Monitoring system for pelvic floor muscle training

Abstract

The invention relates to the field of medical health, in particular to a monitoring system for pelvic floor muscle training, which comprises a data acquisition unit, a data processing unit, a data analysis unit and a personalized training plan generation unit. If the analysis result shows that the training intensity of the user is lower than the training intensity in the personalized training plan, the training intensity of the user is judged to be insufficient, so that the user is reminded to increase the training intensity; if the analysis result shows that the training intensity of the user is not lower than the training intensity in the personalized training plan, it is judged that the preset training target is too large, and therefore the training target is adjusted. The pelvic floor muscle training system and method are used for comprehensively monitoring and analyzing various data of the user in the pelvic floor muscle training process in real time, personalized training guidance and health management schemes are provided for the user, and the pelvic floor muscle training effect is improved.

Description

Technical Field

[0001] The present invention relates to the field of medical health, and in particular to a monitoring system for pelvic floor muscle training. Background Art

[0002] The pelvic floor muscles are one of the important muscle groups in the human body. They play a vital role in maintaining the normal physiological functions of the human body, such as controlling urination, defecation, and maintaining the normal position of sexual organs. With the acceleration of the pace of modern life and the increase of bad living habits, pelvic floor muscle problems are becoming increasingly common, such as pelvic floor muscle relaxation and pelvic floor dysfunction. These problems not only affect the quality of life of individuals, but may also cause a series of health problems. In order to improve the health of the pelvic floor muscles, many people choose to do pelvic floor muscle training.

[0003] However, traditional pelvic floor muscle training often relies on the patient's subjective feelings and the doctor's experience and guidance, and lacks objective and accurate evaluation methods. At the same time, due to the different physical conditions and training needs of each person, traditional training methods often fail to achieve personalized results. In addition, during the training process, patients may not be able to understand their training status and progress in a timely manner, making it difficult to adjust the training intensity and plan, which affects the training effect.

[0004] In recent years, with the rapid development of sensor technology and data analysis technology, people have begun to try to apply these technologies to pelvic floor muscle training to achieve intelligent and personalized training. However, existing pelvic floor muscle training monitoring systems often have problems such as single function, limited data processing capabilities, untimely feedback, and poor user experience, which cannot meet patients' needs for personalized and intelligent training.

[0005] In response to the above problems, the present invention proposes a monitoring system for pelvic floor muscle training, which aims to achieve comprehensive monitoring and personalized guidance of the pelvic floor muscle training process by comprehensively collecting and analyzing data during the pelvic floor muscle training process, thereby helping trainees improve training effects and improve the health of the pelvic floor muscles. Summary of the invention

[0006] To solve the above problems, the present invention provides a monitoring system for pelvic floor muscle training, which is used to comprehensively and in real time monitor and analyze various data of users during pelvic floor muscle training, and provide users with personalized training guidance and health management plans to improve the effectiveness and safety of pelvic floor muscle training.

[0007] In order to achieve the above object, the technical solution of the present invention is as follows: A monitoring system for pelvic floor muscle training, comprising:

[0008] A data collection unit, used to collect real-time data of pelvic floor muscle contraction and relaxation during pelvic floor muscle training, as well as the user's personal information and training history data;

[0009] A data processing unit, used for preprocessing the raw data collected by the data collection unit, the preprocessing including data cleaning, denoising, formatting and feature extraction;

[0010] A data analysis unit, used to perform real-time analysis on the data processed by the data processing unit, identify the contraction and relaxation patterns of the pelvic floor muscles, calculate relevant parameters, and provide instant feedback and guidance to the user based on the analysis results;

[0011] A personalized training plan generation unit, which is used to build a detailed user profile based on the personal information collected by the data collection unit, the training history data and the feedback data of the data analysis unit, and to generate a personalized training plan based on the user profile and the training goal. At the same time, the personalized training plan generation unit is also used to make dynamic adjustments based on the user's training progress and feedback;

[0012] When the user's current training status collected by the data collection unit is inconsistent with the preset training target, the data analysis unit analyzes the cause of the inconsistency:

[0013] If the analysis result shows that the user's training intensity is lower than the training intensity in the personalized training plan, the inconsistency is judged to be caused by insufficient training intensity of the user, and the user is reminded to increase the training intensity;

[0014] If the analysis results show that the user's training intensity is not lower than the training intensity in the personalized training plan, but still cannot achieve the preset training goal, it is judged that the cause of the inconsistency is that the preset training goal is too large, so the personalized training plan generation unit will automatically trigger the adjustment mechanism to adjust the training goal.

