CN118248316A - Cardiopulmonary resuscitation guidance method and system based on remote communication - Google Patents
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Abstract
The invention discloses a cardiopulmonary resuscitation guidance method and system based on remote communication, and relates to the technical field of first aid, wherein the method comprises the following steps: acquiring image data of a patient when cardiopulmonary resuscitation is required; performing face recognition on the image data to obtain basic medical data; performing human morphological recognition on the image data to obtain human morphological characteristics, and determining a cardiopulmonary resuscitation strategy based on the basic medical data and the human morphological characteristics; and the remote terminal is used for guiding and executing the cardiopulmonary resuscitation flow according to the cardiopulmonary resuscitation strategy. By integrating the face recognition, human body morphology recognition and remote communication technology, the accuracy and the effectiveness of cardiopulmonary resuscitation are obviously improved.
Description
Technical Field
The invention relates to the technical field of emergency treatment, in particular to a cardiopulmonary resuscitation guidance method and system based on remote communication.
Background
Cardiopulmonary resuscitation (CPR) is a critical first aid technique with an irreplaceable role in rescuing the life of a sudden cardiac arrest patient. Since the 60 s of the 20 th century, modern cardiopulmonary resuscitation techniques have been gradually created and popularized, and after decades of development, have been widely used in hospitals, emergency centers, and emergency training of the general public worldwide. However, despite the increasing popularity of cardiopulmonary resuscitation techniques, there are still challenges and limitations faced in practical applications. During cardiopulmonary resuscitation (CPR), AED defibrillation, CPR compressions and artificial respiration are interrelated, complementary, rescue measures that together form the complete process of cardiopulmonary resuscitation, which typically should be performed immediately after a sudden cardiac arrest in the patient is detected, whereas CPR compressions and artificial respiration should be performed after AED defibrillation to maintain the patient's vital signs.
However, in the traditional cardiopulmonary resuscitation process, CPR compression and artificial respiration are carried out by adopting fixed parameters, so that physiological characteristics and disease differences of different patients are ignored, and accuracy and effectiveness of cardiopulmonary resuscitation are affected.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a cardiopulmonary resuscitation guidance method and a cardiopulmonary resuscitation guidance system based on remote communication.
In one aspect, the present invention provides a method for cardiopulmonary resuscitation based on telecommunications, the method comprising:
acquiring image data of a patient;
Performing face recognition on the image data to obtain basic medical data; performing human morphological recognition on the image data to obtain human morphological characteristics, and determining a cardiopulmonary resuscitation strategy based on the basic medical data and the human morphological characteristics;
and executing a cardiopulmonary resuscitation flow according to the cardiopulmonary resuscitation strategy.
Preferably, the method includes, before acquiring the image information of the patient: and receiving an emergency request, carrying out dialogue according to the emergency request, and determining whether to carry out cardiopulmonary resuscitation according to dialogue content.
Preferably, the face recognition is performed on the image data to obtain basic medical data, including:
acquiring a face image from the image data by using a face detection algorithm;
Determining face key points from the face image, and correcting the face key points by using an affine transformation algorithm to obtain target face key points;
Acquiring face features corresponding to the target face key points, and inputting the face features into a face recognition model to obtain a face recognition result;
and determining the identity of the patient according to the face recognition result, and acquiring basic medical data according to the identity.
Preferably, the underlying medical data includes age, height, weight and disease condition.
Preferably, the performing human body morphological recognition on the image data to obtain human body morphological features includes:
Extracting a chest image from the image data by using a key point positioning algorithm;
and extracting a chest widest point from the chest image, and determining the chest width according to the chest widest point.
Preferably, determining a cardiopulmonary resuscitation strategy based on the basic medical data and the morphological feature of the human body comprises:
Determining standard parameters according to the age, wherein the standard parameters comprise standard compression depth and standard respiration depth;
Determining a respiratory coefficient from the disease condition;
determining a chest coefficient based on the height, weight, and chest width;
Correcting the standard parameters according to the respiratory coefficient and the thoracic cavity coefficient to obtain target parameters;
And generating a cardiopulmonary resuscitation strategy according to the target parameters.
Preferably, determining the chest coefficient based on the height, weight and chest width comprises:
Determining chest size according to the chest width, height and weight;
And calculating a difference value between the chest cavity size and the standard chest cavity size, and determining a chest cavity coefficient according to the difference value.
