Automatic pain stimulation testing and evaluating system based on computer vision

Abstract

The invention discloses an automatic pain stimulation testing and evaluating system based on computer vision, and relates to the technical field of automatic testing. According to the automatic pain stimulation testing and evaluating system based on computer vision, the shape change condition of human nerves obtained in the nerve shape change detection module and the stress change condition of the human nerves obtained in the nerve stress change detection module are used as interference variables of neural signal transmission; a pain signal which can be sensed by each nerve of a user in a pain transmission process can be realized, so that anesthesia and analgesia of all nerves of the user can be ensured by ensuring anesthesia and analgesia of the nerve with the minimum pain feeling level in an anesthesia and analgesia process, and meanwhile, through a data reduction model, the pain feeling level of the user can be reduced. And restoring the signal transmission state of the nervous system under the human body posture condition of the body image analysis module obtained in the neural signal transmission model to the signal transmission state of the nervous system of the user in the relaxed state.

Description

Technical Field

[0001] The invention relates to the technical field of automatic testing, and in particular to a pain stimulation automatic testing and evaluation system based on computer vision. Background Art

[0002] Pain assessment is the premise of pain treatment. Accurate and timely assessment of pain can provide necessary guidance and assistance for clinical treatment and is the key to effective pain treatment. Clinicians should master these assessment methods scientifically and undergo rigorous training before they can operate them. The observation results of different observers on the same observation index should be consistent, because the dynamic assessment and comprehensive assessment of pain in clinical practice are often not completed by the same assessor. In this way, the credibility and accuracy of the assessment results can be guaranteed.

[0003] The use of validated tools to measure pain intensity should be prioritized to facilitate titration of analgesic doses and minimize the risk of overuse of analgesics. Strategies such as pain assessment, protocol-based analgesia, and analgesia-first sedation have all been shown to be associated with improved outcomes in ICU patients and have led to the development of practice guidelines over the years. As with other patient populations, pain assessment remains a key component of pain management in ICU patients, even though ICU patients are often unable to communicate, sedated, paralyzed, or delirious. Summary of the invention

[0004] The purpose of the present invention is to provide a computer vision-based pain stimulation automatic test and assessment system to solve the above-mentioned deficiencies in the prior art.

[0005] In order to achieve the above-mentioned purpose, the present invention provides the following technical solutions: a computer vision-based pain stimulation automatic test and assessment system, comprising a stimulation signal acquisition module, a nerve response acquisition module, an image acquisition module, a nerve state analysis module, and a pain level assessment module:

[0006] The stimulation signal acquisition module is used to perform pain stimulation on the user based on the pain stimulation device, and collect stimulation signals of the output stimulation level of the pain stimulation device and send them to the pain level assessment module;

[0007] The neural response acquisition module is used to collect the neural response information of the user when receiving pain stimulation from the pain stimulation device based on the electromyographic information acquisition device, and send the neural response information to the pain level assessment module;

[0008] The image acquisition module is used to acquire user body posture information in real time based on a computer vision acquisition device, and send the acquired user body posture information to the nerve state analysis module and the pain level assessment module respectively;

[0009] The neural state analysis module is used to receive the user's body posture information collected by the image acquisition module, analyze the signal transmission state of the nervous system under the current user's body posture based on the neural state analysis model, and send the signal transmission state of the nervous system under the current user's body posture to the pain level assessment module;

[0010] The pain level assessment module is used to judge the user's own pain perception level under the output stimulation level of the pain stimulation device and adjust the pain level assessment model parameters based on the stimulation signal collected by the stimulation signal acquisition module, the neural response information collected by the neural response acquisition module, the signal transmission state of the nervous system under the current user's body posture analyzed by the neural state analysis module, and the user's body posture information collected by the image acquisition module, and send the pain level assessment model parameters to the user plan storage module.

