Pelvic floor muscle rehabilitation exercise monitoring electrode slice, monitoring system and method

Abstract

The invention relates to a pelvic floor muscle rehabilitation exercise monitoring electrode slice, a pelvic floor muscle rehabilitation exercise monitoring system and a pelvic floor muscle rehabilitation exercise monitoring method. The pelvic floor muscle rehabilitation exercise monitoring electrode slice monitors and uploads a myoelectricity value when a patient performs pelvic floor muscle rehabilitation exercise; through a pelvic floor muscle rehabilitation exercise monitoring background, pelvic floor muscle clinical examination symptom characteristics and myoelectricity value characteristics of a patient are intelligently recognized by utilizing a pelvic floor muscle rehabilitation exercise strategy AI recommendation model, and a corresponding pelvic floor muscle rehabilitation exercise strategy is output in a matched mode and issued to the pelvic floor muscle rehabilitation exercise monitoring electrode slice worn by the patient. Intelligent monitoring and management of pelvic floor muscle rehabilitation exercise can be achieved in combination with the electrode slice, instructive rehabilitation exercise is conducted on a patient, the patient conducts rehabilitation using the electrode slice under the guidance of medical advice and obtains timely feedback of a background, invalid or low-efficiency exercise is avoided, and therefore the pelvic floor muscle rehabilitation exercise quality is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of medical rehabilitation technologies, and particularly to a pelvic floor muscle rehabilitation exercise monitoring electrode sheet, a monitoring system, a monitoring method, and an electronic device. Background Art

[0002] Situations where pelvic floor muscle rehabilitation exercises are required mainly include the following: I. Postpartum women During childbirth, the pelvic floor muscles will be damaged to a certain extent, resulting in pelvic floor muscle relaxation. In order to quickly restore the function of the pelvic floor muscles, postpartum women need to perform pelvic floor muscle rehabilitation exercises. This can not only improve the condition of pelvic floor muscle relaxation, but also prevent and treat pelvic floor dysfunctional diseases, such as urinary incontinence, uterine prolapse, etc.

[0003] II. Those with damaged pelvic floor muscles Those with damaged pelvic floor muscles due to trauma, surgery, or other reasons also need to perform pelvic floor muscle rehabilitation exercises. Through exercise, the tension and endurance of the pelvic floor muscles can be enhanced, and the repair and regeneration of damaged tissues can be promoted.

[0004] III. Patients with pelvic floor dysfunctional diseases For patients who already have pelvic floor dysfunctional diseases, such as urinary incontinence, uterine prolapse, vaginal relaxation, etc., pelvic floor muscle rehabilitation exercise is one of the important treatment means. Through exercise, symptoms can be improved and the quality of life can be enhanced.

[0005] IV. People at high risk of pelvic floor muscle injury People with high-risk factors for pelvic floor muscle injury, such as constipation, obesity, multiple childbirths, multiple pregnancies, etc., also need to regularly perform pelvic floor muscle rehabilitation exercises. This can prevent and reduce the occurrence of pelvic floor muscle injury and maintain the healthy state of the pelvic floor muscles.

[0006] Therefore, pelvic floor muscle rehabilitation exercises are of great significance for maintaining the health and function of pelvic floor muscles. In the existing technology, patients usually use a pelvic floor muscle repair electrode sheet as shown in the appendix Figure 1 (the main body of which is mainly composed of a data wire and a pelvic floor muscle electrode sheet, and the wire is connected to a controller to control the electrode work of the electrode sheet) for pelvic floor muscle rehabilitation exercises. When performing the exercises, the patient attaches the pelvic floor muscle electrode sheet to the perineum and selects a suitable exercise method and intensity according to personal circumstances (the corresponding working mode levels will be displayed on the controller or control terminal, and the patient can press the corresponding mode button).

[0007] However, when patients purchase and use the pelvic floor muscle repair electrode sheet, they can only use it according to the product instructions, and there are the following application drawbacks during this period: First, patients cannot perform rehabilitation under the guidance of medical advice, which easily leads to improper use, insufficient or excessive rehabilitation time, and lack of medical guidance. Second, medical staff cannot timely obtain the pelvic floor muscle rehabilitation exercise parameters of patients, and cannot conduct phased rehabilitation exercise guidance and encouragement. Therefore, the pelvic floor muscle rehabilitation results of patients are poor. Summary of the Invention

[0008] To solve the above problems, the present application proposes the following solutions: On the one hand, the present application proposes a pelvic floor muscle rehabilitation exercise monitoring electrode patch, including: A plurality of electrode patches for monitoring and collecting the myoelectric signals of the patient's pelvic floor muscles and feeding them back to the A / D converter; The A / D converter is used for digital-to-analog conversion of the myoelectric signals, generating corresponding electrical signals and sending them to the MCU; The MCU is used to control the sampling of the electrode patches and generate corresponding myoelectric values according to the electrical signals, and send the myoelectric values to the display and the wireless communication module respectively; The display is used to display the myoelectric value in real time; The wireless communication module is used to report the myoelectric value to the pelvic floor muscle rehabilitation exercise monitoring background; if the pelvic floor muscle rehabilitation exercise monitoring background finds that the myoelectric value of the patient does not meet the preset training intensity, it issues an alarm signal corresponding to the training intensity and forwards it to the MCU through the wireless communication module, and after being processed by the MCU, it is forwarded to the alarm; The alarm is used to respond to the alarm signal and remind the patient to adjust the training posture or strength; The power supply is used for power supply; The electrode patch is electrically connected to the A / D converter; The A / D converter, the display, the wireless communication module, the alarm and the power supply are respectively electrically connected to the MCU.

[0009] On the other hand, the present application proposes a pelvic floor muscle rehabilitation exercise monitoring system, including: The above-mentioned pelvic floor muscle rehabilitation exercise monitoring electrode patch for monitoring and uploading the myoelectric value when the patient performs pelvic floor muscle rehabilitation exercise; The pelvic floor muscle rehabilitation exercise monitoring background, including: The file management module is used to record the pelvic floor muscle rehabilitation exercise time, frequency of each exercise of the patient and the training intensity converted based on the myoelectric value of the patient, and write them into the patient's electronic medical record file; The device management module is used to manage the pelvic floor muscle rehabilitation exercise monitoring electrode patch worn by the patient; A processing system for providing data processing and task scheduling services, including: sending a test instruction to the pelvic floor muscle rehabilitation exercise monitoring electrode patch worn by a patient to collect corresponding pelvic floor muscle rehabilitation exercise test data; and, reading the pelvic floor muscle clinical examination data of the patient, and inputting the pelvic floor muscle clinical examination data and the pelvic floor muscle rehabilitation exercise test data into a preset pelvic floor muscle rehabilitation exercise strategy AI recommendation model, identifying the pelvic floor muscle clinical examination symptom characteristics and myoelectric value characteristics of the patient through the pelvic floor muscle rehabilitation exercise strategy AI recommendation model, matching and outputting corresponding pelvic floor muscle rehabilitation exercise strategies and sending them to the pelvic floor muscle rehabilitation exercise monitoring electrode patch worn by the patient; at the same time, writing the pelvic floor muscle rehabilitation exercise strategy into the patient's electronic medical record file; A medical staff terminal for logging in to the background and viewing the patient's electronic medical record file; The pelvic floor muscle rehabilitation exercise monitoring electrode patch and the medical staff terminal are respectively communicatively connected to the pelvic floor muscle rehabilitation exercise monitoring background.

