CN113592788A - CPR compression depth measuring method and system based on machine vision - Google Patents

CPR compression depth measuring method and system based on machine vision Download PDF

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CN113592788A
CN113592788A CN202110795507.0A CN202110795507A CN113592788A CN 113592788 A CN113592788 A CN 113592788A CN 202110795507 A CN202110795507 A CN 202110795507A CN 113592788 A CN113592788 A CN 113592788A
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cpr
feedback pad
compression
video data
depth
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王鸿欣
王湛翔
杨扬
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Henan Jinxin Shulian Electronic Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/22—Measuring arrangements characterised by the use of optical techniques for measuring depth
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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    • G06T7/0002—Inspection of images, e.g. flaw detection
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/50—Depth or shape recovery
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    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • 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
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Abstract

The invention discloses a CPR compression depth measuring method and a CPR compression depth measuring system based on machine vision, wherein the method comprises the following steps: scanning a two-dimensional code on a CPR feedback pad to obtain position information of the CPR feedback pad, wherein the CPR feedback pad is a passive component; acquiring an inclination angle beta with the feedback pad according to the position information, acquiring a static image of a scale mark on the CPR feedback pad, and calculating to obtain a pixel coefficient k according to the static image of the scale mark; and acquiring dynamic pressing video data, processing the dynamic pressing video data, and calculating according to the dynamic pressing video data, the inclination angle beta and the pixel coefficient k to obtain the pressing depth d. The invention realizes the function of measuring the compression depth without a cable, and the CPR feedback pad is a passive component for assisting the compression, so that the CPR operation can not be interrupted due to the damage of elements caused by the compression, thereby improving the convenience and the practicability.