[0011] Furthermore, it also includes:

[0012] A user login module, which is used by medical staff to log in users according to user identity information and send the user identity information to the user plan storage module and the data calling module. The user identity information includes user disease information, user body shape information and user age information;

[0013] A user scheme storage module, the user scheme storage module is used to store the pain level assessment model parameters corresponding to the user based on the user identity information of the user login module and the pain level assessment model parameters generated by the pain level assessment module;

[0014] A data calling module, the data calling module is used to judge whether there is a pain level assessment model parameter corresponding to the user identity information in the user storage scheme based on the user identity information of the user login module and through the data judgment model, if the judgment result is yes, the data calling module directly calls the pain level assessment model parameter corresponding to the user identity information from the user scheme storage module, if the judgment result is no, the data calling module directly sends the user identity information to the data similarity judgment module;

[0015] A data similarity judgment module, the data similarity judgment module is used to receive the user identity information sent by the data calling module, preset the data similarity judgment threshold, and judge through the data similarity judgment model whether the data similarity between the user identity information stored in the user scheme storage module and the user identity information sent by the data calling module is higher than the preset data similarity judgment threshold; if the judgment result is yes, the pain level assessment model parameters corresponding to the user identity information stored in the user scheme storage module are called through the data calling module.

[0016] Furthermore, the neural state analysis module includes:

[0017] A body image analysis module, the body image analysis module is used to analyze in real time the changes in the user's body posture when receiving pain stimulation from the pain stimulation device, and decompose the changes in body posture into changes in the posture of bones in the body;

[0018] A nerve shape change detection module, the nerve shape change detection module is used to analyze the shape change of human nerves under the condition of the posture change of the bones in the body obtained by the body image analysis module based on the human nerve distribution model, the shape change of human nerves including human nerve stretching, human nerve compression and human nerve bending;

[0019] A nerve force change detection module, which is used to detect the force change status of human nerves under the condition of the posture change of the bones in the body obtained by the body image analysis module based on the force analysis model, and the force change status of human nerves includes human nerve compression, no change in human nerve force, and reduced human nerve force;

[0020] A neural transmission state monitoring module is used to obtain the signal transmission state of the nervous system under the human posture condition of the body image analysis module based on the neural signal transmission model and taking the shape change condition of the human nerve obtained in the neural shape change detection module and the force change condition of the human nerve obtained in the neural force change detection module as interference variables of the neural signal transmission.

[0021] Furthermore, the neural state analysis module specifically includes the following working steps:

[0022] A1, analyzing in real time the changes in the user's body posture when receiving pain stimulation from the pain stimulation device through a body image analysis module, and decomposing the changes in the body posture into changes in the posture of the bones in the body;

[0023] A2, analyzing the shape change of human nerves under the condition of the posture change of the bones in the body obtained in step A1 based on the human nerve distribution model;

[0024] A3, based on the force analysis model, the force change of human nerves under the condition of the posture change of the bones in the body obtained in step A1;

[0025] A4, based on the neural signal transmission model, the shape change of the human nerve obtained in step A2 and the force change of the human nerve obtained in step A3 are used as interference variables of neural signal transmission, thereby obtaining the signal transmission state of the nervous system under the human posture condition of step A1;

[0026] A5, based on the signal transmission state of the nervous system under the human body posture condition of step A1 obtained in step A4, and through the data restoration model, the signal transmission state of the nervous system under the human body posture condition of step A1 obtained in step A4 is restored to the signal transmission state of the user's nervous system in a relaxed state.

[0027] Furthermore, the pain level assessment module includes:

[0028] A model preset module, wherein the model preset module is used to preset a pain level assessment model;

[0029] A data association module, the data association module is used to perform data association between the stimulation signal collected by the stimulation signal collection module and the neural response information collected by the neural response collection module through a data association model, and then obtain the response of the user's nerves under the stimulation of the stimulation signal;

[0030] A neural state analysis module is used to analyze the signal transmission state of the nervous system under the current user's body posture and the response of the user's nerves under the stimulation of the stimulation signal obtained in the data association module, and the data simulation model can simulate the perception level of pain of each nerve of the user under the stimulation of the stimulation signal.