[0010] As an optional implementation scheme of the present application, optionally, the processing system is further used for: Judging whether the myoelectric value of the patient meets a preset training intensity: If not, generating a corresponding alarm signal and retrieving nursing education and evaluation information corresponding to the preset training intensity from the education database, and sending it to the pelvic floor muscle rehabilitation exercise monitoring electrode patch worn by the patient; If it meets the requirement, retrieving nursing education and evaluation information corresponding to the training intensity from the education database, and sending it to the pelvic floor muscle rehabilitation exercise monitoring electrode patch worn by the patient.

[0011] As an optional implementation scheme of the present application, optionally, the pelvic floor muscle rehabilitation exercise monitoring background further includes: An education database for storing a number of the nursing education and evaluation information and the pelvic floor muscle rehabilitation exercise strategies corresponding to different training intensities.

[0012] As an optional implementation scheme of the present application, optionally, the method for generating the pelvic floor muscle rehabilitation exercise strategy AI recommendation model includes: Collecting corresponding pelvic floor muscle rehabilitation exercise historical big data from the file management modules of a number of patients, including: the pelvic floor muscle clinical historical examination data of the patients, the pelvic floor muscle rehabilitation exercise monitoring data, and the corresponding pelvic floor muscle rehabilitation exercise strategies; Performing feature engineering on the pelvic floor muscle rehabilitation exercise historical big data to extract corresponding pelvic floor muscle rehabilitation exercise recommendation features, including: extracting the pelvic floor muscle clinical examination symptom characteristics of the patient from the pelvic floor muscle clinical historical examination data, extracting the myoelectric value characteristics of the patient from the pelvic floor muscle rehabilitation exercise monitoring data, and extracting the corresponding strategy characteristics from the pelvic floor muscle rehabilitation exercise strategies, that is: The recommended features for pelvic floor muscle rehabilitation exercises = {clinical examination symptom features of pelvic floor muscles, myoelectric value features, strategy features}; Statistically analyze the recommended features for pelvic floor muscle rehabilitation exercises of each patient and form them into a feature set; Divide the feature set into a training set and a validation set according to a preset ratio; Input the training set into a preset random forest model, enabling the model to train and learn different associated features to generate the AI recommendation model for pelvic floor muscle rehabilitation exercise strategies; Use the validation set to verify the recommendation performance of the AI recommendation model for pelvic floor muscle rehabilitation exercise strategies: If the verification is passed, deploy the AI recommendation model for pelvic floor muscle rehabilitation exercise strategies on the monitoring background of pelvic floor muscle rehabilitation exercises; If the verification fails, repeat the above steps to reconstruct the AI recommendation model for pelvic floor muscle rehabilitation exercise strategies.

[0013] On the other hand, this application proposes a method for monitoring pelvic floor muscle rehabilitation exercises, which is implemented based on the above-mentioned system and includes: The patient wears and activates the monitoring electrode patches for pelvic floor muscle rehabilitation exercises and requests communication with the monitoring background of pelvic floor muscle rehabilitation exercises; The monitoring background of pelvic floor muscle rehabilitation exercises responds to the request, establishes a data communication channel with the monitoring electrode patches for pelvic floor muscle rehabilitation exercises worn by the patient, and binds the monitoring electrode patches for pelvic floor muscle rehabilitation exercises with the patient's visit ID; The processing system sends a preset test instruction to the monitoring electrode patches for pelvic floor muscle rehabilitation exercises worn by the patient, and the monitoring electrode patches for pelvic floor muscle rehabilitation exercises worn by the patient respond to the test instruction, collect and report the corresponding test data for pelvic floor muscle rehabilitation exercises; The processing system reads the clinical examination data of the patient's pelvic floor muscles from the patient's electronic medical record file, inputs the clinical examination data of the patient's pelvic floor muscles and the test data for pelvic floor muscle rehabilitation exercises into the preset AI recommendation model for pelvic floor muscle rehabilitation exercise strategies, identifies the clinical examination symptom features and myoelectric value features of the patient's pelvic floor muscles through the AI recommendation model for pelvic floor muscle rehabilitation exercise strategies, matches and outputs the pelvic floor muscle rehabilitation exercise strategies corresponding to the clinical examination symptom features and myoelectric value features of the patient, and sends them to the monitoring electrode patches for pelvic floor muscle rehabilitation exercises worn by the patient; at the same time, write the pelvic floor muscle rehabilitation exercise strategies into the patient's electronic medical record file; The monitoring electrode patches for pelvic floor muscle rehabilitation exercises worn by the patient receive and parse the pelvic floor muscle rehabilitation exercise strategies, obtain the electrode patch control instructions therein, and the MCU executes the instructions to achieve the working control of the electrode patches; The pelvic floor muscle rehabilitation exercise monitoring electrode sheet monitors and uploads the myoelectric value of the patient during pelvic floor muscle rehabilitation exercise to the pelvic floor muscle rehabilitation exercise monitoring background; The pelvic floor muscle rehabilitation exercise monitoring background records the pelvic floor muscle rehabilitation exercise time, frequency of each exercise of the patient, and the training intensity obtained by converting the myoelectric value of the patient, and writes them into the patient's electronic medical record file; and judges whether the myoelectric value of the patient meets the preset training intensity: If it does not meet the requirements, a corresponding alarm signal is generated, and the nursing education and evaluation information corresponding to the preset training intensity is retrieved from the education database and sent to the pelvic floor muscle rehabilitation exercise monitoring electrode sheet worn by the patient; If it meets the requirements, the nursing education and evaluation information corresponding to the training intensity is retrieved from the education database and sent to the pelvic floor muscle rehabilitation exercise monitoring electrode sheet worn by the patient; Medical staff log in to the background through the medical staff terminal and view the patient's electronic medical record file.

[0014] On the other hand, the present application also proposes an electronic device, including: A processor; A memory for storing instructions executable by the processor; Wherein, the processor is configured to implement the monitoring method when executing the executable instructions.

[0015] The technical effect of the present invention: The present application monitors and uploads the myoelectric value of the patient during pelvic floor muscle rehabilitation exercise through the pelvic floor muscle rehabilitation exercise monitoring electrode sheet; through the pelvic floor muscle rehabilitation exercise monitoring background, using the pelvic floor muscle rehabilitation exercise strategy AI recommendation model, it intelligently identifies the clinical examination symptom characteristics and myoelectric value characteristics of the patient's pelvic floor muscles, matches and outputs the corresponding pelvic floor muscle rehabilitation exercise strategy and sends it to the pelvic floor muscle rehabilitation exercise monitoring electrode sheet worn by the patient. It can combine the electrode sheet to realize the intelligent monitoring and management of pelvic floor muscle rehabilitation exercise, conduct guiding rehabilitation exercise for the patient, enable the patient to use the electrode sheet for rehabilitation under the guidance of medical advice and receive timely feedback from the background, avoid ineffective or inefficient exercise, and thus improve the quality of pelvic floor muscle rehabilitation exercise.