Description

CPR compression depth measuring method and system based on machine vision
Technical Field
The invention relates to the technical field of depth measurement, in particular to a CPR compression depth measurement method and a CPR compression depth measurement system based on machine vision.
Background
Cardiopulmonary resuscitation (CPR for short) is a life-saving technology for sudden cardiac arrest and respiration, and the existing CPR instruments are mainly divided into two types, one is equipment without a compression depth feedback function, and the other is equipment with a compression depth feedback function. The equipment without the pressing depth feedback function only prompts a rescuer to press the chest according to a certain frequency, the rescuer cannot know the pressing force and the chest pressing depth of each time, the too small force cannot play a role in cardio-pulmonary resuscitation, the too large force easily causes fracture of a patient, the degree of pressing pressure is difficult to grasp at a crisis moment, the rescuer must be trained professionally to effectively rescue by experience, and otherwise, the blind pressing effect is not ideal.
The device with the function of compression depth feedback is additionally provided with a depth feedback sensor which can monitor the compression depth of each time, but a CPR feedback pad and a CPR host machine are connected by a cable, a compression depth signal detected by the feedback sensor is transmitted to the host machine by a CPR cable, the host machine judges whether the compression is in place or excessive by calculation, and guides a rescuer to adjust the compression force and frequency at any time in real time to achieve better treatment effect, the device has the function of depth feedback, but is very inconvenient to use in the actual rescue site, the sudden cardiac arrest site of the victim has the best treatment time, the cardio-pulmonary resuscitation treatment is carried out within 3 minutes after the onset of disease, under the situation of nervous confusion, one cable connecting the sensor and the host machine is likely to wind the arm of the rescuer to influence normal compression, or carelessly touches the cable to drag the host machine to cause the device to be damaged, and one cable is likely to cause the treatment to be invalid, delaying the best rescue opportunity, the inside accurate circuit that has electronic component such as acceleration sensor that is equipped with of CPR feedback pad of cable moreover, probably damage internal circuit and cause the rescue interruption under the exogenic action during pressing, must the level place when using, hardly guarantees the level when rescuing patient, and inside acceleration sensor output signal is inaccurate, can mislead the rescue.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a CPR compression depth measuring method and a CPR compression depth measuring system based on machine vision.
In a first aspect, a machine vision-based CPR compression depth measurement method comprises the steps of:
scanning a two-dimensional code on a CPR feedback pad to obtain position information of the CPR feedback pad, wherein the CPR feedback pad is a passive component;
acquiring an inclination angle beta with the feedback pad according to the position information, acquiring a static image of a scale mark on the CPR feedback pad, and calculating to obtain a pixel coefficient k according to the static image of the scale mark;
and acquiring dynamic pressing video data, processing the dynamic pressing video data, and calculating according to the dynamic pressing video data, the inclination angle beta and the pixel coefficient k to obtain the pressing depth d.
Further, the pixel coefficient k is a proportionality coefficient of an actual distance between adjacent scale marks in the static image and a pixel number between adjacent scale marks.
Further, the dynamic compression video data includes up and down reciprocating motion images of the rescuer pressing the palm, and the processing of the dynamic compression video data specifically includes:
and performing mode identification and AI artificial intelligence operation on the up-and-down reciprocating motion image of the pressed palm to obtain a depth pixel value m.
Further, the calculating the compression depth d specifically includes:
using a formula
Figure BDA0003162676250000021
And calculating the compression depth d, wherein m is a depth pixel value, k is a pixel coefficient, and beta is an inclination angle.
In a second aspect, a machine vision-based CPR compression depth measurement system comprises a CPR host computer and a CPR feedback pad, the CPR feedback pad being a passive component, the CPR host computer comprising:
an identification acquisition module: the CPR feedback pad is used for scanning a two-dimensional code on the CPR feedback pad to acquire position information of the CPR feedback pad;
a static acquisition module: the device is used for acquiring an inclination angle beta with the feedback pad according to the position information, acquiring a static image of a scale mark on the CPR feedback pad, and calculating to obtain a pixel coefficient k according to the static image of the scale mark;
a dynamic processing module: the device is used for acquiring dynamic pressing video data, processing the dynamic pressing video data, and calculating the pressing depth d according to the dynamic pressing video data, the inclination angle beta and the pixel coefficient k.
Further, the pixel coefficient k is a proportionality coefficient of an actual distance between adjacent scale marks in the static image and a pixel number between adjacent scale marks.
Further, the dynamic compression video data includes up and down reciprocating motion images of the rescuer pressing the palm, and the processing of the dynamic compression video data specifically includes:
and performing mode identification and AI artificial intelligence operation on the up-and-down reciprocating motion image of the pressed palm to obtain a depth pixel value m.
Further, the calculating the compression depth d specifically includes:
