为监控呼吸信号输出。
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH11276443A (ja) * 1998-03-27 1999-10-12 Toshiba Corp 被介護者観測装置及びその方法
WO2015186374A1 (ja) * 2014-06-03 2015-12-10 株式会社イデアクエスト 呼吸運動測定装置
CN106991268A (zh) * 2016-01-21 2017-07-28 宏达国际电子股份有限公司 监控呼吸运动的方法及其电子装置
CN110047079A (zh) * 2019-04-26 2019-07-23 重庆交通大学 一种基于对象相似性的最优分割尺度选择方法
CN110097078A (zh) * 2019-03-26 2019-08-06 深圳大学 一种最优尺度选择方法、装置及计算机可读存储介质
CN112313705A (zh) * 2018-07-01 2021-02-02 谷歌有限责任公司 视频中细微运动的分析和可视化

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2767233A1 (en) * 2013-02-15 2014-08-20 Koninklijke Philips N.V. Device for obtaining respiratory information of a subject
MX2015014078A (es) * 2013-04-09 2015-12-11 Koninkl Philips Nv Aparato y metodo para determinar señales de respiracion de un sujeto.

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH11276443A (ja) * 1998-03-27 1999-10-12 Toshiba Corp 被介護者観測装置及びその方法
WO2015186374A1 (ja) * 2014-06-03 2015-12-10 株式会社イデアクエスト 呼吸運動測定装置
CN106991268A (zh) * 2016-01-21 2017-07-28 宏达国际电子股份有限公司 监控呼吸运动的方法及其电子装置
CN112313705A (zh) * 2018-07-01 2021-02-02 谷歌有限责任公司 视频中细微运动的分析和可视化
CN110097078A (zh) * 2019-03-26 2019-08-06 深圳大学 一种最优尺度选择方法、装置及计算机可读存储介质
CN110047079A (zh) * 2019-04-26 2019-07-23 重庆交通大学 一种基于对象相似性的最优分割尺度选择方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
LIU, MENGHAN ET AL.: "Asthma Pattern Identification via Continuous Diaphragm Motion Monitoring", IEEE TRANSACTIONS ON MULTI-SCALE COMPUTING SYSTEMS, vol. 1, no. 2, 30 June 2015 (2015-06-30), XP011594454, DOI: 10.1109/TMSCS.2015.2496214 *

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