[0016] According to the following detailed description of the exemplary embodiments with reference to the accompanying drawings, other features and aspects of the present disclosure will become clear. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings included in the specification and constituting a part of the specification show the exemplary embodiments, features and aspects of the present disclosure together with the specification, and are used to explain the principles of the present disclosure.

[0018] Figure 1 Shows the application structure schematic diagram of the pelvic floor muscle repair electrode sheet applied in the prior art; Figure 2 It shows a schematic structural diagram of the electrode sheet integrated circuit of the present invention; Figure 3 It shows a schematic structural diagram of the composition of the application system of the present invention; Figure 4 It shows a schematic diagram of the model generation process of the present invention; Figure 5 It shows a schematic diagram of the application of the electronic device of the present invention. Specific embodiments

[0019] Hereinafter, various exemplary embodiments, features and aspects of the present disclosure will be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements having the same or similar functions. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0020] The special term "exemplary" here means "serving as an example, embodiment or illustration". Any embodiment described as "exemplary" here is not necessarily to be construed as superior to or better than other embodiments.

[0021] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. Those skilled in the art should understand that the present disclosure can be implemented without some specific details. In some instances, means, elements and circuits well known to those skilled in the art are not described in detail so as to highlight the gist of the present disclosure.

[0022] In the present invention, regarding the structure, size, application control principle and hardware structure of the electrode sheet, etc., the application technology of the existing pelvic floor muscle repair electrode sheet can be referred to. For the gear operation of the electrode sheet, etc., it can be understood in combination with the usage instructions of the existing pelvic floor muscle repair electrode sheet. Embodiment 1

[0023] As Figure 2 shown, on the one hand, the present application proposes a pelvic floor muscle rehabilitation exercise monitoring electrode sheet, including: A plurality of electrode sheets for monitoring and collecting the myoelectric signals of the patient's pelvic floor muscles and feeding them back to the A / D converter; An A / D converter for performing digital-to-analog conversion on the myoelectric signals, generating corresponding electrical signals and sending them to the MCU; The MCU is used to control the sampling of the electrode sheet and generate corresponding myoelectric values according to the electrical signals, and send the myoelectric values to the display and the wireless communication module respectively; The display is used to display the myoelectric values in real time; A wireless communication module, which is used to report the myoelectric value to the pelvic floor muscle rehabilitation exercise monitoring background; if the pelvic floor muscle rehabilitation exercise monitoring background finds that the myoelectric value of the patient does not meet the preset training intensity, it will send a corresponding training intensity alarm signal, which is then forwarded by the wireless communication module to the MCU, and after being processed by the MCU, it is further forwarded to the alarm; An alarm, which is used to respond to the alarm signal and remind the patient to adjust the training posture or intensity; A power supply, which is used for power supply; The electrode patch is electrically connected to the A / D converter; The A / D converter, the display, the wireless communication module, the alarm and the power supply are respectively electrically connected to the MCU.

[0024] The following are the functions of each component and the interaction methods between them: 1. Electrode patch Function: Monitor and collect the myoelectric signals of the patient's pelvic floor muscles.

[0025] Implementation: Select a suitable electrode patch to ensure that it can accurately collect the myoelectric signals of the pelvic floor muscles. The electrode patch should be designed for single use to avoid cross-infection and ensure signal quality. The electrode patch should be placed at appropriate positions, such as the lower abdomen and the anterior superior iliac spine, to effectively collect the myoelectric signals.

[0026] 2. A / D converter Function: Perform analog-to-digital conversion on the myoelectric signals collected by the electrode patch to generate corresponding electrical signals.

[0027] Implementation: Select a suitable A / D converter, and its performance should meet the accuracy and speed requirements for myoelectric signal collection. The A / D converter should be able to convert the analog myoelectric signals into digital signals for subsequent processing.

[0028] 3. MCU (Microcontroller Unit) Function: Control the electrode patch to sample, generate corresponding myoelectric values according to the electrical signals output by the A / D converter, and send the myoelectric values to the display and the wireless communication module.

[0029] Implementation: Select a suitable MCU to ensure that it has sufficient computing power and communication interfaces. The MCU should be able to control the electrode patch to perform timed sampling, process the electrical signals output by the A / D converter to generate myoelectric values. The MCU should also have the ability to communicate with the display and the wireless communication module to display the myoelectric values in real time and report them to the pelvic floor muscle rehabilitation exercise monitoring background.

[0030] 4. Display Function: Display the myoelectric values sent by the MCU in real time.

[0031] Implementation: Select a suitable display to ensure that it can clearly and accurately display the EMG value. The display should be connected to the MCU through an appropriate interface (such as SPI, I2C, etc.) to receive and display the EMG value in real time.

[0032] 5. Wireless communication module Function: Report the EMG value sent by the MCU to the pelvic floor muscle rehabilitation exercise monitoring background and receive the alarm signal from the background.

[0033] Implementation: Select a suitable wireless communication module to ensure that it can communicate with the pelvic floor muscle rehabilitation exercise monitoring background stably and reliably. The wireless communication module should be connected to the MCU through an appropriate interface (such as UART, SPI, etc.) to receive the EMG value and report it to the background. At the same time, the wireless communication module should also be able to receive the alarm signal from the background and forward it to the MCU.

[0034] 6. Pelvic floor muscle rehabilitation exercise monitoring background Function: Receive the EMG value reported by the wireless communication module, judge whether it meets the preset training intensity, and send down the alarm signal.

[0035] Implementation: The pelvic floor muscle rehabilitation exercise monitoring background should have the ability of data processing and analysis, and be able to judge whether the patient's training intensity meets the preset requirements according to the received EMG value. If not, the background should generate the corresponding alarm signal and send it down to the MCU through the wireless communication module.

[0036] 7. Alarm Function: Respond to the alarm signal forwarded by the MCU and remind the patient to adjust the training posture or intensity.

[0037] Implementation: Select a suitable alarm, such as a sound alarm, a light alarm, etc., to ensure that it can convey the alarm information clearly and accurately. The alarm should be connected to the MCU through an appropriate interface (such as GPIO, etc.) to receive and respond to the alarm signal. The alarm signal generated by the background can be generated according to the signal format supported by the configured alarm.

[0038] The model and material of the electrode patch can be selected by the user and need to be waterproof. Materials that can obstruct or affect the communication signal should not be used to avoid poor quality of the collected EMG signal and affecting the data control and communication between the background and the electrode patch.

[0039] During the data transmission process, the security and integrity of the data should be ensured to prevent data leakage or tampering. An encryption chip can also be deployed in the electrode patch to implement data encryption and decryption between the MCU and the communication module, encrypt the uploaded data, and decrypt and verify the data sent down by the received background. For example: Deploying an encryption chip between the MCU and the communication module is an effective security measure to ensure the confidentiality and integrity of data.

[0040] 1. Select an encryption chip Function: Select an encryption chip that supports data encryption and decryption to ensure its sufficient security and performance.

[0041] Implementation: Consider the encryption algorithms supported by the encryption chip (such as AES, RSA, etc.), encryption speed, power consumption, and compatibility with the MCU and the communication module.

[0042] 2. Integrate the encryption chip Hardware connection: Connect the encryption chip to the MCU and the communication module through appropriate interfaces (such as SPI, I2C, UART, etc.).