using a formula
Figure BDA0003162676250000031
And calculating the compression depth d, wherein m is a depth pixel value, k is a pixel coefficient, and beta is an inclination angle.
The invention has the beneficial effects that: through obtaining scale image and gathering the developments on the CPR feedback pad and pressing the video data, calculation processing obtains the depth of compression data to realize the function that no cable also can measure the depth of compression, and CPR feedback pad is supplementary passive component of pressing, and inside does not have circuits such as electronic components, thereby can not cause the component to damage because of pressing and interrupt CPR operation, improved convenience and practicality.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
Fig. 1 is a flow chart of a machine vision based CPR compression depth measurement method;
FIG. 2 is a graph of the relationship between the tilt angle and compression depth for a machine vision based CPR compression depth measurement method;
figure 3 is a block diagram of a machine vision based CPR compression depth measurement system.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
Example one
A machine vision based CPR compression depth measurement method, as shown in fig. 1, comprising the steps of:
s1: scanning a two-dimensional code on a CPR feedback pad to obtain position information of the CPR feedback pad, wherein the CPR feedback pad is a passive component;
specifically, the CPR feedback pad is a passive component for assisting in pressing, circuits such as electronic components are not arranged inside the CPR feedback pad, scale marks and two-dimensional codes are arranged on the CPR feedback pad, positioning is carried out by scanning the two-dimensional codes on the CPR feedback pad, position information of the CPR feedback pad is obtained, when the positioning two-dimensional codes are not scanned, whether scanning is overtime or not is judged, if scanning is overtime, a voice alarm is sent out, and the position of a CPR host machine is prompted to be changed until scanning is successful.
S2, acquiring an inclination angle beta with the feedback pad according to the position information, acquiring a static image of a scale mark on the CPR feedback pad, and calculating to obtain a pixel coefficient k according to the static image of the scale mark;
specifically, after the two-dimensional code is successfully positioned, the inclination angle beta between the two-dimensional code and the CPR feedback pad is obtained, a static image of scale marks on the CPR feedback pad is collected and subjected to autocorrelation operation processing, the number of pixels n between adjacent scale marks is obtained, and the actual distance delta between the adjacent scale marks is known, so that the proportionality coefficient of the actual size and the number of pixels can be calculated
Figure BDA0003162676250000051
S3: and acquiring dynamic pressing video data, processing the acquired video data, and calculating according to the video data, the inclination angle beta and the pixel coefficient k to obtain the pressing depth d.
Specifically, in the rescue process of a rescuer, the CPR host continuously collects up-and-down reciprocating motion images of a pressed palm, finds out the motion images of the palm through mode identification and AI artificial intelligence operation, performs single-valued processing on the two-dimensional images, performs various operations such as cross correlation in the vertical direction on a plurality of groups of pictures containing the motion trail of the palm, finds out the pixel value m of the motion depth of the palm in the vertical direction, and obtains the compression depth by calibrating with a column coefficient. There are two functions v (t) and h (t) of the picture data over time, the cross-correlation function z (t) of the two functions reflecting the degree to which the functions v (t) and h (t) match each other at different relative positions, z (t) being expressed as:
Figure BDA0003162676250000052
discretization into a discretization function of N points is as follows:
Figure BDA0003162676250000061
when the maximum extremum of the function z (kt) is obtained, the extremum corresponds to a time T, and a pixel value m, where m is T/T, and the pixel m is a pixel projection of the actual compression depth d on the picture, as shown in fig. 2, the inclination angle β, the pixel displacement m, and the compression depth d form a trigonometric function relationship, and the actual compression depth data d is obtained according to the function relationship and the pixel proportionality coefficient k:
Figure BDA0003162676250000062
where d is the actual CPR compression depth feedback data, m is the calculated pixel depth, Δ is the actual distance between adjacent graduations of the ruler, n is the number of pixels between adjacent graduations of the ruler, and β is the angle of inclination to the CPR feedback pad.
After the pressing depth d is obtained through calculation, the pressing frequency f is obtained according to the pressing period, whether pressing is qualified or not is checked through judgment of the depth and the frequency, and a voice prompt is sent out, wherein the judgment process specifically comprises the following steps: judging whether the pressing depth d is less than 5cm, if so, the voice prompt force is too small, if not, further judging whether the pressing depth is more than 6cm, if so, judging whether the pressing depth is more than 6cm, the voice prompt pressing is too large, otherwise, further judging whether the pressing frequency f is less than 100 times/minute, if so, judging whether the pressing frequency f is less than 100 times/minute, the voice prompt pressing is too slow, otherwise, further judging whether the pressing frequency f is more than 120 times/minute, if so, the voice prompt pressing is too fast, otherwise, the voice prompt operation is normal, and adjusting the pressing pressure or frequency in time according to the voice prompt.
Example two