[0015] Furthermore, the data acquisition unit includes a sensor component, a personal information access module and a training history data recording module; the sensor component includes a pressure sensor, an electromyography sensor, an acceleration sensor, a displacement sensor and a temperature sensor, the pressure sensor is used to detect the pressure change generated when the pelvic floor muscles contract; the electromyography sensor is used to capture the electrical signal generated when the pelvic floor muscles are active; the acceleration sensor and the displacement sensor are used to monitor the tiny movements of the pelvic floor muscles; the temperature sensor is used to detect the temperature change of the pelvic floor area and evaluate the muscle fatigue or inflammation state;

[0016] The personal information access module is used for users to input personal information, including age, gender and health status;

[0017] The training history data recording module is used to record the user's past training data, which includes training time, training intensity and training effect.

[0018] Furthermore, the relevant parameters calculated in the data analysis unit include: the contraction force used to indicate the strength of the pelvic floor muscles during contraction, the duration of the contraction or relaxation of the pelvic floor muscles, and the frequency of contraction and relaxation of the pelvic floor muscles per unit time.

[0019] Furthermore, the personalized training plan includes: training goals set according to the user's actual needs and health status, training intensity and time schedule formulated according to the user's physical condition and training progress, training movement instructions and demonstration guidance, and a mechanism for tracking the user's training progress in real time and providing personalized training progress tracking and feedback.

[0020] Furthermore, it also includes a visual feedback unit for providing a visual feedback report on the smart terminal. The visual feedback report includes but is not limited to: a chart for intuitively displaying the user's training progress and effect, a curve for reflecting the changing trend of the user's pelvic floor muscle activity, and an animation demonstration for simulating the pelvic floor muscle contraction and relaxation process.

[0021] Furthermore, it also includes an intelligent reminder unit, which is used to send different reminders to the user according to the user's personalized training plan and the real-time data collected by the data collection module;

[0022] If the user has not started training, the smart reminder unit will send a reminder to the user to start training at the scheduled training time;

[0023] If the user has already started training, the smart reminder unit will send training process reminders and end reminders to the user based on the training progress and real-time data:

[0024] During training, the smart reminder unit will send reminders to users based on their training status and data to guide them to adjust their training intensity, posture or breathing method.

[0025] When the scheduled training time is reached, the intelligent reminder unit will send a reminder to the user to end the training to inform the user that the training has been completed. At the same time, it will also provide the user with a training summary based on the user's training data and feedback.

[0026] Furthermore, it also includes a motion recognition unit, which is used to determine whether the user's current activity state is daily activity or pelvic floor muscle training through data from the sensor component;

[0027] When the data collected by the acceleration sensor and the displacement sensor show a regular movement pattern, and the movement pattern matches the pelvic floor muscle training action, and the data collected by the pressure sensor also shows corresponding pressure changes, it is determined that the user is currently in the training state;

[0028] When the data collected by the acceleration sensor and the displacement sensor show irregular movement changes, and the data collected by the pressure sensor does not show pressure changes related to pelvic floor muscle training, it is determined that the user is currently in a daily activity state;

[0029] When the user is in the training state, the data acquisition unit will start all sensors in the sensor assembly to perform data collection and analysis processes;

[0030] When the user is engaged in daily activities, the data collection unit will reduce unnecessary data processing.

[0031] Furthermore, it also includes a user health status evaluation unit, which is used to evaluate the user's pelvic floor health status and training effect, calculate a comprehensive health index based on the real-time data collected by the sensor component and the user's personal information and training history, classify the user's pelvic floor health status into different levels according to the calculated comprehensive health index, compare the user's comprehensive health index before and after training, and evaluate the effectiveness of the training plan;

[0032] If the user's health status level is improved, continue with the current plan;

[0033] If the level of the user's health status remains unchanged, the personalized training plan generating unit adjusts the training plan according to the evaluation result.

[0034] Furthermore, it also includes a remote monitoring and guidance unit, which is used to allow medical personnel or trainers to remotely monitor the user's training status in real time, including parameters such as the contraction strength, duration and frequency of the pelvic floor muscles, as well as the user's training progress and feedback. Based on the monitoring results, the medical personnel or trainers can provide the user with real-time training guidance or suggestions.

