A pressure monitoring system and method based on knee joint rehabilitation

Abstract

The present invention relates to the field of rehabilitation training technology, specifically a pressure monitoring system and method for knee joint rehabilitation. First, the system collects physiological data of the quadriceps femoris during knee contraction in healthy individuals and uploads it to a database. Based on this data, a pressure threshold is calculated. Secondly, by gradually increasing the contraction pressure of the patient's quadriceps femoris, new pressure and electromyographic feedback data are obtained and stored in the database along with the patient's baseline data. This data is then analyzed using a graph factorization machine algorithm to generate a personalized recommended contraction pressure. Finally, while the patient performs the recommended pressure training, the system records data such as contraction pressure, duration, and number of times in real time. Based on this data, an adaptive adjustment model is established to continuously optimize the recommended pressure to adapt to the patient's rehabilitation progress.

Description

Technical Field

[0001] The present invention relates to the technical field of rehabilitation training, and in particular to a pressure monitoring system and method based on knee joint rehabilitation. Background Art

[0002] Patients with orthopedic lower limb disorders often experience reduced activity due to inflammatory pain, fracture immobilization, and surgical trauma, which can lead to arthrogenic muscle inhibition, resulting in lower limb muscle atrophy and decreased strength. This, in turn, leads to reduced body activity, gait abnormalities, and decreased balance, severely impacting lower limb functional rehabilitation, particularly joint functional rehabilitation. Early rehabilitation exercises can ensure surgical outcomes, effective joint range of motion, maintain joint stability, and gradually restore joint functional rehabilitation. The quadriceps femoris, the sole extensor muscle of the knee, is considered a key focus of rehabilitation care for orthopedic lower limb disorders. However, current clinical nursing practices lack specific requirements for the intensity, duration, and frequency of muscle contraction during quadriceps femoris training, lack professional assessment criteria, and are highly arbitrary. There are also no specific, real-time, dynamic monitoring tools for evaluating the intensity and effectiveness of quadriceps femoris rehabilitation exercises.

[0003] To this end, a pressure monitoring system and method based on knee joint rehabilitation are proposed. Summary of the Invention

[0004] The present invention aims to provide a pressure monitoring system and method for knee rehabilitation, thereby implementing a scientific and reliable knee rehabilitation program. To address the existing technical issues, the present invention proposes a pressure calculation model that uses the displacement and angle changes of the stretched fabric and the IMU sensor group to obtain real-time quadriceps femoris contraction pressure during knee joint movement. Secondly, the present invention proposes a graph factorization machine algorithm that intelligently recommends appropriate contraction pressure by combining the patient's own basic data, basic data in the patient database, contraction pressure data, and electromyographic feedback data. Finally, the present invention establishes a pressure adaptive adjustment model based on real-time data to dynamically adjust contraction pressure, ensuring pressure optimization and patient comfort during the rehabilitation process.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A pressure monitoring system based on knee joint rehabilitation, comprising:

[0007] A first pressure acquisition module is configured to acquire physiological data generated by the quadriceps femoris of a healthy person when the knee joint contracts through a pressure acquisition device, and upload the physiological data to a healthy person database; the physiological data includes a first contraction pressure and a first myoelectric feedback;

[0008] a pressure analysis module, configured to obtain a first contraction pressure threshold and a second contraction pressure threshold according to the physiological data;

[0009] a second pressure acquisition module, configured to gradually increase the contraction pressure at the quadriceps femoris of the patient according to the first contraction pressure threshold and the second contraction pressure threshold, obtain a second contraction pressure data set and a second myoelectric feedback data set, and upload the second contraction pressure data set, the second myoelectric feedback data set and the patient's basic data to a patient database;

[0010] a pressure recommendation module, configured to obtain a recommended contraction pressure using a graph factorization machine algorithm based on the second contraction pressure data group, the second electromyography feedback data group, and the patient basic data in the patient database;

[0011] an effective counting module, configured to record the real-time contraction pressure, contraction time, contraction times, and effective contraction times of the quadriceps femoris during the implementation phase of the recommended contraction pressure, and to display the real-time contraction pressure, the recommended contraction pressure, and the effective contraction times;

[0012] The adaptive adjustment module is used to establish a pressure adaptive adjustment model according to the real-time contraction pressure, the contraction time, the number of contractions and the effective number of contractions, and adaptively update the recommended contraction pressure.

[0013] Furthermore, the pressure acquisition device includes a stretch fabric, a surface electromyography sensor and an IMU sensor group. The IMU sensor group includes a main sensor unit and multiple slave sensor units. The slave sensor units are evenly and equidistantly distributed with the main sensor unit as the center.