A CPR compression depth measuring system based on machine vision comprises a CPR host and a CPR feedback pad, wherein the CPR feedback pad is a passive component for assisting compression, circuits such as electronic components and the like are not arranged in the CPR feedback pad, scale marks and two-dimensional codes are arranged on the CPR feedback pad, as shown in figure 3, the CPR host comprises an identification acquisition module, a static acquisition module and a dynamic processing module, wherein the identification acquisition module is used for scanning the two-dimensional codes on the CPR feedback pad to acquire position information of the CPR feedback pad, the static acquisition module is used for acquiring an inclination angle beta with the feedback pad according to the position information, acquiring static images of the scale marks on the CPR feedback pad, a pixel coefficient k is obtained by calculation according to the static images of the scale marks, the dynamic processing module is used for acquiring dynamic compression video data, processing the dynamic compression video data, and processing the dynamic compression video data according to the dynamic compression video data, The compression depth d is calculated by the inclination angle beta and the pixel coefficient k.
Specifically, the CPR feedback pad is a passive component for assisting in pressing, circuits such as electronic components and the like are not arranged in the CPR feedback pad, scale marks and two-dimensional codes are arranged on the CPR feedback pad, the two-dimensional codes on the CPR feedback pad are scanned and positioned by the identification acquisition module, the position information of the CPR feedback pad is acquired, when the positioned two-dimensional codes are not scanned, whether scanning is overtime is judged, if scanning is overtime, a voice alarm is given out, the position of a CPR host computer is prompted to be changed, until scanning is successful, after scanning is successful, the static acquisition module acquires the inclination angle beta between the CPR feedback pad and the CPR feedback pad, static images of the scale marks on the CPR feedback pad are acquired, self-correlation operation processing is carried out, the number n of pixels between adjacent scale marks is obtained, and the actual distance delta between the adjacent scale marks is known, so that the proportionality coefficient of the actual size and the number of pixels can be calculated
Figure BDA0003162676250000071
In the process of rescuing a rescuer, the dynamic processing module continuously collects up-and-down reciprocating motion images for pressing a palm, finds out the motion images of the palm through mode identification and AI artificial intelligence operation, performs single-valued processing on the two-dimensional images, performs various operations such as cross correlation in the vertical direction on a plurality of groups of pictures containing the motion trail of the palm, finds out the pixel value m of the motion depth of the palm in the vertical direction, and calibrates the pixel value m with a column coefficient to obtain the compression depth. Is provided with twoRegarding the time-varying picture data functions v (t) and h (t), the cross-correlation function z (t) of the two functions reflects the degree to which the functions v (t) and h (t) match each other at different relative positions, and z (t) can be expressed as:
Figure BDA0003162676250000072
discretization into a discretization function of N points is as follows:
Figure BDA0003162676250000073
when the maximum extremum of the function z (kt) is obtained, the extremum corresponds to a time T, and a pixel value m, where m is T/T, and the pixel m is a pixel projection of the actual compression depth d on the picture, as shown in fig. 2, the inclination angle β, the pixel displacement m, and the compression depth d form a trigonometric function relationship, and the actual compression depth data d is obtained according to the function relationship and the pixel proportionality coefficient k:
Figure BDA0003162676250000081
where d is the actual CPR compression depth feedback data, m is the calculated pixel depth, Δ is the actual distance between adjacent graduations of the ruler, n is the number of pixels between adjacent graduations of the ruler, and β is the angle of inclination to the CPR feedback pad.
After the pressing depth d is obtained through calculation, the pressing frequency f is obtained according to the pressing period, whether pressing is qualified or not is checked through judgment of the depth and the frequency, and a voice prompt is sent out, wherein the judgment process specifically comprises the following steps: judging whether the pressing depth d is less than 5cm, if so, the voice prompt force is too small, if not, further judging whether the pressing depth is more than 6cm, if so, judging whether the pressing depth is more than 6cm, the voice prompt pressing is too large, otherwise, further judging whether the pressing frequency f is less than 100 times/minute, if so, judging whether the pressing frequency f is less than 100 times/minute, the voice prompt pressing is too slow, otherwise, further judging whether the pressing frequency f is more than 120 times/minute, if so, the voice prompt pressing is too fast, otherwise, the voice prompt operation is normal, and adjusting the pressing pressure or frequency in time according to the voice prompt.
The invention obtains the compression depth data by acquiring the scale image on the CPR feedback pad and acquiring dynamic compression video data, and then calculating and processing the compression depth data, thereby realizing the function of measuring the compression depth without a cable, and the CPR feedback pad is a passive component for assisting in compression, and has no circuits such as electronic components and the like in the CPR feedback pad, thereby avoiding the interruption of CPR operation caused by the damage of elements due to compression, and improving the convenience and the practicability.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.