[0035] Furthermore, it also includes a data security and privacy protection unit, which is used to use encryption technology to protect all collected, processed, stored and transmitted user data and perform access control.

[0036] The technical principle of the above scheme is as follows: various data in the pelvic floor muscle training process are collected in real time through sensor components (including pressure sensors, electromyography sensors, acceleration sensors, displacement sensors and temperature sensors), such as pressure changes, electrical signals, micro-movements, temperature changes, etc. At the same time, the personal information access module and the training history data recording module collect the user's personal information and past training data respectively.

[0037] The collected raw data is preprocessed, including data cleaning, denoising, formatting, and feature extraction, to obtain more accurate and useful data. Then, the data analysis unit performs real-time analysis on the processed data, identifies the contraction and relaxation patterns of the pelvic floor muscles, calculates relevant parameters, and provides users with immediate feedback and guidance based on the analysis results.

[0038] Based on the user's personal information, training history data and feedback data from the data analysis unit, a detailed user profile is constructed. Then, a personalized training plan is generated based on the user profile and training goals, and dynamically adjusted based on the user's training progress and feedback.

[0039] Through the visual feedback unit and intelligent reminder unit, users are provided with intuitive training progress display, training effect evaluation and personalized training suggestions. At the same time, the action recognition unit can determine the user's current activity status, thereby starting or closing the data collection and analysis process.

[0040] The above scheme has the following beneficial effects:

[0041] 1. This solution comprehensively collects real-time data during pelvic floor muscle training, as well as the user's personal information and training history data through the data acquisition unit, providing a rich data basis for subsequent personalized training. Compared with the simple data collection in the prior art, this solution can more accurately reflect the user's training status and progress, thereby providing users with more accurate training guidance.

[0042] 2. This solution pre-processes and analyzes the raw data in real time through the data processing and data analysis unit, which can accurately identify the contraction and relaxation patterns of the pelvic floor muscles, calculate relevant parameters, and provide users with immediate feedback and guidance. This helps users to adjust the training intensity and posture in time and improve the training effect. Compared with manual analysis or simple feedback in the prior art, this solution is more intelligent and efficient.

[0043] 3. This solution uses a personalized training plan generation unit to build a detailed user profile and generate a personalized training plan based on the user's personal information, training history data, and real-time feedback data. This personalized training plan can better meet the user's actual needs and health conditions, and improve the pertinence and effectiveness of training. Compared with the general training plan in the prior art, this solution is more flexible and personalized.

[0044] 4. This solution also includes a visual feedback unit and an intelligent reminder unit, which provide users with intuitive training progress display and personalized reminder services through intelligent terminals. This helps users better understand their training status, arrange training time reasonably, and improve their enthusiasm and participation in training. Compared with simple text reminders in the prior art, this solution is more intuitive and convenient.

[0045] 5. This solution also includes multiple functional modules such as motion recognition unit, user health status assessment unit, remote monitoring and guidance unit, and data security and privacy protection unit, which further enhance the intelligence and security of the system. These functional modules can provide users with more comprehensive training support and guarantees to ensure the safety and effectiveness of the training process. Compared with the single-function system in the prior art, this solution is more complete and reliable.

[0046] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a partial framework diagram of an embodiment of a monitoring system for pelvic floor muscle training of the present invention;

[0048] Figure 2 It is an overall framework diagram of an embodiment of a monitoring system for pelvic floor muscle training of the present invention. DETAILED DESCRIPTION

[0049] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] The following is further described in detail through specific implementation methods:

[0051] Embodiment 1:

[0052] As attached Figure 1 and Figure 2 Shown: A monitoring system for pelvic floor muscle training, comprising:

[0053] A data acquisition unit is used to collect real-time data of pelvic floor muscle contraction and relaxation during pelvic floor muscle training, as well as the user's personal information and training history data; the data acquisition unit includes a sensor component, a personal information access module and a training history data recording module; the sensor component includes a pressure sensor, an electromyography sensor, an acceleration sensor, a displacement sensor and a temperature sensor. In this embodiment, the pressure sensor, the electromyography sensor, the acceleration sensor, the displacement sensor and the temperature sensor are all electrode sheets, which are connected to the controller through wireless signals. The pressure sensor is used to detect the pressure changes caused by the contraction of the pelvic floor muscles; the electromyography sensor is used to capture the electrical signals generated when the pelvic floor muscles are active; the acceleration sensor and the displacement sensor are used to monitor the tiny movements of the pelvic floor muscles; the temperature sensor is used to detect the temperature changes in the pelvic floor area and evaluate the muscle fatigue or inflammation status; the personal information access module is used for the user to input personal information, and the personal information includes age, gender and health status; the training history data recording module is used to record the user's past training data, and the training data includes training time, training intensity and training effect.