Preferably, determining the respiratory coefficient from the disease condition comprises:
determining whether the patient has respiratory diseases according to the disease conditions;
when a patient has respiratory diseases, judging whether the respiratory diseases can be cured and whether the respiratory diseases are cured;
When the respiratory diseases are curable and not cured, determining the influence degree of the respiratory diseases according to the generation time and the time attenuation of the respiratory diseases;
Determining the extent of the effect of the respiratory disease according to the severity of the respiratory disease when the respiratory disease is incurable;
And determining the respiratory coefficient according to the influence degree.
Preferably, cardiopulmonary resuscitation is performed according to the cardiopulmonary resuscitation strategy, and then further comprising:
collecting real-time cardiopulmonary resuscitation parameters including real-time CPR compression depth and frequency and real-time artificial respiration rescue depth and frequency;
And when the real-time cardiopulmonary resuscitation parameters do not meet the requirements of cardiopulmonary resuscitation strategies, generating early warning prompt information.
In another aspect, an embodiment of the present invention provides a cardiopulmonary resuscitation guidance system based on remote communication, the system comprising:
acquiring image data of a patient;
Performing face recognition on the image data to obtain basic medical data; performing human morphological recognition on the image data to obtain human morphological characteristics, and determining a cardiopulmonary resuscitation strategy based on the basic medical data and the human morphological characteristics;
And the remote terminal is used for guiding and executing the cardiopulmonary resuscitation flow according to the cardiopulmonary resuscitation strategy.
According to the cardiopulmonary resuscitation guidance method and system based on remote communication, provided by the invention, the accuracy and the effectiveness of cardiopulmonary resuscitation are obviously improved through integrating the face recognition, the human body morphology recognition and the remote communication technology. In particular, the invention has the following beneficial effects:
1) Personalized strategies: according to the age, height, weight, disease condition and morphological characteristics of human bodies of different patients, a personalized cardiopulmonary resuscitation strategy is determined, so that the emergency treatment process is more accurate and effective.
2) Real-time monitoring and early warning: by collecting the cardiopulmonary resuscitation parameters in real time and comparing the cardiopulmonary resuscitation parameters with the strategy requirements, the improper position in the operation can be timely found, the early warning prompt information is generated, and the safety and the effectiveness of the cardiopulmonary resuscitation process are ensured.
3) Remote guidance: by utilizing the telecommunication technology, an expert can participate in and guide the cardiopulmonary resuscitation process in real time, so that the problem that a professional cannot arrive in time in the traditional emergency is solved, and the coverage range of emergency services is enlarged.
4) High-efficiency synergy: the system realizes the integrated processes of emergency request receiving, image data obtaining, strategy formulation, instruction execution and monitoring and early warning, and improves the overall efficiency of emergency work.
In conclusion, the invention has remarkable advantages and wide application prospect in improving accuracy and effectiveness of cardiopulmonary resuscitation and guaranteeing life safety of patients.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. Like elements or portions are generally identified by like reference numerals throughout the several figures. In the drawings, elements or portions thereof are not necessarily drawn to scale.
FIG. 1 is a flow chart of a method for cardiopulmonary resuscitation based on telecommunications provided by the present invention;
fig. 2 is a system block diagram of a cardiopulmonary resuscitation guidance system based on remote communication according to the present invention.
Detailed Description
Embodiments of the technical scheme of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and thus are merely examples, and are not intended to limit the scope of the present invention.
It is noted that unless otherwise indicated, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs.
Example 1
As shown in fig. 1, the present invention provides a cardiopulmonary resuscitation method based on remote communication, the method comprising: step 1, receiving an emergency request, carrying out dialogue according to the emergency request, and determining whether to carry out cardiopulmonary resuscitation according to dialogue content. Step 2, when cardiopulmonary resuscitation is needed, acquiring image data of a patient; performing face recognition on the image data to obtain basic medical data; and carrying out human morphological recognition on the image data, acquiring human morphological characteristics, and determining a cardiopulmonary resuscitation strategy based on the basic medical data and the human morphological characteristics. And 3, executing a cardiopulmonary resuscitation flow at the remote terminal according to the cardiopulmonary resuscitation strategy guidance.
Specifically, the emergency request may be received by making an emergency call (e.g. 120), or may be a small-program one-key help call, which is not limited in this embodiment. When someone needs an emergency, they will make this call, providing the operator with basic information and symptoms about the patient. After receiving the call, the operator will quickly talk to the caller to obtain more information about the patient. This dialogue should be as detailed as possible, including asking the patient for symptoms, consciousness, normal breathing, pulse, etc. At the same time, the operator will ask the patient if there is any known history or allergic reaction and where they are currently located. Based on the information obtained in the session, the operator can initially determine whether the patient requires cardiopulmonary resuscitation. Cardiopulmonary resuscitation may be required if the patient is unconscious, non-breathing, or has only abnormal breathing.