[0031] Furthermore, the pain level assessment module specifically includes the following working steps:

[0032] B1, receiving the stimulation signal A collected by the stimulation signal collection module, the neural response information B collected by the neural response collection module, the signal transmission state C of the nervous system under the current user's body posture analyzed by the neural state analysis module, and the user's body posture information D collected by the image acquisition module;

[0033] B2, preset pain rating model;

[0034] B3, using the user's body posture information D collected by the image acquisition module as an input parameter of the preset pain level assessment model in step B2, and then obtaining the pain level exhibited by the user under the conditions of the preset pain level assessment model;

[0035] B4, through the data association model, the stimulation signal A collected by the stimulation signal acquisition module and the neural response information B collected by the neural response acquisition module are data associated, and then the response of the user's nerves under the stimulation of the stimulation signal A is obtained;

[0036] B5, based on the signal transmission state C of the nervous system under the current user's body posture analyzed by the neural state analysis module and the response of the user's nerves under the stimulation of the stimulation signal A obtained in step B4, the data simulation model can simulate the pain perception level of each nerve of the user under the stimulation of the stimulation signal A.

[0037] Further, the following working steps are included:

[0038] S1, medical staff logs in according to user identity information through the user login module;

[0039] S2, based on the user identity information of step S1, the data calling module is used to determine whether there is a pain level assessment model parameter corresponding to the user identity information in the user storage module through the data judgment model. If the judgment result is yes, the data calling module directly calls the pain level assessment model parameter corresponding to the user identity information from the user solution storage module. If the judgment result is no, the data calling module directly sends the user identity information to step S3;

[0040] S3, receiving the user identity information sent in step S2;

[0041] S4, preset data similarity judgment threshold;

[0042] S5, judging whether the data similarity between the user identity information stored in the user plan storage module and the user identity information sent by the data calling module is higher than the data similarity judgment threshold preset in step S4 through the data similarity judgment model, if the judgment result is yes, calling the pain level assessment model parameters corresponding to the user identity information stored in the user plan storage module through the data calling module, if the judgment result is no, executing step S6;

[0043] S6, performing pain stimulation on the user based on the pain stimulation device, and collecting stimulation signals of the stimulation levels output by the pain stimulation device and sending them to step S10;

[0044] S7, collecting the neural response information of the user when receiving pain stimulation from the pain stimulation device through the neural response collection module based on the electromyographic information collection device, and sending the neural response information to step S10;

[0045] S8, collecting the user's body posture information in real time through the image acquisition module based on the computer vision acquisition device, and sending the collected user's body posture information to step S9 and step S10 respectively;

[0046] S9, receiving the user body posture information collected in step S8 through the neural state analysis module, analyzing the signal transmission state of the nervous system under the current user body posture based on the neural state analysis model, and sending the signal transmission state of the nervous system under the current user body posture to step S10;

[0047] S10, based on the stimulation signal collected by the stimulation signal collection module, the neural response information collected by the neural response collection module, the signal transmission state of the nervous system under the current user's body posture analyzed by the neural state analysis module, and the user's body posture information collected by the image acquisition module, the pain level assessment module determines the user's own pain perception level under the output stimulation level of the pain stimulation device and adjusts the pain level assessment model parameters, and sends the pain level assessment model parameters to the user solution storage module.

[0048] Compared with the prior art, the computer vision-based pain stimulation automatic test and assessment system provided by the present invention can deduce the user's response to pain stimulation in different states by analyzing the signal transmission state of the nervous system. This ensures that in the subsequent use of the pain stimulation automatic test and assessment system, there is no need to consider the influence of the user's posture on pain transmission when receiving pain stimulation. At the same time, the shape change state of the human nerve obtained in the nerve shape change detection module and the force change state of the human nerve obtained in the nerve force change detection module are used as interference variables for nerve signal transmission. This can realize the pain signal that each nerve of the user can feel during the pain transmission process. This can not only realize the anesthesia and analgesia process By ensuring the anesthesia and analgesia of the nerves with the lowest pain perception level, the anesthesia and analgesia of all the user's nerves can be guaranteed. At the same time, through the data restoration model, the signal transmission state of the nervous system under the human posture conditions of the body image analysis module obtained in the neural signal transmission model is restored to the signal transmission state of the user's nervous system in a relaxed state. After the anesthesia and analgesia completes the relaxation of the human body, the signal transmission state of the user's nervous system in a relaxed state can be used to determine the amount of drugs for continuous anesthesia and analgesia. This can not only avoid excessive use of continuous anesthesia and analgesia drugs that prolongs the user's awakening process, but also avoid the occurrence of situations such as the user feeling pain due to the low amount of drugs used during the use of continuous anesthesia and analgesia. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0050] Figure 1A system structure block diagram provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0052] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0053] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0054] Example embodiments will be described more fully below with reference to the accompanying drawings, but the example embodiments may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. On the contrary, the purpose of providing these embodiments is to make the present disclosure thorough and complete and to enable those skilled in the art to fully understand the scope of the present disclosure.