[0054] The data processing unit is used to preprocess the raw data collected by the data acquisition unit. The preprocessing includes data cleaning, denoising, formatting and feature extraction to ensure data quality and analysis accuracy.

[0055] The data analysis unit is used to analyze the data processed by the data processing unit in real time, identify the contraction and relaxation patterns of the pelvic floor muscles, calculate relevant parameters, and provide immediate feedback and guidance to the user based on the analysis results. The relevant parameters include: the contraction strength used to indicate the strength of the pelvic floor muscles during the contraction process, the duration used to indicate the contraction or relaxation state of the pelvic floor muscles, and the frequency used to indicate the contraction and relaxation of the pelvic floor muscles per unit time.

[0056] The personalized training plan generation unit is used to build a detailed user profile based on the personal information collected by the data collection unit, the training history data and the feedback data of the data analysis unit, and generate a personalized training plan based on the user profile and training goals. At the same time, the personalized training plan generation unit is also used to make dynamic adjustments based on the user's training progress and feedback. The personalized training plan includes: training goals set according to the user's actual needs and health status, training intensity and time schedule formulated according to the user's physical condition and training progress, training action instructions and demonstration guidance, and a mechanism for tracking the user's training progress in real time and providing personalized training progress tracking and feedback.

[0057] When the user's current training status collected in the data collection unit is inconsistent with the preset training goal, the data analysis unit analyzes the cause of the inconsistency: if the analysis result shows that the user's training intensity is lower than the training intensity in the personalized training plan, the cause of the inconsistency is judged to be insufficient training intensity of the user, thereby reminding the user to increase the training intensity; if the analysis result shows that the user's training intensity is not lower than the training intensity in the personalized training plan, and still cannot achieve the preset training goal, the cause of the inconsistency is judged to be that the preset training goal is too large, thereby the personalized training plan generation unit will automatically trigger the adjustment mechanism to adjust the training goal.

[0058] It also includes an intelligent reminder unit, which is used to send different reminders to the user according to the user's personalized training plan and the real-time data collected by the data acquisition module; if the user has not started training, the intelligent reminder unit will send a reminder to the user to start training at the scheduled training time; if the user has already started training, the intelligent reminder unit will send training process reminders and end reminders to the user according to the training progress and real-time data: when in the training process, the intelligent reminder unit will send reminders to the user according to the user's training status and data, to guide the user to adjust the training intensity, posture or breathing method; when the scheduled training time is reached, the intelligent reminder unit will send a reminder to the user to end the training, informing the user that the training has been completed. At the same time, it will also provide the user with a training summary based on the user's training data and feedback.

[0059] It also includes a user health status evaluation unit, which is used to evaluate the user's pelvic floor health status and training effect. Based on the real-time data collected by the sensor component and the user's personal information and training history, the unit calculates a comprehensive health index. According to the calculated comprehensive health index, the user's pelvic floor health status is divided into different levels, and the comprehensive health indicators of the user before and after training are compared to evaluate the effectiveness of the training plan. If the level of the user's health status improves, the current plan continues; if the level of the user's health status remains unchanged, the personalized training plan generation unit adjusts the training plan according to the evaluation results.

[0060] The specific implementation process is as follows: The user first needs to download and install the system application on the smart terminal (such as a mobile phone or tablet). When the system is started for the first time, the system will perform a series of initialization operations, including checking hardware connections, software version updates, database initialization, etc. Through the personal information access module, users need to enter their basic personal information in detail through the smart terminal, such as age, gender, height, weight, health status (including past medical history, surgical history, allergy history, etc.), and living habits (such as smoking, drinking, exercise habits, etc.). The system will establish a preliminary user health profile based on this information. Make sure that all sensors (pressure sensors, electromyography sensors, acceleration sensors, displacement sensors, temperature sensors) are in good working condition, the battery is fully charged, and they have been correctly worn or placed in the designated position (such as perineum, abdomen, etc.) according to the instructions.

[0061] Through the personalized training plan generation unit, according to the user's training goals and health conditions, and combined with the user's actual situation, a detailed plan including training intensity, time, action instructions and demonstration guidance is automatically generated. Before the training begins, the user needs to make sure that he is in a comfortable and relaxed state and avoid tension or anxiety. At the same time, according to the training plan, prepare the necessary auxiliary tools (such as yoga mats, dumbbells, etc.).