In the embodiment of the invention, the face recognition is performed on the image data to obtain basic medical data, which comprises the following steps: acquiring a face image from the image data by using a face detection algorithm; determining face key points from the face image, and correcting the face key points by using an affine transformation algorithm to obtain target face key points; acquiring face features corresponding to the target face key points, and inputting the face features into a face recognition model to obtain a face recognition result; and determining the identity of the patient according to the face recognition result, and acquiring basic medical data according to the identity.
Specifically, first, image data is processed using a face detection algorithm (e.g., haar cascade classifier, deep learning model such as MTCNN, etc.) to identify and locate the position of a face in an image. Where the output of the face detection algorithm is typically one or more rectangular boxes that mark the location of the face in the image. Once a face is detected, face keypoints (e.g., eye corners, nose tips, mouth corners, etc.) may be determined using conventional methods such as active shape Models (ACTIVE SHAPE Models, ASM) or active appearance Models (ACTIVE APPEARANCE Models, AAM), or using methods based on deep learning such as deep alignment networks (DEEP ALIGNMENT networks, DAN), etc. Since the face may have problems of angular deflection, expression change or occlusion, the key points of the initial positioning need to be corrected, and the affine transformation algorithm (Affine Transformation) is used to adjust the key points of the face so as to enable the key points to be aligned to the standard face model more accurately, wherein affine transformation can comprise operations such as rotation, scaling, translation and the like. And extracting corresponding face features according to the corrected target face key points. These features may include texture features, shape features, or depth features, among others. Feature extraction may be accomplished by manually designed feature descriptors (e.g., LBP, HOG, etc.) or deep learning models (e.g., convolutional neural network CNN). And inputting the extracted face features into a pre-trained face recognition model. The model can be a traditional model based on a Support Vector Machine (SVM) and Principal Component Analysis (PCA), and can also be a model based on deep learning, such as FaceNet, sphereFace. The face recognition model can match the input features and output the face most similar to the face in the known face library as a recognition result. After determining the identity of the patient based on the face recognition result, a database or Electronic Health Record (EHR) associated therewith may be accessed. Basic medical data of the patient is obtained by querying a database or EHR, and the data may include key information such as age, height, weight, disease history, drug allergy history and the like.
In an embodiment of the invention, the underlying medical data includes age, height, weight, and disease condition. The cardiopulmonary resuscitation strategy can be optimized targeted taking into account the underlying medical data of the patient.
In the embodiment of the invention, the human body morphological recognition is performed on the image data to obtain human body morphological characteristics, which comprises the following steps: extracting a chest image from the image data by using a key point positioning algorithm; and extracting a chest widest point from the chest image, and determining the chest width according to the chest widest point.
Specifically, first, image data is processed using a key point localization algorithm in order to identify and extract an image of a thoracic region. The key point localization algorithm may employ feature-based methods (e.g., HOG, SIFT, etc.) or deep learning models (e.g., object detection algorithms such as convolutional neural network CNN, YOLO, SSD). By training a model to identify the chest region in the image, the model can output coordinates or bounding boxes of the chest region. And cutting or extracting the chest image from the original image data according to the chest region coordinates or the boundary frame output by the key point positioning algorithm. The extracted chest image should contain sufficient information to perform subsequent characterization, such as chest width measurements. In the extracted chest image, it is necessary to further identify the widest point of the chest. This may be achieved by image processing techniques such as edge detection, contour extraction, etc. An edge detection algorithm (e.g., canny edge detector) is used to detect edges in the chest image and generate an edge image. Contour extraction is performed on the edge image to find a curve or set of points representing the contour of the chest. And analyzing the contour curve to determine the position of the widest point of the chest. This typically involves finding the location on the contour where the distance between two points is greatest. Once the chest widest point is identified, the chest width can be measured. If the widest point is represented by two specific points, the Euclidean distance between the two points is directly calculated as the chest width. If the widest point is represented by a region or curve segment, it may be desirable to calculate the average or maximum width of the region or curve segment as the chest width.