[0055] In the absence of conflict, the various embodiments of the present disclosure and the various features therein may be combined with each other.

[0056] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0057] The terms used herein are only used to describe specific embodiments and are not intended to limit the present disclosure. As used herein, the singular forms "a", "an" and "the" are also intended to include the plural forms, unless the context clearly indicates otherwise. It will also be understood that when the terms "comprising" and / or "made of" are used in this specification, the presence of the features, wholes, steps, operations, elements and / or components is specified, but the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof is not excluded.

[0058] The embodiments described herein may be described with reference to plan views and / or cross-sectional views by means of idealized schematic diagrams of the present disclosure. Therefore, the example illustrations may be modified according to manufacturing techniques and / or tolerances. Therefore, the embodiments are not limited to the embodiments shown in the accompanying drawings, but include modifications of the configurations formed based on the manufacturing process. Therefore, the regions illustrated in the accompanying drawings have schematic properties, and the shapes of the regions shown in the figures illustrate the specific shapes of the regions of the elements, but are not intended to be limiting.

[0059] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted as having an idealized or overly formal meaning unless explicitly defined as such herein.

[0060] See also Figure 1 , the automatic test and assessment system for pain stimulation based on computer vision includes stimulation signal acquisition module, nerve response acquisition module, image acquisition module, nerve state analysis module and pain level assessment module:

[0061] The stimulation signal acquisition module is used to perform pain stimulation on the user based on the pain stimulation device, and collect the stimulation signal of the output stimulation level of the pain stimulation device and send it to the pain level assessment module;

[0062] The neural response acquisition module is used to collect the neural response information of the user when receiving pain stimulation from the pain stimulation device based on the electromyographic information acquisition device, and send the neural response information to the pain level assessment module;

[0063] The image acquisition module is used to acquire the user's body posture information in real time based on the computer vision acquisition device, and send the acquired user's body posture information to the nerve state analysis module and the pain level assessment module respectively;

[0064] The neural state analysis module is used to receive the user's body posture information collected by the image acquisition module, and analyze the signal transmission state of the nervous system under the current user's body posture based on the neural state analysis model, and send the signal transmission state of the nervous system under the current user's body posture to the pain level assessment module;

[0065] The pain level assessment module is used to judge the user's own pain perception level under the output stimulation level of the pain stimulation device and adjust the pain level assessment model parameters based on the stimulation signal collected by the stimulation signal acquisition module, the neural response information collected by the neural response acquisition module, the signal transmission state of the nervous system under the current user's body posture analyzed by the neural state analysis module, and the user's body posture information collected by the image acquisition module, and send the pain level assessment model parameters to the user solution storage module.

[0066] Also includes:

[0067] User login module, which is used by medical staff to log in users according to user identity information and send the user identity information to the user plan storage module and the data call module. The user identity information includes user disease information, user body shape information and user grade information;

[0068] A user scheme storage module, the user scheme storage module is used to store the pain level assessment model parameters corresponding to the user based on the user identity information of the user login module and the pain level assessment model parameters generated by the pain level assessment module;

[0069] The data calling module is used to judge whether there are pain level assessment model parameters corresponding to the user identity information in the user storage scheme based on the user identity information of the user login module through the data judgment model. If the judgment result is yes, the data calling module directly calls the pain level assessment model parameters corresponding to the user identity information from the user scheme storage module. If the judgment result is no, the data calling module directly sends the user identity information to the data similarity judgment module;

[0070] The data similarity judgment module is used to receive the user identity information sent by the data calling module, preset the data similarity judgment threshold, and judge through the data similarity judgment model whether the data similarity between the user identity information stored in the user scheme storage module and the user identity information sent by the data calling module is higher than the preset data similarity judgment threshold. If the judgment result is yes, the pain level assessment model parameters corresponding to the user identity information stored in the user scheme storage module are called through the data calling module.