[0043] Software configuration: Write or configure the software driver of the encryption chip so that the MCU and the communication module can communicate with it.

[0044] 3. Encrypt the data to be reported generated by the MCU For example, the following encryption and decryption process: The MCU generates the electromyogram value data to be reported.

[0045] The MCU sends the data to be reported to the encryption chip.

[0046] The encryption chip encrypts the data using the preset encryption algorithm and key.

[0047] The encryption chip returns the encrypted data to the MCU.

[0048] The MCU sends the encrypted data to the communication module for reporting.

[0049] 4. Decrypt and verify the background data received by the communication module Process: The communication module receives data from the pelvic floor muscle rehabilitation exercise monitoring background (which may include encrypted instructions or data).

[0050] The communication module sends the received data to the encryption chip.

[0051] The encryption chip decrypts the data using the corresponding decryption algorithm and key.

[0052] The encryption chip returns the decrypted data to the MCU.

[0053] The MCU verifies and processes the decrypted data, such as determining whether it is a valid alarm signal, etc.

[0054] 5. Security management Key Management: Ensure that the keys used in the encryption and decryption processes are secure, and take appropriate measures to protect the keys from leakage.

[0055] Update Mechanism: Provide an update mechanism for the firmware and keys of the encryption chip to address potential security threats.

[0056] Access Control: Strictly control access to the encryption chip to prevent unauthorized access and operations.

[0057] 6. Testing and Verification Function Testing: Ensure that the encryption and decryption functions work properly and that data can be correctly encrypted and decrypted.

[0058] Security Testing: Conduct security testing to verify that the encryption chip and the entire system can withstand common security attacks.

[0059] Performance Testing: Evaluate the impact of the encryption and decryption processes on system performance to ensure that the system can meet real-time and other performance requirements.

[0060] By deploying an encryption chip between the MCU and the communication module, encrypting the data to be reported generated by the MCU, and decrypting and verifying the background data received by the communication module, the security of the system can be significantly improved, protecting the privacy and data security of patients.

[0061] User Experience: The system should have a good user experience, including easy operation, friendly interface, etc.

[0062] Compliance: Ensure that the system meets the requirements of relevant medical device regulations and standards, such as FDA, CE, etc.

[0063] Through the above steps, it is possible to monitor, collect, process, and feedback the patient's pelvic floor muscle EMG signals, thereby helping the patient to perform effective pelvic floor muscle rehabilitation exercises.

[0064] The integration of each electronic hardware, as well as the models and rules, can be selected by the user himself as long as the interactive program function of the present invention is realized. This embodiment does not make any limitations. For example, the wireless communication module can use Bluetooth or a 5G module, and the MCU can use a 32-bit STM single-chip microcomputer, etc.

[0065] Based on the above application, as Figure 3 shown, on the other hand, the present application proposes a pelvic floor muscle rehabilitation exercise monitoring system, including: The above-mentioned pelvic floor muscle rehabilitation exercise monitoring electrode patch (referred to as the electrode patch) for monitoring and uploading the EMG value when the patient performs pelvic floor muscle rehabilitation exercises; The pelvic floor muscle rehabilitation exercise monitoring background (referred to as the background), including: The file management module is used to record the pelvic floor muscle rehabilitation exercise time, frequency of each exercise of the patient, and the training intensity converted based on the myoelectric value of the patient, and write them into the patient's electronic medical record file; The device management module is used to manage the pelvic floor muscle rehabilitation exercise monitoring electrode patches worn by the patient; The processing system is used to provide data processing and task scheduling services, including: sending a test instruction to the pelvic floor muscle rehabilitation exercise monitoring electrode patch worn by the patient, and collecting the corresponding pelvic floor muscle rehabilitation exercise test data; and, reading the pelvic floor muscle clinical examination data of the patient, and inputting the pelvic floor muscle clinical examination data and the pelvic floor muscle rehabilitation exercise test data into a preset pelvic floor muscle rehabilitation exercise strategy AI recommendation model. By using the pelvic floor muscle rehabilitation exercise strategy AI recommendation model to identify the pelvic floor muscle clinical examination symptom characteristics and myoelectric value characteristics of the patient, matching and outputting the corresponding pelvic floor muscle rehabilitation exercise strategy and sending it to the pelvic floor muscle rehabilitation exercise monitoring electrode patch worn by the patient; at the same time, writing the pelvic floor muscle rehabilitation exercise strategy into the patient's electronic medical record file; The medical staff terminal is used to log in to the background and view the patient's electronic medical record file; The pelvic floor muscle rehabilitation exercise monitoring electrode patch and the medical staff terminal are respectively communicatively connected to the pelvic floor muscle rehabilitation exercise monitoring background.

[0066] After the patient wears the electrode patch, the electrode patch can be actively activated to communicate with the background (or the background can send an activation instruction according to the communication ID of the communication module on the electrode patch to activate the communication by the background), and by binding with the corresponding patient ID, the supervision and management of the device and the patient are realized. This process ensures the accuracy of data and the security of patient information, and at the same time facilitates the medical institution to remotely monitor and manage the patient.

[0067] Specifically, when the electrode patch is activated, it will start to collect the patient's physiological signals (such as myoelectric signals (at the same time, electrocardiogram signals can also be collected, because some patients may become tense or excited during pelvic floor muscle rehabilitation exercises. To avoid abnormalities, the electrode patch can collect electrocardiogram signals and monitor the electrocardiogram and heart rate values during the rehabilitation exercise. If the background finds abnormalities, it will immediately issue an alarm and send medical staff to the patient's location) etc.). These signals are then transmitted to the background management system wirelessly or wiredly. After receiving the data, the background system will first analyze and process the data to extract useful physiological information. Then, the system will associate this information with the corresponding patient ID according to the binding relationship between the electrode patch and the patient.

[0068] By binding to the patient ID, the back-end management system can achieve personalized management of patients. For example, the system can record the patient's historical data to form a health record, which is convenient for doctors to conduct long-term tracking and evaluation of the patient's condition. At the same time, the system can also provide timely health reminders or warnings based on the patient's real-time data to help patients detect and handle potential health problems in a timely manner.

[0069] In addition, this binding relationship also helps to ensure the security and privacy of data. Only authorized medical institutions or personnel can access the data associated with a specific patient ID, thus protecting the patient's personal privacy and the security of medical information.

[0070] In summary, after the electrode patch is activated, it communicates with the back-end and binds to the corresponding patient ID, which is an important step in realizing the supervision and management of the device and the patient. It not only improves the efficiency and quality of medical services, but also enhances the security and privacy of patient information.

[0071] The clinical examination data of the pelvic floor muscles can be: The clinical examination data of the pelvic floor muscles mainly include the muscle strength grade, the muscle contraction force value, and the related evaluation criteria.

[0072] 1. Muscle strength grade: The muscle strength of the pelvic floor muscle group can be divided into grades 0-5. Grade 0 indicates basically no contraction ability, which is the weakest state of muscle strength; grade 5 indicates the strongest muscle strength, which is the normal state. Generally speaking, a pelvic floor muscle strength above grade 3 is considered normal, and below grade 3 indicates a decrease in pelvic floor muscle strength, which may require treatment.