[0062] During the training process, the data acquisition unit will continuously collect the contraction and relaxation data of the pelvic floor muscles, as well as the user's personal information and training history data. These data include pressure changes, electromyography signals, micro-movement data, and temperature changes. The data processing unit will pre-process the collected raw data, including data cleaning (removing outliers, filling missing values), denoising (eliminating background noise), formatting (converting data into a unified format), and feature extraction (extracting key features related to pelvic floor muscle activity).

[0063] The data analysis unit will analyze the processed data in real time, and use machine learning algorithms (such as threshold judgment, cluster analysis, support vector machine, etc.) to process the electromyography signal and pressure data to identify the contraction and relaxation phases of the pelvic floor muscles. The identified contraction and relaxation patterns are matched with preset templates or typical patterns in the database to further confirm the type and characteristics of pelvic floor muscle activity.

[0064] By calculating the amplitude of the electromyographic signal during the contraction phase or the reading of the pressure sensor, the strength of the pelvic floor muscles during the contraction process can be quantified. Measure the duration of the contraction or relaxation state of the pelvic floor muscles, that is, the time interval from the beginning to the end of the contraction (or the beginning to the end of the relaxation), which can be determined by detecting the change point of the signal (such as the threshold crossing point). At the same time, calculate the number of times the pelvic floor muscles contract and relax per unit time, that is, the frequency, which can be achieved by counting the number of contraction and relaxation events in a certain period of time. Compare the calculated parameters with the preset thresholds or standards to evaluate the normality, strength, efficiency, etc. of the pelvic floor muscle activity. Combined with the user's personal information and training history, analyze training progress, fatigue level, possible muscle imbalance or injury, etc.

[0065] Combined with the analysis results of the data analysis unit, if it is detected that the user's training intensity (such as the contraction strength and duration of the pelvic floor muscles) is lower than the preset target, and the duration exceeds a certain threshold (such as two consecutive training sessions or longer), it is judged that the training intensity is insufficient. At this time, the intelligent reminder unit will send a reminder to the user, suggesting to increase the training intensity, such as increasing the number of contractions, extending the training time or increasing the difficulty of training.

[0066] If the system analysis results show that the user has difficulty in achieving the preset training goal in the current training state, and this difficulty persists (such as failing to achieve the goal after multiple attempts), it is judged that the preset training goal is too large. At this time, the personalized training plan generation unit will automatically adjust the training goal, reduce the difficulty or adjust the training focus to adapt to the user's actual situation and training progress.

[0067] When the scheduled training time is reached and the user has not finished the training, the intelligent reminder unit will send a reminder to the user to end the training. At the same time, the system will record the relevant data at the end of the training. The system provides a detailed training summary report based on the user's training data and feedback. The report includes the training effect (such as the improvement of pelvic floor muscle strength, the extension of duration, etc.), progress (such as the degree of improvement compared with the last training), areas that need improvement (such as improper posture, breathing problems, etc.) and personalized training suggestions.

[0068] The user health status assessment unit calculates a series of comprehensive health indicators based on the real-time data collected by the sensor component and the user's personal information and training history. These indicators are designed to fully reflect the user's pelvic floor muscle health status, including but not limited to:

[0069] Pelvic floor muscle strength index: By quantifying the strength of the pelvic floor muscles during contraction, combined with the user's age, gender and physical condition, it assesses the strength of the pelvic floor muscles.

[0070] Muscle fatigue degree: Determine whether the muscles are in a state of fatigue based on the duration of pelvic floor muscle activity, the frequency of contraction and relaxation, and the fluctuation of the electromyography signal.

[0071] Inflammation Risk: Combines temperature changes, micro-motion data, and the user’s historical health status to assess whether there is potential risk of inflammation in the pelvic floor area.

[0072] Based on these calculated comprehensive health indicators, the system classifies the user's pelvic floor health status into different levels, such as excellent, good, average, poor, etc. This grading system is designed to provide users with intuitive health status feedback to help them better understand their pelvic floor health. The user's comprehensive health indicators before and after training are then compared to evaluate the effectiveness of the training plan. This process involves a detailed analysis of each indicator, including the trend of the indicator, the magnitude of the change, and the comparison with the preset health standards.