[0071] The neural state analysis module includes:

[0072] A body image analysis module, which is used to analyze in real time the changes in the user's body posture when receiving pain stimulation from the pain stimulation device, and decompose the changes in body posture into changes in the posture of bones in the body;

[0073] A nerve shape change detection module, which is used to analyze the shape change of human nerves under the condition of the posture change of the bones in the body obtained by the body image analysis module based on the human nerve distribution model. The shape change of human nerves includes stretching, compression and bending of human nerves.

[0074] A nerve force change detection module is used to detect the force change of human nerves under the condition of the posture change of the bones in the body obtained by the body image analysis module based on the force analysis model. The force change of human nerves includes human nerve compression, no change in human nerve force, and reduced human nerve force.

[0075] The neural transmission state monitoring module is used to obtain the signal transmission state of the nervous system under the human posture condition of the body image analysis module based on the neural signal transmission model and taking the shape change condition of the human nerve obtained in the neural shape change detection module and the force change condition of the human nerve obtained in the neural force change detection module as interference variables of the neural signal transmission.

[0076] This is because different parts of the nervous system are in different states in different body postures, so there will be errors in the transmission of pain signals by the nervous system. By analyzing the signal transmission state of the nervous system, the user's response to pain stimuli in different states can be deduced. This can ensure that in the subsequent use of the pain stimulation automatic test and assessment system, there is no need to consider the influence of the user's posture on pain transmission when receiving pain stimulation. At the same time, the shape change of the human nerve obtained in the nerve shape change detection module and the force change of the human nerve obtained in the nerve force change detection module are used as interference variables for nerve signal transmission. The pain signal that each nerve of the user can feel during the pain transmission process can be realized. This can not only achieve the purpose of automatic testing and assessment of pain stimulation during the subsequent use of the pain stimulation automatic test and assessment system, but also can ensure the effectiveness of automatic testing and assessment of pain stimulation during the subsequent use of the pain stimulation automatic test and assessment system. During the anesthesia and analgesia process, by ensuring the anesthesia and analgesia of the nerves with the lowest pain perception level, the anesthesia and analgesia of all the user's nerves can be guaranteed. At the same time, through the data restoration model, the signal transmission state of the nervous system under the human posture conditions of the body image analysis module obtained in the neural signal transmission model is restored to the signal transmission state of the user's nervous system in a relaxed state. After the anesthesia and analgesia completes the relaxation of the human body, the signal transmission state of the user's nervous system in a relaxed state can be used to determine the amount of drugs used for continuous anesthesia and analgesia. This can not only avoid excessive use of continuous anesthesia and analgesia drugs, which will cause the user's awakening process to be prolonged, but also avoid the occurrence of situations such as the user feeling pain due to the low amount of drugs used during the use of continuous anesthesia and analgesia.

[0077] The neural state analysis module specifically includes the following working steps:

[0078] A1, analyzing in real time the changes in the user's body posture when receiving pain stimulation from the pain stimulation device through a body image analysis module, and decomposing the changes in the body posture into changes in the posture of the bones in the body;

[0079] A2, analyzing the shape change of human nerves under the condition of the posture change of the bones in the body obtained in step A1 based on the human nerve distribution model;

[0080] A3, based on the force analysis model, the force change of human nerves under the condition of the posture change of the bones in the body obtained in step A1;

[0081] A4, based on the neural signal transmission model, the shape change of the human nerve obtained in step A2 and the force change of the human nerve obtained in step A3 are used as interference variables of neural signal transmission, thereby obtaining the signal transmission state of the nervous system under the human posture condition of step A1;

[0082] A5, based on the signal transmission state of the nervous system under the human body posture condition of step A1 obtained in step A4, and through the data restoration model, the signal transmission state of the nervous system under the human body posture condition of step A1 obtained in step A4 is restored to the signal transmission state of the user's nervous system in a relaxed state.