[0073] 2. Muscle contraction force value: The pelvic floor muscle test value is usually within a certain range, such as between 50 and 100, which is considered normal. This value reflects the contraction force and endurance of the pelvic floor muscles. Specifically, if the test value is below 50, it may indicate weak pelvic floor muscle strength; if the test value is above 100, it may indicate excessive muscle tension and requires appropriate relaxation and adjustment.

[0074] The evaluation of the pelvic floor muscles also includes the evaluation of aspects such as muscle tension, reaction speed, and stability of contraction control. For example, in the pre-resting stage, if the test average value is greater than a certain reference value, it indicates an increase in pelvic floor muscle tension, which may lead to a series of symptoms such as pain and urinary retention.

[0075] The muscle strength of fast-twitch and slow-twitch muscles is also the focus of evaluation. Fast-twitch muscles are mainly responsible for controlling urination and defecation. If the muscle strength is insufficient, symptoms such as stress urinary incontinence may occur; slow-twitch muscles mainly play a role in supporting internal organs. If the muscle strength is insufficient, problems such as visceral prolapse are likely to occur.

[0076] The monitoring background for pelvic floor muscle rehabilitation exercise of the present invention can refer to the existing nurse station background server, on which an archive management module is deployed. The archive management module can be understood in combination with the HIS system, which can be used to manage the electronic medical record archives of patients, record the information of each pelvic floor muscle rehabilitation exercise of patients, including exercise time, exercise frequency, and the myoelectric values monitored and reported through the electrode patches worn by the patients, and can convert the corresponding training intensity according to the conversion relationship between the preset myoelectric values and the training intensity. Subsequently, medical staff can log in to the background through, for example, a PDA terminal to view the archives, and at the same time, can issue corresponding pelvic floor muscle rehabilitation exercise orders and corresponding medication orders to the corresponding patients through the terminal, etc.

[0077] Issued by the background and displayed on the electronic display of its electrode patch.

[0078] The device management module of the background is mainly used to manage each electrode patch. After each electrode patch is activated, the background can receive its signal and display the working status of the electrode patch worn by the patient. At the same time, it can also perform asset background management on the electrode patches in the hospital to avoid the loss of electrode patches.

[0079] The processing system mainly consists of a background processor such as a CPU system for data processing and task scheduling. The processing functions include: after the electrode patch is activated, according to the preset test instructions, sending test instructions to the electrode patch worn by the corresponding patient to sample test data of the patient. At the same time, it can also read the pelvic floor muscle clinical examination data of the patient from the electronic medical record file (the clinical examination data can be uploaded by the corresponding examination department after examining the patient's pelvic floor muscles) and write it into the electronic medical record file. After sampling the patient, the pelvic floor muscle clinical examination data and pelvic floor muscle rehabilitation exercise test data of the patient can be input into the AI model (pelvic floor muscle rehabilitation exercise strategy AI recommendation model) pre-deployed in the background. The AI model identifies the symptom characteristics and electromyogram value characteristics of the patient's pelvic floor muscles. The clinical examination symptom characteristics are the corresponding symptom data characteristics of the patient's pelvic floor muscles, such as the evaluation score of the pelvic floor muscles, muscle tension, contraction control intensity, body level and other characteristics. The electromyogram value characteristics are mainly to collect the current electromyogram value of the patient through testing. By combining the clinical symptom characteristics of the patient's pelvic floor muscles and the electromyogram level reflected by the current electromyogram value of the sampling test, the AI model predicts and outputs a pelvic floor muscle rehabilitation exercise strategy that matches the current patient's pelvic floor muscle clinical symptom characteristics and electromyogram value characteristics. This strategy includes the corresponding working mode of the electrode patch (the working parameters of the corresponding electrode patch in this strategy). It matches the current patient's symptom characteristics and electromyogram value characteristics, so it can recommend an exercise strategy that matches the current symptoms and electromyogram value characteristics to the patient, so as to guide the patient to carry out rehabilitation training according to the doctor's advice in the background. Let the patient follow the doctor's advice strategy recommended by the background AI model for pelvic floor muscle rehabilitation exercise, avoid blind exercise, and thus improve the quality of the patient's pelvic floor muscle rehabilitation exercise.

[0080] As an optional implementation solution of this application, optionally, the processing system is further configured to: Judge whether the electromyogram value of the patient meets the preset training intensity: If it does not meet, generate a corresponding alarm signal, retrieve the nursing education and evaluation information corresponding to the preset training intensity from the education database, and send it to the pelvic floor muscle rehabilitation exercise monitoring electrode patch worn by the patient; If it meets, retrieve the nursing education and evaluation information corresponding to the training intensity from the education database, and send it to the pelvic floor muscle rehabilitation exercise monitoring electrode patch worn by the patient.

[0081] The preset training intensity that the electromyogram values of different patients should meet can be written by the administrator in the file in advance with its intensity range for judgment. If the training intensity is insufficient, an alarm is issued to remind the patient to increase the training intensity, and at the same time, the education data corresponding to the standard intensity is sent and played / displayed, such as reminding the patient what the training intensity should reach. If the training intensity exceeds the standard, an alarm can also be issued and the same education can be carried out.

[0082] If the training intensity matches the threshold, encouraging education data is sent down.

[0083] The nursing education and evaluation information corresponding to specific different intensities can be set by the administrator.

[0084] The present invention uses an AI algorithm to jointly predict and recommend a strategy that matches the current patient status based on the clinical examination data of the patient's pelvic floor muscles and the current electromyogram test data.

[0085] As an alternative implementation of the present application, optionally, the pelvic floor muscle rehabilitation exercise monitoring background further includes: An education database for storing a number of the nursing education and evaluation information corresponding to different training intensities and the pelvic floor muscle rehabilitation exercise strategies.

[0086] As Figure 4 shown, as an alternative implementation of the present application, optionally, the method for generating the pelvic floor muscle rehabilitation exercise strategy AI recommendation model includes: Collect the corresponding big data on the history of pelvic floor muscle rehabilitation exercises from the file management modules of a number of patients, including: the clinical history examination data of the patients' pelvic floor muscles, the monitoring data of the pelvic floor muscle rehabilitation exercises, and the corresponding pelvic floor muscle rehabilitation exercise strategies; Perform feature engineering on the big data on the history of pelvic floor muscle rehabilitation exercises to extract the corresponding recommended features for pelvic floor muscle rehabilitation exercises, including: extracting the clinical examination symptom features of the patients' pelvic floor muscles from the clinical history examination data of the pelvic floor muscles, extracting the electromyogram value features of the patients from the monitoring data of the pelvic floor muscle rehabilitation exercises, and extracting the corresponding strategy features from the pelvic floor muscle rehabilitation exercise strategies, that is: Recommended features for pelvic floor muscle rehabilitation exercises = {clinical examination symptom features of pelvic floor muscles, electromyogram value features, strategy features}; Statistically analyze the recommended features for pelvic floor muscle rehabilitation exercises of each patient and form them into a feature set; Divide the feature set into a training set and a validation set according to a preset ratio; Input the training set into a preset random forest model to enable the model to train and learn different associated features, and generate the pelvic floor muscle rehabilitation exercise strategy AI recommendation model; Use the validation set to verify the recommendation performance of the pelvic floor muscle rehabilitation exercise strategy AI recommendation model: If the verification is passed, deploy the pelvic floor muscle rehabilitation exercise strategy AI recommendation model on the pelvic floor muscle rehabilitation exercise monitoring background; If the verification fails, repeat the above steps to reconstruct the pelvic floor muscle rehabilitation exercise strategy AI recommendation model.