In an embodiment of the invention, determining a cardiopulmonary resuscitation strategy based on the underlying medical data and the morphological feature of the human body comprises: determining standard parameters according to the age, wherein the standard parameters comprise standard compression depth and standard respiration depth; determining a respiratory coefficient from the disease condition; determining chest size according to the chest width, height and weight; calculating a difference value between the chest cavity size and a standard chest cavity size, and determining a chest cavity coefficient according to the difference value; correcting the standard parameters according to the respiratory coefficient and the thoracic cavity coefficient to obtain target parameters; and generating a cardiopulmonary resuscitation strategy according to the target parameters. Wherein determining chest coefficients from the height, weight, and chest width comprises: in an embodiment of the invention, determining the respiratory coefficient from the disease condition comprises: determining whether the patient has respiratory diseases according to the disease conditions; when a patient has respiratory diseases, judging whether the respiratory diseases can be cured and whether the respiratory diseases are cured; when the respiratory diseases are curable and not cured, determining the influence degree of the respiratory diseases according to the generation time and the time attenuation of the respiratory diseases; determining the extent of the effect of the respiratory disease according to the severity of the respiratory disease when the respiratory disease is incurable; and determining the respiratory coefficient according to the influence degree.
Specifically, standard parameters are determined: according to the age of the patient, the standard compression depth and the standard respiration depth corresponding to the age are acquired from preset reference data. These standard parameters were derived based on extensive medical research and practical experience, aimed at providing appropriate compression and respiratory support for patients of different ages.
Specifically, the respiratory coefficient is determined: firstly, judging whether the patient has respiratory diseases according to the disease condition of the patient. This may be done by querying the patient's Electronic Health Record (EHR) or querying the patient's family members. If the patient has respiratory diseases, further judging the cure of the diseases and whether the diseases are cured currently. This requires analysis of the nature of the disease and the history of treatment. For curable and incurable respiratory diseases, the time of disease production is considered, and the extent of its effect on current respiratory function is evaluated in combination with a time decay function. The time decay function may be an empirical formula for estimating the effect of the disease on the ability of the patient to breathe over time. For incurable respiratory diseases, the extent of its effect on respiratory function is determined directly from the severity of the disease. This may require reference to a medical expert's assessment of disease severity. Finally, a breathing factor is determined as a function of the degree of influence, which factor is to be used for subsequent adjustment of the standard parameters.
Specifically, the chest coefficient is determined: the chest size is calculated using a preset algorithm or formula in combination with the height, weight and chest width of the patient. This calculation process may involve analysis of body proportions and morphology. The calculated chest size is compared to a standard chest size to obtain a difference. The standard chest size may be an average or reference value based on a large population of data statistics. A chest coefficient is determined based on the difference, the coefficient reflecting the degree of difference between the patient's chest size and a standard value.
Specifically, correction standard parameters: standard compression depth and depth of breath are corrected using the respiratory and thoracic coefficients. The correction process may be implemented by means of mathematical formulas or algorithms that take into account the specific influence of different coefficients on the standard parameters. The modified parameters are referred to as target parameters, which are tailored to the individual characteristics of the patient.
In an embodiment of the present invention, cardiopulmonary resuscitation is performed according to the cardiopulmonary resuscitation policy, and then further including: collecting real-time cardiopulmonary resuscitation parameters including real-time CPR compression depth and frequency and real-time artificial respiration rescue depth and frequency; and when the real-time cardiopulmonary resuscitation parameters do not meet the requirements of cardiopulmonary resuscitation strategies, generating early warning prompt information.
In summary, the present invention first receives an emergency request and initiates an image acquisition module to acquire image data of a patient. The face recognition module is used for processing the images and extracting basic medical data such as age, sex, height and weight of a patient. Meanwhile, the human body morphology recognition module further analyzes the image to determine morphology features such as chest width and the like of the patient. These data are transmitted to a policy determination module which, in combination with standard parameters and algorithms in the medical knowledge base, calculates a personalized cardiopulmonary resuscitation policy. The instruction execution module generates detailed operation instruction information according to the strategy and sends the detailed operation instruction information to mobile equipment of emergency personnel or family members of patients through the remote communication module. In the execution process, the real-time monitoring and early warning module continuously collects real-time parameters and compares the real-time parameters with the policy requirements, and once the condition that the real-time parameters do not meet the requirements is found, early warning prompt information is immediately generated and sent to related personnel so as to adjust operation in time. The invention realizes the cardiopulmonary resuscitation guidance method and system based on remote communication through integrating the face recognition, human body morphology recognition and remote communication technology, remarkably improves the accuracy and the effectiveness of cardiopulmonary resuscitation, has the beneficial effects of individuation strategy, real-time monitoring and early warning, remote guidance, high-efficiency coordination and the like, and provides powerful support for improving the emergency treatment efficiency and guaranteeing the life safety of patients.
Example 2
As shown in fig. 2, an embodiment of the present invention provides a cardiopulmonary resuscitation guidance system based on remote communication, the system including: an acquisition module for acquiring image data of a patient; the determining module is used for carrying out face recognition on the image data to obtain basic medical data; performing human morphological recognition on the image data to obtain human morphological characteristics, and determining a cardiopulmonary resuscitation strategy based on the basic medical data and the human morphological characteristics; and the guidance module is used for guiding the execution of the cardiopulmonary resuscitation flow according to the cardiopulmonary resuscitation strategy.