[0014] Furthermore, the physiological data generated by the quadriceps femoris during knee contraction of healthy subjects obtained through the pressure acquisition device include:

[0015] Wrapping the pressure collection device on the quadriceps femoris of a healthy person;

[0016] collecting a first state of the slave sensor unit when the knee joint of a healthy person is in an extended state, and a second state of the slave sensor unit when the knee joint of a healthy person is in a contracted state;

[0017] obtaining the first contraction pressure using a pressure calculation model;

[0018] collecting the first myoelectric feedback from a surface electromyographic sensor of a healthy person in a knee joint contraction state;

[0019] The first state includes a first distance from the slave sensing unit to the master sensing unit, and a first horizontal angle and a first vertical angle of the slave sensing unit relative to the master sensing unit; the second state includes a second distance from the slave sensing unit to the master sensing unit, and a second horizontal angle and a second vertical angle of the slave sensing unit relative to the master sensing unit; the calculation formula of the pressure calculation model is:

[0020]

[0021] Wherein, T represents the first contraction pressure, n represents the number of the slave sensing units, k represents the tensile elastic coefficient of the stretched fabric, i represents the index number of the slave sensing unit, Δd i represents the distance change between the first distance and the second distance from the sensing unit i, θ i represents the horizontal change between the first horizontal angle and the second horizontal angle of the slave sensing unit i, Indicates the vertical changes of the first vertical angle and the second vertical angle of the slave sensing unit i.

[0022] Furthermore, the first pressure threshold and the second pressure threshold are obtained through normal distribution, and the first contraction pressure threshold P lower =μ-2σ, the second contraction pressure threshold P upper =μ+2σ, where μ represents the mean systolic pressure and σ represents the standard deviation of the pressure.

[0023] Furthermore, obtaining the recommended contraction pressure using a graph factorization machine algorithm includes:

[0024] Constructing a graph structure according to the second contraction pressure data set, the second myoelectric feedback data set and the patient basic data in the patient database;

[0025] Using a graph neural network to embed the nodes in the graph structure, and converting the second contraction pressure data set, the second electromyography feedback data set, and the patient basic data into a low-dimensional embedding vector;

[0026] Based on the embedded representation, the factorization machine algorithm is used to learn the feature interaction information between features;

[0027] The low-dimensional embedding vector processed by the graph neural network is integrated with the feature interaction information of the factorization machine to predict the patient's electromyographic feedback data under different contraction pressures and obtain the predicted electromyographic feedback data;

[0028] Minimizing the difference between the predicted myoelectric feedback data and the second myoelectric feedback data group by optimizing a loss function;

[0029] The patient's basic data is input, and a graph factorization machine algorithm is used to obtain the recommended contraction pressure generated by the quadriceps femoris when the patient's knee joint contracts.

[0030] Furthermore, the effective counting module includes a real-time contraction pressure display device, a recommended contraction pressure display device, an effective contraction number display device and an effective number prompt device; the real-time contraction pressure display device is used to display the real-time contraction pressure; the recommended contraction pressure display device is used to display the recommended contraction pressure; the effective contraction number display device is used to display the effective contraction number, and the effective contraction number is the number of times the real-time contraction pressure is greater than the recommended contraction pressure and the contraction time is greater than the contraction time threshold; the effective number prompt device is used to prompt whether the real-time contraction pressure is greater than the recommended contraction pressure.

[0031] Furthermore, the pressure adaptive adjustment model calculation formula is:

[0032]

[0033] Among them, P recommended Indicates the updated recommended systolic pressure, P current represents the real-time contraction pressure, α0 represents the adjustment coefficient, ln(·) represents the logarithmic function, e represents the natural exponent, T represents the contraction time, N represents the number of contractions, N effective Indicates the effective number of contractions.

[0034] Furthermore, the monitoring system further includes:

[0035] A manual adjustment module is used to manually adjust the recommended contraction pressure when the recommended contraction pressure is not within the user's comfort range. The manual adjustment module includes an adjustment device.

[0036] A pressure monitoring method based on knee joint rehabilitation, comprising:

[0037] Acquire physiological data generated by the quadriceps femoris during knee contraction of a healthy person, and upload the physiological data to a healthy person database; the physiological data includes the first contraction pressure and the first myoelectric feedback;

[0038] Obtaining a first contraction pressure threshold and a second contraction pressure threshold according to the first contraction pressure and the first myoelectric feedback;

[0039] Gradual increase of the contraction pressure at the quadriceps femoris of the patient according to the first contraction pressure threshold and the second contraction pressure threshold, obtaining a second contraction pressure data set and a second myoelectric feedback data set, and uploading the second contraction pressure data set, the second myoelectric feedback data set and the patient's basic data to a patient database;

[0040] Obtaining a recommended contraction pressure using a graph factorization machine algorithm according to the second contraction pressure data set, the second electromyography feedback data set, and the patient's basic data in the patient database;

[0041] The real-time contraction pressure, contraction time, number of contractions and effective number of contractions of the quadriceps femoris during the implementation of the recommended contraction pressure are recorded, and a pressure adaptive adjustment model is established based on the real-time contraction pressure, contraction time, number of contractions and effective number of contractions to adaptively update the recommended pressing pressure.