[0087] The structure and principle of the random forest model are not described in this embodiment.

[0088] The model training can be implemented according to the following steps: 1. Data collection Obtain data from the file management module: Extract the clinical history examination data of the pelvic floor muscles of all patients, including but not limited to muscle strength level, muscle contraction force value, symptom description, etc.

[0089] Collect the monitoring data of pelvic floor muscle rehabilitation exercises, such as the electromyogram (EMG) records, exercise duration, exercise frequency, etc. of the patients.

[0090] Sort out the corresponding pelvic floor muscle rehabilitation exercise strategies, including the exercise plans, exercise methods, exercise intensities, etc. formulated by doctors.

[0091] The corresponding historical big data can be extracted from the files. Here, the corresponding data retrieval keywords and retrieval logics can also be constructed, the combined prompt words are input into the preset large language model LLM (invoked in the background or developed and deployed by oneself), and the LLM large language model is used to check and collect the corresponding clinical history examination data of the pelvic floor muscles, so as to improve the model training efficiency. For example: In order to construct effective data retrieval keywords and retrieval logics so that the large language model LLM (Large Language Model) can accurately check and collect the clinical history examination data of the pelvic floor muscles, the following steps can be carried out: 1). Determine the data retrieval target First, clarify from which sources (such as electronic medical record systems, patient file management systems, rehabilitation exercise monitoring devices, etc.) to retrieve the clinical history examination data of the pelvic floor muscles. These data may include the basic information of the patients, pelvic floor muscle examination records, electromyogram measurement data, etc.

[0092] 2). Construct retrieval keywords According to the data retrieval target, the following retrieval keywords can be constructed: ‌Basic information keywords‌: Patient ID, name, gender, age, visit date, etc.

[0093] ‌Pelvic floor muscle examination keywords‌: Pelvic floor muscle examination, muscle strength level, muscle contraction force, pelvic floor muscle function assessment, pelvic floor muscle symptoms, etc.

[0094] ‌Electromyogram measurement keywords‌: Electromyogram (EMG), electromyogram, muscle activity potential, electromyogram signal, etc.

[0095] 3). Design the retrieval logic Next, it is necessary to design retrieval logic so that the large language model (LLM) can retrieve data according to this logic. The retrieval logic can include the following steps: Locate the data source: First, determine from which data sources to retrieve data.

[0096] Filtering conditions: Filter specific records based on the patient's basic information (such as patient ID, name, etc.).

[0097] Keyword matching: Among the filtered records, use keywords related to pelvic floor muscle examination and electromyogram value measurement for matching to find records containing these keywords.

[0098] Data extraction: Extract the required data fields from the matched records, such as muscle strength grade, muscle contraction force value, electromyogram value, etc.

[0099] 4). Combine the prompt words and input them into the large language model (LLM) Combine the above retrieval logic and keywords into prompt words and input them into the large language model (LLM). The prompt words can be in a similar form as follows: Please retrieve all records containing the following keywords from [data source name]: - Patient ID: [specific patient ID] - Keywords related to pelvic floor muscle examination: Pelvic floor muscle examination, muscle strength grade, muscle contraction force - Keywords related to electromyogram value measurement: Electromyogram value (EMG), electromyogram For each matched record, please extract the following information: - Patient's basic information: Name, gender, age, date of visit - Pelvic floor muscle examination data: Muscle strength grade, muscle contraction force value - Electromyogram value data: Electromyogram value (EMG) numerical value Return the extracted data in [specific format].

[0100] 5). Verification and correction Initial verification: After the large language model (LLM) returns the data, conduct initial verification to check whether the data is complete and accurate.

[0101] Iterative retrieval: According to the need, the retrieval process can be iterated multiple times until the required data is obtained.

[0102] Through the above steps, the large language model (LLM) can be used to effectively check and collect clinical historical examination data of the pelvic floor muscles, providing a basis for subsequent data analysis and model training.

[0103] 2. Feature engineering Extract recommended features for pelvic floor muscle rehabilitation exercises: Clinical symptom features of pelvic floor muscles: Extract key symptom indicators from clinical examination data, such as the degree of muscle strength decline, pain level, urinary incontinence situation, etc.

[0104] Electromyogram value features: Extract statistical features such as the mean, standard deviation, maximum value, and minimum value of electromyogram values from monitoring data, as well as the change trend of electromyogram values over time.

[0105] Strategy features: Extract strategy parameters such as exercise type, exercise duration, exercise intensity, rest interval, etc. from rehabilitation exercise strategies.

[0106] The feature extraction method can be: Statistical feature extraction: Such as histogram feature extraction, which extracts features by statistically analyzing the data distribution.

[0107] SIFT (Scale-Invariant Feature Transform): Find key points in different scale spaces and calculate the directions of the key points, which is used in the field of image processing.

[0108] Specifically selected by the administrator.

[0109] 3. Build a feature set Integrate the recommended features for pelvic floor muscle rehabilitation exercises of each patient into a feature vector.

[0110] Combine the feature vectors of all patients into a feature set.

[0111] 4. Data partitioning Partition the feature set into a training set and a validation set according to a preset ratio (such as 70% training set and 30% validation set).

[0112] 5. Model training Input the training set into the random forest model: Use the feature vectors in the training set and the corresponding rehabilitation exercise strategies as input and output to train the random forest model.

[0113] Adjust the parameters of the random forest, such as the number of trees, maximum depth, etc., to optimize the model performance.

[0114] 6. Model validation Validate the model performance using the validation set: Input the feature vectors in the validation set into the trained model to predict the rehabilitation exercise strategies.

[0115] Compare with the actual rehabilitation exercise strategies and evaluate performance indicators such as the prediction accuracy, recall rate, and F1 score of the model.

[0116] 7. Deployment and iteration Decide on model deployment or iteration based on the verification results: If the model performance meets the requirements (e.g., the accuracy is higher than a certain threshold), then deploy the model to the pelvic floor muscle rehabilitation exercise monitoring background for actual recommendation of rehabilitation exercise strategies.

[0117] If the model performance does not meet the requirements, then go back to the data collection or feature engineering stage, re-collect data, extract features or adjust model parameters, and then repeat the above steps until a model with satisfactory performance is constructed.

[0118] 8. Continuous Optimization and Monitoring Continuously optimize and monitor after the model goes live: Regularly collect new data for updating the model or verifying the model's stability.

[0119] Monitor the model's performance metrics, such as accuracy, response time, etc., and promptly discover and solve problems.

[0120] According to user feedback and actual needs, continuously adjust and optimize the model to improve the user experience and rehabilitation effect.

[0121] Through the above steps, an AI recommendation model for pelvic floor muscle rehabilitation exercise strategies based on random forest can be constructed. This model can recommend personalized rehabilitation exercise strategies for patients based on the patients' pelvic floor muscle clinical examination data, rehabilitation exercise monitoring data, and historical data of rehabilitation exercise strategies.