[0083] The pain rating module includes:

[0084] A model preset module, which is used to preset a pain level assessment model;

[0085] A data association module, which is used to perform data association between the stimulation signal collected by the stimulation signal collection module and the neural response information collected by the neural response collection module through a data association model, and then obtain the response of the user's nerves under the stimulation of the stimulation signal;

[0086] The neural state analysis module is used to analyze the signal transmission state of the nervous system under the current user's body posture and the response of the user's nerves under the stimulation of the stimulation signal obtained in the data association module. The data simulation model can also simulate the pain perception level of each nerve of the user under the stimulation of the stimulation signal.

[0087] The pain level assessment module specifically includes the following working steps:

[0088] B1, receiving the stimulation signal A collected by the stimulation signal collection module, the neural response information B collected by the neural response collection module, the signal transmission state C of the nervous system under the current user's body posture analyzed by the neural state analysis module, and the user's body posture information D collected by the image acquisition module;

[0089] B2, preset pain rating model;

[0090] B3, using the user's body posture information D collected by the image acquisition module as an input parameter of the preset pain level assessment model in step B2, and then obtaining the pain level exhibited by the user under the conditions of the preset pain level assessment model;

[0091] B4, through the data association model, the stimulation signal A collected by the stimulation signal acquisition module and the neural response information B collected by the neural response acquisition module are data associated, and then the response of the user's nerves under the stimulation of the stimulation signal A is obtained;

[0092] B5, based on the signal transmission state C of the nervous system under the current user's body posture analyzed by the neural state analysis module and the response of the user's nerves under the stimulation of the stimulation signal A obtained in step B4, the data simulation model can simulate the pain perception level of each nerve of the user under the stimulation of the stimulation signal A.

[0093] The following steps are included:

[0094] S1, medical staff logs in according to user identity information through the user login module;

[0095] S2, based on the user identity information of step S1, the data calling module is used to determine whether there is a pain level assessment model parameter corresponding to the user identity information in the user storage module through the data judgment model. If the judgment result is yes, the data calling module directly calls the pain level assessment model parameter corresponding to the user identity information from the user solution storage module. If the judgment result is no, the data calling module directly sends the user identity information to step S3;

[0096] S3, receiving the user identity information sent in step S2;

[0097] S4, preset data similarity judgment threshold;

[0098] S5, judging whether the data similarity between the user identity information stored in the user plan storage module and the user identity information sent by the data calling module is higher than the data similarity judgment threshold preset in step S4 through the data similarity judgment model, if the judgment result is yes, calling the pain level assessment model parameters corresponding to the user identity information stored in the user plan storage module through the data calling module, if the judgment result is no, executing step S6;

[0099] S6, performing pain stimulation on the user based on the pain stimulation device, and collecting stimulation signals of the stimulation levels output by the pain stimulation device and sending them to step S10;

[0100] S7, collecting the neural response information of the user when receiving pain stimulation from the pain stimulation device through the neural response collection module based on the electromyographic information collection device, and sending the neural response information to step S10;

[0101] S8, collecting the user's body posture information in real time through the image acquisition module based on the computer vision acquisition device, and sending the collected user's body posture information to step S9 and step S10 respectively;

[0102] S9, receiving the user body posture information collected in step S8 through the neural state analysis module, analyzing the signal transmission state of the nervous system under the current user body posture based on the neural state analysis model, and sending the signal transmission state of the nervous system under the current user body posture to step S10;

[0103] S10, based on the stimulation signal collected by the stimulation signal acquisition module, the neural response information collected by the neural response acquisition module, the signal transmission state of the nervous system under the current user's body posture analyzed by the neural state analysis module, and the user's body posture information collected by the image acquisition module, the pain level assessment module determines the user's own pain perception level under the output stimulation level of the pain stimulation device and adjusts the pain level assessment model parameters, and sends the pain level assessment model parameters to the user solution storage module.