[0073] If the user's health status level is improved (such as from "average" to "good"): this indicates that the current training plan is effective for the user, and the system will continue to follow the current plan, while encouraging the user to maintain the training rhythm and strive to further improve the health of the pelvic floor.

[0074] If the user's health status level remains unchanged or decreases (such as continuing to remain "poor", or dropping from "good" to "average"): this may mean that the current training plan is not suitable for the user or there are some problems. At this time, the personalized training plan generation unit will adjust the training plan based on the evaluation results. Adjustments may include increasing training intensity, changing training methods, introducing new training elements, or adjusting training frequency, etc., to better meet the needs of users and promote the improvement of pelvic floor health.

[0075] After adjusting the training plan, the system will continue to monitor the user's training progress and health status changes, and re-evaluate and adjust as needed. This dynamic adjustment process is designed to ensure that the training plan is always in sync with the user's actual situation and needs, so as to achieve the best training effect.

[0076] As the user's training continues, the system will continue to collect new data and continuously optimize and adjust the user's health status, training results and personalized training plan based on this data.

[0077] Embodiment 2:

[0078] The difference from Example 1 is that it also includes a visual feedback unit for providing a visual feedback report on the smart terminal. The visual feedback report includes but is not limited to: a chart for intuitively displaying the user's training progress and effect, a curve for reflecting the changing trend of the user's pelvic floor muscle activity, and an animation demonstration for simulating the pelvic floor muscle contraction and relaxation process.

[0079] The specific implementation process is as follows: The visual feedback unit will provide real-time and post-training visual feedback reports on the smart terminal. These reports include but are not limited to:

[0080] Chart display: Through bar charts, line charts and other charts, the user's training progress and results are intuitively displayed, such as the improvement of pelvic floor muscle strength, the extension of duration, etc. These charts can help users clearly see their progress and changes during the training process.

[0081] Curve reflection: Use curve graphs to show the changing trend of the user's pelvic floor muscle activity, such as the fluctuation of the electromyography signal, the trend of pressure change, etc. These curves can help users understand the activity status and changing rules of the pelvic floor muscles in different time periods.

[0082] Animation demonstration: The contraction and relaxation process of the pelvic floor muscles is simulated through animation, showing the dynamic effect of muscle activity. These animations can help users better understand the working principle of the pelvic floor muscles and the correctness of training movements, thereby improving the training effect.

[0083] Users can view historical training data and visual feedback reports at any time on the smart terminal application to understand their training progress and health status changes. Based on these data and reports, users can self-evaluate training results, adjust training plans, or seek professional guidance.

[0084] Embodiment 3:

[0085] The difference from Example 2 is that it also includes an action recognition unit, which is used to determine whether the user's current activity state is daily activity or pelvic floor muscle training through data from the sensor component;

[0086] When the data collected by the acceleration sensor and the displacement sensor show a regular movement pattern, and the movement pattern matches the pelvic floor muscle training action, and the data collected by the pressure sensor also shows corresponding pressure changes, it is determined that the user is currently in the training state;

[0087] When the data collected by the acceleration sensor and the displacement sensor show irregular movement changes, and the data collected by the pressure sensor does not show pressure changes related to pelvic floor muscle training, it is determined that the user is currently in a daily activity state;

[0088] When the user is in the training state, the data acquisition unit will start all sensors in the sensor assembly to perform data collection and analysis processes;

[0089] When the user is engaged in daily activities, the data collection unit will reduce unnecessary data processing.

[0090] The specific implementation process is as follows: The motion recognition unit analyzes the data collected by the acceleration sensor and the displacement sensor to find out whether there are regular movement patterns. These patterns usually match pelvic floor muscle training movements (such as Kegel exercises), including specific muscle contraction and relaxation cycles, which form specific waveforms or rhythms on the acceleration and displacement data (for example, small and frequent muscle contraction movements are detected). At the same time, the motion recognition unit checks the data collected by the pressure sensor to confirm whether it shows pressure changes related to pelvic floor muscle training. These changes are usually manifested as an increase and decrease in pressure values ​​within a specific time period. If the above two conditions are met at the same time, the motion recognition unit will determine that the user is currently in a training state.

[0091] If the data collected by the acceleration sensor and displacement sensor show irregular or large-scale movement changes (such as walking, running, jumping, etc.), this usually means that the user is performing free activities or daily tasks. At the same time, if the data collected by the pressure sensor does not show pressure changes related to pelvic floor muscle training (such as pressure values ​​remain relatively stable or change slightly), the motion recognition unit will determine that the user is currently in a daily activity state.