[0122] Therefore, subsequently, the model file can be loaded and deployed on the background to perform matching recommendations of pelvic floor muscle rehabilitation exercise strategies for patients.

[0123] Obviously, those skilled in the art should understand that to implement all or part of the processes in the above embodiments, it can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above control embodiments. Those skilled in the art can understand that to implement all or part of the processes in the above embodiments, it can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above control embodiments. Among them, the storage medium can be a magnetic disk, an optical disc, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memories. Example 2

[0124] Based on the implementation principle of Example 1, on the other hand, this application proposes a method for monitoring pelvic floor muscle rehabilitation exercises, which is implemented based on the above-mentioned system, and includes: The patient wears and activates the pelvic floor muscle rehabilitation exercise monitoring electrode patch and requests communication with the pelvic floor muscle rehabilitation exercise monitoring background; The pelvic floor muscle rehabilitation exercise monitoring background responds to the request, establishes a data communication channel with the pelvic floor muscle rehabilitation exercise monitoring electrode patch worn by the patient, and binds the pelvic floor muscle rehabilitation exercise monitoring electrode patch to the patient's visit ID; The processing system sends a preset test instruction to the pelvic floor muscle rehabilitation exercise monitoring electrode patch worn by the patient, and the pelvic floor muscle rehabilitation exercise monitoring electrode patch worn by the patient responds to the test instruction, collects and reports the corresponding pelvic floor muscle rehabilitation exercise test data; The processing system reads the patient's pelvic floor muscle clinical examination data from the patient's electronic medical record file, inputs the pelvic floor muscle clinical examination data and the pelvic floor muscle rehabilitation exercise test data into a preset pelvic floor muscle rehabilitation exercise strategy AI recommendation model, identifies the pelvic floor muscle clinical examination symptom characteristics and electromyogram value characteristics of the patient through the pelvic floor muscle rehabilitation exercise strategy AI recommendation model, matches and outputs a pelvic floor muscle rehabilitation exercise strategy corresponding to the pelvic floor muscle clinical examination symptom characteristics and electromyogram value characteristics of the patient and sends it to the pelvic floor muscle rehabilitation exercise monitoring electrode patch worn by the patient; at the same time, writes the pelvic floor muscle rehabilitation exercise strategy into the patient's electronic medical record file; The pelvic floor muscle rehabilitation exercise monitoring electrode patch worn by the patient receives and analyzes the pelvic floor muscle rehabilitation exercise strategy, obtains the electrode patch control instruction therein, and the MCU executes the instruction to realize the working control of the electrode patch; The pelvic floor muscle rehabilitation exercise monitoring electrode patch monitors and uploads the electromyogram value of the patient during pelvic floor muscle rehabilitation exercises to the pelvic floor muscle rehabilitation exercise monitoring background; The pelvic floor muscle rehabilitation exercise monitoring background records the pelvic floor muscle rehabilitation exercise time, number of times of each exercise of the patient and the training intensity calculated based on the electromyogram value of the patient and writes it into the patient's electronic medical record file; and judges whether the electromyogram value of the patient meets the preset training intensity: If not, generate a corresponding alarm signal, retrieve the nursing education and evaluation information corresponding to the preset training intensity from the education database, and send it to the pelvic floor muscle rehabilitation exercise monitoring electrode patch worn by the patient; If it meets the requirements, retrieve the nursing education and evaluation information corresponding to the training intensity from the education database, and send it to the pelvic floor muscle rehabilitation exercise monitoring electrode patch worn by the patient; Medical staff log in to the background through the medical staff terminal and view the patient's electronic medical record file.

[0125] For each step in the above method, please understand and implement it in combination with the corresponding process in Embodiment 1, and this embodiment will not be elaborated here.

[0126] Each module or step of the present invention described above can be implemented by a general-purpose computing system. They can be concentrated on a single computing system or distributed on a network composed of multiple computing systems. Optionally, they can be implemented by program code executable by the computing system. Thus, they can be stored in the storage system for execution by the computing system, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module for implementation. In this way, the present invention is not limited to any specific combination of hardware and software. Embodiment 3

[0127] As Figure 5 shown, further, on the other hand, the present application also proposes an electronic device, including: A processor; A memory for storing instructions executable by the processor; Wherein, when the processor is configured to execute the executable instructions, it implements the monitoring method described in Embodiment 2.

[0128] The electronic device of the embodiments of the present disclosure includes a processor and a memory for storing instructions executable by the processor. Wherein, when the processor is configured to execute the executable instructions, it implements the monitoring method described in Embodiment 2 above.

[0129] Here, it should be noted that the number of processors can be one or more. At the same time, in the electronic device of the embodiments of the present disclosure, an input system and an output system can also be included. Wherein, the processor, the memory, the input system, and the output system can be connected through a bus or in other ways, and specific limitations are not provided here.

[0130] As a computer-readable storage medium, the memory can be used to store software programs, computer-executable programs, and various modules, such as: the programs or modules corresponding to the monitoring method of the embodiments of the present disclosure. The processor executes various functional applications and data processing of the electronic device by running the software programs or modules stored in the memory.

[0131] The input system can be used to receive input numbers or signals. Wherein, the signal can be a key signal related to the user settings and function control of the device / terminal / server. The output system can include a display device such as a display screen.

[0132] The embodiments of the present disclosure have been described above. The above description is exemplary and not exhaustive, and is also not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, the practical application, or the improvement of the technology in the market, or to enable other ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A pelvic floor muscle rehabilitation training monitoring electrode sheet, characterized in that: include: A number of electrodes for monitoring and collecting electromyographic signals of the patient's pelvic floor muscles and feeding them back to an A / D converter; An A / D converter is used to perform digital-to-analog conversion on the electromyographic signal, generate a corresponding electrical signal and send it to the MCU; MCU, used for controlling the sampling of the electrode sheet and generating corresponding electromyographic values ​​according to the electrical signal, and sending the electromyographic values ​​to the display and the wireless communication module respectively; A display, used for displaying the electromyographic value in real time; A wireless communication module, used for reporting the electromyographic value to the pelvic floor muscle rehabilitation training monitoring background; If the pelvic floor muscle rehabilitation training monitoring background finds that the patient's electromyographic value does not meet the preset training intensity, an alarm signal corresponding to the training intensity is issued and forwarded by the wireless communication module to the MCU, which is processed by the MCU and then forwarded to the alarm; An alarm, used to respond to the alarm signal and remind the patient to adjust the training posture or strength; Power supply, used for power supply; The electrode sheet is electrically connected to the A / D converter; The A / D converter, display, wireless communication module, alarm and power supply are electrically connected to the MCU respectively.