[0104] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A computer vision-based pain stimulation automatic test and assessment system, comprising a stimulation signal acquisition module, a nerve response acquisition module, an image acquisition module, a nerve state analysis module, and a pain level assessment module, characterized in that: The stimulation signal acquisition module is used to perform pain stimulation on the user based on the pain stimulation device, and collect stimulation signals of the output stimulation level of the pain stimulation device and send them to the pain level assessment module; The neural response acquisition module is used to collect the neural response information of the user when receiving pain stimulation from the pain stimulation device based on the electromyographic information acquisition device, and send the neural response information to the pain level assessment module; The image acquisition module is used to acquire user body posture information in real time based on a computer vision acquisition device, and send the acquired user body posture information to the nerve state analysis module and the pain level assessment module respectively; The neural state analysis module is used to receive the user's body posture information collected by the image acquisition module, analyze the signal transmission state of the nervous system under the current user's body posture based on the neural state analysis model, and send the signal transmission state of the nervous system under the current user's body posture to the pain level assessment module; The pain level assessment module is used to judge the user's own pain perception level under the output stimulation level of the pain stimulation device and adjust the pain level assessment model parameters based on the stimulation signal collected by the stimulation signal acquisition module, the neural response information collected by the neural response acquisition module, the signal transmission state of the nervous system under the current user's body posture analyzed by the neural state analysis module, and the user's body posture information collected by the image acquisition module, and send the pain level assessment model parameters to the user plan storage module.

2. The computer vision-based pain stimulation automatic test and assessment system according to claim 1, characterized in that: Also includes: A user login module, which is used by medical staff to log in users according to user identity information and send the user identity information to the user plan storage module and the data calling module. The user identity information includes user disease information, user body shape information and user age information; A user scheme storage module, the user scheme storage module is used to store the pain level assessment model parameters corresponding to the user based on the user identity information of the user login module and the pain level assessment model parameters generated by the pain level assessment module; A data calling module, the data calling module is used to judge whether there is a pain level assessment model parameter corresponding to the user identity information in the user storage scheme based on the user identity information of the user login module and through the data judgment model, if the judgment result is yes, the data calling module directly calls the pain level assessment model parameter corresponding to the user identity information from the user scheme storage module, if the judgment result is no, the data calling module directly sends the user identity information to the data similarity judgment module; A data similarity judgment module, the data similarity judgment module is used to receive the user identity information sent by the data calling module, preset the data similarity judgment threshold, and judge through the data similarity judgment model whether the data similarity between the user identity information stored in the user scheme storage module and the user identity information sent by the data calling module is higher than the preset data similarity judgment threshold; if the judgment result is yes, the pain level assessment model parameters corresponding to the user identity information stored in the user scheme storage module are called through the data calling module.

3. The computer vision-based pain stimulation automatic test and assessment system according to claim 2, characterized in that: The neural state analysis module comprises: A body image analysis module, the body image analysis module is used to analyze in real time the changes in the user's body posture when receiving pain stimulation from the pain stimulation device, and decompose the changes in body posture into changes in the posture of bones in the body; A nerve shape change detection module, the nerve shape change detection module is used to analyze the shape change of human nerves under the condition of the posture change of the bones in the body obtained by the body image analysis module based on the human nerve distribution model, the shape change of human nerves including human nerve stretching, human nerve compression and human nerve bending; A nerve force change detection module, which is used to detect the force change status of human nerves under the condition of the posture change of the bones in the body obtained by the body image analysis module based on the force analysis model, and the force change status of human nerves includes human nerve compression, no change in human nerve force, and reduced human nerve force; A neural transmission state monitoring module is used to obtain the signal transmission state of the nervous system under the human posture condition of the body image analysis module based on the neural signal transmission model and taking the shape change condition of the human nerve obtained in the neural shape change detection module and the force change condition of the human nerve obtained in the neural force change detection module as interference variables of the neural signal transmission.

4. The computer vision-based pain stimulation automatic test and assessment system according to claim 3, characterized in that: The neural state analysis module specifically includes the following working steps: A1, analyzing in real time the changes in the user's body posture when receiving pain stimulation from the pain stimulation device through a body image analysis module, and decomposing the changes in the body posture into changes in the posture of the bones in the body; A2, analyzing the shape change of human nerves under the condition of the posture change of the bones in the body obtained in step A1 based on the human nerve distribution model; A3, based on the force analysis model, the force change of human nerves under the condition of the posture change of the bones in the body obtained in step A1; A4, based on the neural signal transmission model, the shape change of the human nerve obtained in step A2 and the force change of the human nerve obtained in step A3 are used as interference variables of neural signal transmission, thereby obtaining the signal transmission state of the nervous system under the human posture condition of step A1; A5, based on the signal transmission state of the nervous system under the human body posture condition of step A1 obtained in step A4, and through the data restoration model, the signal transmission state of the nervous system under the human body posture condition of step A1 obtained in step A4 is restored to the signal transmission state of the user's nervous system in a relaxed state.