[0092] When the user is in training state, the data acquisition unit will start all sensors in the sensor assembly to conduct a comprehensive data collection and analysis process. This includes real-time processing of electromyography signals, pressure data, motion data, etc. to generate detailed training feedback reports. At the same time, the motion recognition unit will continuously monitor the user's training movements to ensure that they match the preset training plan. If the movement is found to be deviant or does not meet the requirements, the system will issue a reminder in time.

[0093] When the user is in daily activities, the data acquisition unit will reduce unnecessary data processing. For example, the frequency of real-time analysis of EMG signals can be reduced, or only key motion data (such as number of steps, activity time, etc.) can be recorded. This helps save system resources while avoiding excessive interference information during daily activities.

[0094] Embodiment 4:

[0095] The difference from Example 3 is that it also includes a remote monitoring and guidance unit, which is used to allow medical personnel or trainers to remotely monitor the user's training status in real time, including parameters such as the contraction strength, duration and frequency of the pelvic floor muscles, as well as the user's training progress and feedback. Based on the monitoring results, the medical personnel or trainers can provide the user with real-time training guidance or suggestions.

[0096] The specific implementation process is as follows: medical staff or trainers view the user's training data and status in real time through the remote monitoring and guidance unit. Based on the monitoring results, medical staff or trainers provide training guidance or suggestions to users in real time through voice, video or text. This includes adjusting training intensity, correcting movement posture, encouraging users to persist, etc.

[0097] Embodiment 5:

[0098] The difference from Example 4 is that it also includes a data security and privacy protection unit, which is used to use encryption technology to protect all collected, processed, stored and transmitted user data and perform access control.

[0099] The specific implementation process is as follows: According to the sensitivity and importance of the data, select appropriate encryption technology, such as symmetric encryption, asymmetric encryption, hash function, etc. Encrypt the collected user data to ensure that the data content is not accessed by unauthorized personnel during storage and transmission. Focus on encrypting sensitive data (such as personal privacy information, transaction data, etc.) to improve data security. Establish a strict key management system to ensure the safe storage, distribution and update of keys. Use technical means such as key escrow and key splitting to prevent keys from being controlled by a single person or system.

[0100] Use multi-factor authentication technology, such as username + password + biometrics, to ensure the authenticity of user identities. Perform strict identity authentication on users who access sensitive data to prevent unauthorized users from accessing data. Assign appropriate access rights based on user responsibilities and needs. Use technical means such as role-based access control (RBAC) or attribute-based access control (ABAC) to achieve fine-grained permission management.

[0101] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A monitoring system for pelvic floor muscle training, characterized in that: include: A data collection unit, used to collect real-time data of pelvic floor muscle contraction and relaxation during pelvic floor muscle training, as well as the user's personal information and training history data; A data processing unit, used for preprocessing the raw data collected by the data collection unit, the preprocessing including data cleaning, denoising, formatting and feature extraction; A data analysis unit, used to perform real-time analysis on the data processed by the data processing unit, identify the contraction and relaxation patterns of the pelvic floor muscles, calculate relevant parameters, and provide instant feedback and guidance to the user based on the analysis results; A personalized training plan generation unit, which is used to build a detailed user profile based on the personal information collected by the data collection unit, the training history data and the feedback data of the data analysis unit, and to generate a personalized training plan based on the user profile and the training goal. At the same time, the personalized training plan generation unit is also used to make dynamic adjustments based on the user's training progress and feedback; When the current training state of the user collected by the data collection unit is inconsistent with the preset training target, the data analysis unit analyzes the cause of the inconsistency; If the analysis result shows that the user's training intensity is lower than the training intensity in the personalized training plan, the inconsistency is judged to be caused by insufficient training intensity of the user, and the user is reminded to increase the training intensity; If the analysis results show that the user's training intensity is not lower than the training intensity in the personalized training plan, but still cannot achieve the preset training goal, it is judged that the cause of the inconsistency is that the preset training goal is too large, so the personalized training plan generation unit will automatically trigger the adjustment mechanism to adjust the training goal.