2. A pelvic floor muscle rehabilitation training monitoring system, characterized in that: include: The pelvic floor muscle rehabilitation training monitoring electrode sheet according to claim 1 is used to monitor and upload the electromyographic value of the patient when performing pelvic floor muscle rehabilitation training; Pelvic floor muscle rehabilitation training monitoring background, including: The file management module is used to record the time, number of pelvic floor muscle rehabilitation exercises of each patient and the training intensity converted based on the patient's electromyographic value and write them into the patient's electronic medical record file; A device management module, used for managing the pelvic floor muscle rehabilitation exercise monitoring electrode sheet worn by the patient; A processing system for providing data processing and task scheduling services, including: issuing a test instruction to the pelvic floor muscle rehabilitation exercise monitoring electrode sheet worn by the patient, collecting corresponding pelvic floor muscle rehabilitation exercise test data; and reading the patient's pelvic floor muscle clinical examination data, and inputting the pelvic floor muscle clinical examination data and the pelvic floor muscle rehabilitation exercise test data into a preset pelvic floor muscle rehabilitation exercise strategy AI recommendation model, identifying the patient's pelvic floor muscle clinical examination symptom characteristics and electromyographic value characteristics through the pelvic floor muscle rehabilitation exercise strategy AI recommendation model, matching and outputting the corresponding pelvic floor muscle rehabilitation exercise strategy and issuing it to the pelvic floor muscle rehabilitation exercise monitoring electrode sheet worn by the patient; and writing the pelvic floor muscle rehabilitation exercise strategy into the patient's electronic medical record file; The medical terminal is used to log in to the backend and view the patient's electronic medical records; The pelvic floor muscle rehabilitation training monitoring electrode sheet and the medical terminal are respectively connected to the pelvic floor muscle rehabilitation training monitoring background for communication.

3. The pelvic floor muscle rehabilitation training monitoring system according to claim 2, characterized in that: The processing system is also used for: Determine whether the patient's electromyographic value meets the preset training intensity: If it does not meet the requirements, a corresponding alarm signal is generated, and nursing education and evaluation information corresponding to the preset training intensity is retrieved from the education database, and sent to the pelvic floor muscle rehabilitation training monitoring electrode sheet worn by the patient; If it meets the requirements, the nursing education and evaluation information of the corresponding training intensity is retrieved from the education database and sent to the pelvic floor muscle rehabilitation exercise monitoring electrode worn by the patient.

4. The pelvic floor muscle rehabilitation training monitoring system according to claim 3, characterized in that: The pelvic floor muscle rehabilitation training monitoring background also includes: The education database is used to store a plurality of nursing education and evaluation information corresponding to different training intensities and the pelvic floor muscle rehabilitation training strategies.

5. The pelvic floor muscle rehabilitation training monitoring system according to claim 2, characterized in that: The method for generating the AI ​​recommendation model for pelvic floor muscle rehabilitation exercise strategy includes: Collect corresponding pelvic floor muscle rehabilitation exercise history big data from the file management modules of several patients, including: the patient's pelvic floor muscle clinical history examination data, pelvic floor muscle rehabilitation exercise monitoring data and corresponding pelvic floor muscle rehabilitation exercise strategies; Feature engineering is performed on the pelvic floor muscle rehabilitation exercise history big data to extract corresponding pelvic floor muscle rehabilitation exercise recommendation features, including: extracting the patient's pelvic floor muscle clinical examination symptom features from the pelvic floor muscle clinical history examination data, extracting the patient's electromyographic value features from the pelvic floor muscle rehabilitation exercise monitoring data, and extracting corresponding strategy features from the pelvic floor muscle rehabilitation exercise strategy, that is: Recommended characteristics of pelvic floor muscle rehabilitation exercises = {symptom characteristics of pelvic floor muscle clinical examination, characteristics of electromyography, and characteristics of strategies}; Counting the recommended features of the pelvic floor muscle rehabilitation exercises for each patient and forming a feature set; Dividing the feature set into a training set and a validation set according to a preset ratio; Inputting the training set into a preset random forest model, allowing the model to train and learn different associated features, and generating the pelvic floor muscle rehabilitation exercise strategy AI recommendation model; The validation set is used to verify the recommendation performance of the pelvic floor muscle rehabilitation exercise strategy AI recommendation model: If the verification is successful, the pelvic floor muscle rehabilitation exercise strategy AI recommendation model is deployed on the pelvic floor muscle rehabilitation exercise monitoring background; If the verification fails, repeat the above steps to rebuild the AI ​​recommendation model for the pelvic floor muscle rehabilitation exercise strategy.

6. A pelvic floor muscle rehabilitation training monitoring method, implemented based on the system according to any one of claims 2 to 5, characterized in that: include: The patient wears and activates the pelvic floor muscle rehabilitation training monitoring electrodes, and requests communication with the pelvic floor muscle rehabilitation training monitoring background; The pelvic floor muscle rehabilitation training monitoring background responds to the request, establishes a data communication channel with the pelvic floor muscle rehabilitation training monitoring electrode sheet worn by the patient, and binds the pelvic floor muscle rehabilitation training monitoring electrode sheet to the patient's medical ID; The processing system sends a preset test instruction to the pelvic floor muscle rehabilitation exercise monitoring electrode sheet worn by the patient, and the pelvic floor muscle rehabilitation exercise monitoring electrode sheet worn by the patient responds to the test instruction, collects and reports corresponding pelvic floor muscle rehabilitation exercise test data; The processing system reads the patient's pelvic floor muscle clinical examination data from the patient's electronic medical record file, and inputs the pelvic floor muscle clinical examination data and the pelvic floor muscle rehabilitation exercise test data into a preset pelvic floor muscle rehabilitation exercise strategy AI recommendation model, identifies the patient's pelvic floor muscle clinical examination symptom characteristics and electromyographic value characteristics through the pelvic floor muscle rehabilitation exercise strategy AI recommendation model, matches and outputs a pelvic floor muscle rehabilitation exercise strategy corresponding to the patient's pelvic floor muscle clinical examination symptom characteristics and electromyographic value characteristics, and sends it to the pelvic floor muscle rehabilitation exercise monitoring electrode sheet worn by the patient; and at the same time, writes the pelvic floor muscle rehabilitation exercise strategy into the patient's electronic medical record file; The pelvic floor muscle rehabilitation training monitoring electrode sheet worn by the patient receives and analyzes the pelvic floor muscle rehabilitation training strategy, obtains the electrode sheet control instruction therein, and the MCU executes the instruction to realize the working control of the electrode sheet; The pelvic floor muscle rehabilitation training monitoring electrode sheet monitors and uploads the electromyographic value of the patient when performing pelvic floor muscle rehabilitation training to the pelvic floor muscle rehabilitation training monitoring background; The pelvic floor muscle rehabilitation training monitoring background records the pelvic floor muscle rehabilitation training time, number of times and training intensity converted based on the patient's electromyographic value of each exercise and writes them into the patient's electronic medical record file; and determines whether the patient's electromyographic value meets the preset training intensity: If it does not meet the requirements, a corresponding alarm signal is generated, and nursing education and evaluation information corresponding to the preset training intensity is retrieved from the education database, and sent to the pelvic floor muscle rehabilitation training monitoring electrode sheet worn by the patient; If it is in compliance, the nursing education and evaluation information of the corresponding training intensity is retrieved from the education database and sent to the pelvic floor muscle rehabilitation training monitoring electrode sheet worn by the patient; Medical staff log in to the backend through the medical terminal and view the patient's electronic medical record file.

7. An electronic device, characterized in that include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to implement a pelvic floor muscle rehabilitation exercise monitoring electrode sheet, monitoring system and method according to any one of claims 1 to 8 when executing the executable instructions.

Images