Claims (8)

1. A machine vision based CPR compression depth measurement method comprising the steps of:
scanning a two-dimensional code on a CPR feedback pad to obtain position information of the CPR feedback pad, wherein the CPR feedback pad is a passive component;
acquiring an inclination angle beta with the feedback pad according to the position information, acquiring a static image of a scale mark on the CPR feedback pad, and calculating to obtain a pixel coefficient k according to the static image of the scale mark;
and acquiring dynamic pressing video data, processing the dynamic pressing video data, and calculating according to the dynamic pressing video data, the inclination angle beta and the pixel coefficient k to obtain the pressing depth d.
2. A machine vision based CPR compression depth measurement method according to claim 1, wherein the pixel coefficient k is a proportionality coefficient of the actual distance between adjacent scale marks in the still image and the number of pixels between adjacent scale marks.
3. The machine vision-based CPR compression depth measurement method according to claim 1, wherein the dynamic compression video data comprises up and down reciprocating motion images of a rescuer compressing a palm, and the processing of the dynamic compression video data comprises:
and performing mode identification and AI artificial intelligence operation on the up-and-down reciprocating motion image of the pressed palm to obtain a depth pixel value m.
4. A machine vision based CPR compression depth measurement method according to claim 3, wherein the calculating of the compression depth d comprises in particular:
using a formula
Figure FDA0003162676240000011
And calculating the compression depth d, wherein m is a depth pixel value, k is a pixel coefficient, and beta is an inclination angle.
5. A machine vision based CPR compression depth measurement system comprising a CPR host machine and a CPR feedback pad, wherein the CPR feedback pad is a passive component, the CPR host machine comprising:
an identification acquisition module: the CPR feedback pad is used for scanning a two-dimensional code on the CPR feedback pad to acquire position information of the CPR feedback pad;
a static acquisition module: the device is used for acquiring an inclination angle beta with the feedback pad according to the position information, acquiring a static image of a scale mark on the CPR feedback pad, and calculating to obtain a pixel coefficient k according to the static image of the scale mark;
a dynamic processing module: the device is used for acquiring dynamic pressing video data, processing the dynamic pressing video data, and calculating the pressing depth d according to the dynamic pressing video data, the inclination angle beta and the pixel coefficient k.
6. A machine vision based CPR compression depth measurement system of claim 5, wherein the pixel coefficient k is a proportionality coefficient of the actual distance between adjacent scale markings to the number of pixels between adjacent scale markings in the still image.
7. A machine vision based CPR compression depth measurement system according to claim 5, wherein the dynamic compression video data comprises up and down reciprocating motion images of the rescuer pressing the palm, and the dynamic compression video data is processed by:
and performing mode identification and AI artificial intelligence operation on the up-and-down reciprocating motion image of the pressed palm to obtain a depth pixel value m.
8. A machine vision based CPR compression depth measurement system according to claim 7, wherein the calculating the compression depth d specifically comprises:
using a formula
Figure FDA0003162676240000021
And calculating the compression depth d, wherein m is a depth pixel value, k is a pixel coefficient, and beta is an inclination angle.
CN202110795507.0A 2021-07-14 2021-07-14 CPR compression depth measuring method and system based on machine vision Withdrawn CN113592788A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023123214A1 (en) * 2021-12-30 2023-07-06 焦旭 Electronic device, hand compression depth measurement method, system, and wearable device

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023123214A1 (en) * 2021-12-30 2023-07-06 焦旭 Electronic device, hand compression depth measurement method, system, and wearable device

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Application publication date: 20211102