5. The computer vision-based pain stimulation automatic test and assessment system according to claim 4, characterized in that: The pain level assessment module includes: A model preset module, wherein the model preset module is used to preset a pain level assessment model; A data association module, the data association module is used to perform data association between the stimulation signal collected by the stimulation signal collection module and the neural response information collected by the neural response collection module through a data association model, and then obtain the response of the user's nerves under the stimulation of the stimulation signal; A neural state analysis module is used to analyze the signal transmission state of the nervous system under the current user's body posture and the response of the user's nerves under the stimulation of the stimulation signal obtained in the data association module, and the data simulation model can simulate the perception level of pain of each nerve of the user under the stimulation of the stimulation signal.

6. The computer vision-based pain stimulation automatic test and assessment system according to claim 5, characterized in that: The pain level assessment module specifically includes the following working steps: B1, receiving the stimulation signal A collected by the stimulation signal collection module, the neural response information B collected by the neural response collection module, the signal transmission state C of the nervous system under the current user's body posture analyzed by the neural state analysis module, and the user's body posture information D collected by the image acquisition module; B2, preset pain rating model; B3, using the user's body posture information D collected by the image acquisition module as an input parameter of the preset pain level assessment model in step B2, and then obtaining the pain level exhibited by the user under the conditions of the preset pain level assessment model; B4, through the data association model, the stimulation signal A collected by the stimulation signal acquisition module and the neural response information B collected by the neural response acquisition module are data associated, and then the response of the user's nerves under the stimulation of the stimulation signal A is obtained; B5, based on the signal transmission state C of the nervous system under the current user's body posture analyzed by the neural state analysis module and the response of the user's nerves under the stimulation of the stimulation signal A obtained in step B4, the data simulation model can simulate the pain perception level of each nerve of the user under the stimulation of the stimulation signal A.

7. The computer vision-based automatic pain stimulation test and assessment system according to claim 6, characterized in that: The following steps are included: S1, medical staff logs in according to user identity information through the user login module; S2, based on the user identity information of step S1, the data calling module is used to determine whether there is a pain level assessment model parameter corresponding to the user identity information in the user storage module through the data judgment model. If the judgment result is yes, the data calling module directly calls the pain level assessment model parameter corresponding to the user identity information from the user solution storage module. If the judgment result is no, the data calling module directly sends the user identity information to step S3; S3, receiving the user identity information sent in step S2; S4, preset data similarity judgment threshold; S5, judging whether the data similarity between the user identity information stored in the user plan storage module and the user identity information sent by the data calling module is higher than the data similarity judgment threshold preset in step S4 through the data similarity judgment model, if the judgment result is yes, calling the pain level assessment model parameters corresponding to the user identity information stored in the user plan storage module through the data calling module, if the judgment result is no, executing step S6; S6, performing pain stimulation on the user based on the pain stimulation device, and collecting stimulation signals of the stimulation levels output by the pain stimulation device and sending them to step S10; S7, collecting the neural response information of the user when receiving pain stimulation from the pain stimulation device through the neural response collection module based on the electromyographic information collection device, and sending the neural response information to step S10; S8, collecting the user's body posture information in real time through the image acquisition module based on the computer vision acquisition device, and sending the collected user's body posture information to step S9 and step S10 respectively; S9, receiving the user body posture information collected in step S8 through the neural state analysis module, analyzing the signal transmission state of the nervous system under the current user body posture based on the neural state analysis model, and sending the signal transmission state of the nervous system under the current user body posture to step S10; S10, based on the stimulation signal collected by the stimulation signal collection module, the neural response information collected by the neural response collection module, the signal transmission state of the nervous system under the current user's body posture analyzed by the neural state analysis module, and the user's body posture information collected by the image acquisition module, the pain level assessment module determines the user's own pain perception level under the output stimulation level of the pain stimulation device and adjusts the pain level assessment model parameters, and sends the pain level assessment model parameters to the user solution storage module.

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