2. The monitoring system for pelvic floor muscle training according to claim 1, characterized in that: The data acquisition unit includes a sensor component, a personal information access module and a training history data recording module; The sensor components include pressure sensors, electromyography sensors, acceleration sensors, displacement sensors and temperature sensors. The pressure sensor is used to detect the pressure changes generated when the pelvic floor muscles contract; the electromyography sensor is used to capture the electrical signals generated when the pelvic floor muscles are active; the acceleration sensor and displacement sensor are used to monitor the tiny movements of the pelvic floor muscles; the temperature sensor is used to detect the temperature changes in the pelvic floor area and evaluate muscle fatigue or inflammation. The personal information access module is used for users to input personal information, including age, gender and health status; The training history data recording module is used to record the user's past training data, which includes training time, training intensity and training effect.

3. The monitoring system for pelvic floor muscle training according to claim 2, characterized in that: The relevant parameters calculated in the data analysis unit include: the contraction force used to indicate the strength of the pelvic floor muscles during contraction, the duration of the contraction or relaxation of the pelvic floor muscles, and the frequency of contraction and relaxation of the pelvic floor muscles per unit time.

4. The monitoring system for pelvic floor muscle training according to claim 3, characterized in that: The personalized training plan includes: training goals set according to the user's actual needs and health status, training intensity and time schedule formulated according to the user's physical condition and training progress, training movement instructions and demonstration guidance, as well as a real-time tracking of the user's training progress and providing personalized training progress tracking and feedback mechanism.

5. The monitoring system for pelvic floor muscle training according to claim 4, characterized in that: It also includes a visual feedback unit for providing a visual feedback report on the smart terminal. The visual feedback report includes but is not limited to: a chart for intuitively displaying the user's training progress and effect, a curve for reflecting the changing trend of the user's pelvic floor muscle activity, and an animation demonstration for simulating the pelvic floor muscle contraction and relaxation process.

6. The monitoring system for pelvic floor muscle training according to claim 5, characterized in that: It also includes an intelligent reminder unit, which is used to send different reminders to the user according to the user's personalized training plan and the real-time data collected by the data collection module; If the user has not started training, the smart reminder unit will send a reminder to the user to start training at the scheduled training time; If the user has already started training, the intelligent reminder unit will send training process reminders and end reminders to the user based on the training progress and real-time data; During training, the smart reminder unit will send reminders to users based on their training status and data to guide them to adjust their training intensity, posture or breathing method. When the scheduled training time is reached, the intelligent reminder unit will send a reminder to the user to end the training to inform the user that the training has been completed. At the same time, it will also provide the user with a training summary based on the user's training data and feedback.

7. The monitoring system for pelvic floor muscle training according to claim 6, characterized in that: It also includes a motion recognition unit, which is used to determine whether the user's current activity state is daily activity or pelvic floor muscle training through data from the sensor component; When the data collected by the acceleration sensor and the displacement sensor show a regular movement pattern, and the movement pattern matches the pelvic floor muscle training action, and the data collected by the pressure sensor also shows corresponding pressure changes, it is determined that the user is currently in the training state; When the data collected by the acceleration sensor and the displacement sensor show irregular movement changes, and the data collected by the pressure sensor does not show pressure changes related to pelvic floor muscle training, it is determined that the user is currently in a daily activity state; When the user is in the training state, the data acquisition unit will start all sensors in the sensor assembly to perform data collection and analysis processes; When the user is engaged in daily activities, the data collection unit will reduce unnecessary data processing.

8. The monitoring system for pelvic floor muscle training according to claim 7, characterized in that: The user health status evaluation unit is further included, and the user health status evaluation unit is used to evaluate the user's pelvic floor health status and training effect, calculate a comprehensive health index based on the real-time data collected by the sensor component and the user's personal information and training history, classify the user's pelvic floor health status into different levels according to the calculated comprehensive health index, compare the user's comprehensive health index before and after training, and evaluate the effectiveness of the training plan; If the user's health status level is improved, continue with the current plan; If the level of the user's health status remains unchanged, the personalized training plan generating unit adjusts the training plan according to the evaluation result.

9. The monitoring system for pelvic floor muscle training according to claim 8, characterized in that: It also includes a remote monitoring and guidance unit, which is used to allow medical personnel or trainers to remotely monitor the user's training status in real time, including parameters such as the contraction strength, duration and frequency of the pelvic floor muscles, as well as the user's training progress and feedback. Based on the monitoring results, the medical personnel or trainers can provide the user with real-time training guidance or suggestions.

10. The monitoring system for pelvic floor muscle training according to claim 9, characterized in that: It also includes a data security and privacy protection unit, which is used to protect all collected, processed, stored and transmitted user data using encryption technology and to perform access control.

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