It should be understood that, for the same inventive concept, the cardiopulmonary resuscitation guidance system based on remote communication provided by the embodiments of the present invention and the cardiopulmonary resuscitation guidance method based on remote communication provided by the embodiments of the present invention, reference may be made to the above embodiments for more specific working principles of each module in the embodiments of the present invention, and details are not repeated in the embodiments of the present invention.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention, and are intended to be included within the scope of the appended claims and description.
Claims (10)
1. A method of cardiopulmonary resuscitation guidance based on telecommunications, comprising:
Acquiring image data of a patient when cardiopulmonary resuscitation is required;
Performing face recognition on the image data to obtain basic medical data; performing human morphological recognition on the image data to obtain human morphological characteristics, and determining a cardiopulmonary resuscitation strategy based on the basic medical data and the human morphological characteristics;
And the remote terminal is used for guiding and executing the cardiopulmonary resuscitation flow according to the cardiopulmonary resuscitation strategy.
2. The method of claim 1, wherein the step of obtaining image information of the patient is preceded by: and receiving an emergency request, carrying out dialogue according to the emergency request, and determining whether to carry out cardiopulmonary resuscitation according to dialogue content.
3. The method of claim 1, wherein performing face recognition on the image data to obtain basic medical data, comprises:
acquiring a face image from the image data by using a face detection algorithm;
Determining face key points from the face image, and correcting the face key points by using an affine transformation algorithm to obtain target face key points;
Acquiring face features corresponding to the target face key points, and inputting the face features into a face recognition model to obtain a face recognition result;
and determining the identity of the patient according to the face recognition result, and acquiring basic medical data according to the identity.
4. A method of remotely communicating based cardiopulmonary resuscitation guidance according to claim 3, wherein said underlying medical data includes age, height, weight, and disease condition.
5. The method of claim 3, wherein performing human morphology recognition on the image data to obtain human morphology features comprises:
Extracting a chest image from the image data by using a key point positioning algorithm;
and extracting a chest widest point from the chest image, and determining the chest width according to the chest widest point.
6. The method of claim 5, wherein determining a cardiopulmonary resuscitation strategy based on the underlying medical data and the morphological feature of the person comprises:
Determining standard parameters according to the age, wherein the standard parameters comprise standard compression depth and standard respiration depth;
Determining a respiratory coefficient from the disease condition;
determining a chest coefficient based on the height, weight, and chest width;
Correcting the standard parameters according to the respiratory coefficient and the thoracic cavity coefficient to obtain target parameters;
And generating a cardiopulmonary resuscitation strategy according to the target parameters.
7. The method of claim 6, wherein determining chest coefficients based on the height, weight and chest width comprises:
Determining chest size according to the chest width, height and weight;
And calculating a difference value between the chest cavity size and the standard chest cavity size, and determining a chest cavity coefficient according to the difference value.
8. The method of claim 6, wherein determining respiratory coefficients based on the disease condition comprises:
determining whether the patient has respiratory diseases according to the disease conditions;
when a patient has respiratory diseases, judging whether the respiratory diseases can be cured and whether the respiratory diseases are cured;
When the respiratory diseases are curable and not cured, determining the influence degree of the respiratory diseases according to the generation time and the time attenuation of the respiratory diseases;
Determining the extent of the effect of the respiratory disease according to the severity of the respiratory disease when the respiratory disease is incurable;
And determining the respiratory coefficient according to the influence degree.
9. The method of claim 1, wherein performing cardiopulmonary resuscitation according to the cardiopulmonary resuscitation policy guidelines further comprises:
collecting real-time cardiopulmonary resuscitation parameters including real-time CPR compression depth and frequency and real-time artificial respiration rescue depth and frequency;
And when the real-time cardiopulmonary resuscitation parameters do not meet the requirements of cardiopulmonary resuscitation strategies, generating early warning prompt information.
10. A cardiopulmonary resuscitation guidance system based on telecommunications, comprising:
An acquisition module for acquiring image data of a patient;
The determining module is used for carrying out face recognition on the image data to obtain basic medical data; performing human morphological recognition on the image data to obtain human morphological characteristics, and determining a cardiopulmonary resuscitation strategy based on the basic medical data and the human morphological characteristics;
And the guidance module is used for guiding the execution of the cardiopulmonary resuscitation flow according to the cardiopulmonary resuscitation strategy.
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