[0042] Furthermore, the method further comprises:

[0043] Used to manually adjust the recommended contraction pressure when the recommended contraction pressure is not within the user's comfort range.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. The pressure calculation model proposed in this invention can obtain the contraction pressure generated by the quadriceps femoris during knee joint contraction in real time. By analyzing the displacement and angle changes of the sensor units distributed on the muscle surface, the pressure distribution and contraction intensity of the quadriceps femoris under different contraction states are accurately calculated. Combined with the feedback information of the electromyographic signal, it provides data support for subsequent contraction pressure recommendations.

[0046] 2. The proposed Graph Factorization Machine algorithm intelligently calculates the most appropriate recommended contraction pressure for each patient by integrating a large amount of patient data, contraction pressure data, and electromyographic feedback data from a patient database. This algorithm utilizes a graph neural network to capture low-dimensional feature relationships between different features and, through factorization, to capture higher-order feature interactions. This ensures that the recommended contraction pressure is more personalized and accurate, tailored to the patient's specific needs.

[0047] 3. The adaptive pressure adjustment model proposed in this invention dynamically and adaptively adjusts the recommended contraction pressure based on parameters such as real-time contraction pressure, contraction time, number of contractions, and number of effective contractions during the patient's knee rehabilitation process. Through real-time monitoring and feedback, this model ensures that the applied contraction pressure is personalized to the patient's specific situation, optimizing contraction pressure control during rehabilitation. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A structural diagram of a pressure monitoring system based on knee joint rehabilitation provided by the present invention;

[0049] Figure 2 A diagram showing some modules of a pressure monitoring system based on knee joint rehabilitation provided by the present invention;

[0050] Figure 3A flow chart of a pressure monitoring method based on knee joint rehabilitation provided by the present invention;

[0051] Figure numerals: 21-stretching fabric, 22-slave sensing unit, 23-master sensing unit, 24-surface electromyography sensor, 61-real-time contraction pressure display device, 62-recommended contraction pressure display device, 63-effective contraction number display device, 64-effective number prompting device, 81-adjustment device. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention 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 making creative efforts are within the scope of protection of the present invention.

[0053] A pressure monitoring system based on knee joint rehabilitation, such as Figure 1 As shown, including:

[0054] A first pressure acquisition module is configured to acquire physiological data generated by the quadriceps femoris of a healthy person when the knee joint contracts through a pressure acquisition device, and upload the physiological data to a healthy person database; the physiological data includes a first contraction pressure and a first myoelectric feedback;

[0055] a pressure analysis module, configured to obtain a first contraction pressure threshold and a second contraction pressure threshold according to the physiological data;

[0056] a second pressure acquisition module, configured to gradually increase the contraction pressure at the quadriceps femoris of the patient according to the first contraction pressure threshold and the second contraction pressure threshold, obtain a second contraction pressure data set and a second myoelectric feedback data set, and upload the second contraction pressure data set, the second myoelectric feedback data set and the patient's basic data to a patient database;

[0057] a pressure recommendation module, configured to obtain a recommended contraction pressure using a graph factorization machine algorithm based on the second contraction pressure data group, the second electromyography feedback data group, and the patient basic data in the patient database;

[0058] an effective counting module, configured to record the real-time contraction pressure, contraction time, contraction times, and effective contraction times of the quadriceps femoris during the implementation phase of the recommended contraction pressure, and to display the real-time contraction pressure, the recommended contraction pressure, and the effective contraction times;

[0059] The adaptive adjustment module is used to establish a pressure adaptive adjustment model according to the real-time contraction pressure, the contraction time, the number of contractions and the effective number of contractions, and adaptively update the recommended contraction pressure.

[0060] See Figure 1 The first pressure acquisition module in:

[0061] Furthermore, if Figure 2 As shown, the pressure acquisition device includes a stretch fabric, a surface electromyography sensor and an IMU sensor group. The IMU sensor group includes a main sensor unit and a slave sensor unit. The slave sensor units are evenly and equidistantly distributed with the main sensor unit as the center.

[0062] Specifically, the IMU sensor group includes a master sensor unit and a slave sensor unit. The number of the master sensor unit is 1, and the number of the slave sensor units can be 4, 5, 6, ... 12. The slave sensor units are evenly and equidistantly distributed with the master sensor unit as the center. The surface electromyography sensor is located on the side of the main sensor unit, and there is no overlapping part between the surface electromyography sensor and the main sensor unit or the slave sensor unit. In a feasible embodiment, if Figure 2 As shown, the pressure acquisition device includes stretch fabric, surface electromyography sensor and IMU sensor group, wherein the IMU sensor group includes a master sensor unit and 8 slave sensor units. The slave sensor units are evenly and equidistantly distributed with the master sensor unit as the center, and the angle between every two slave sensor units and the master sensor unit is 45°.

[0063] The pressure acquisition device has a uniform distribution of master and slave sensing units, especially a structure with the master sensing unit as the center and arranged at equal angles. This enables the device to more comprehensively and accurately monitor the stretching and displacement changes in all directions, thereby improving the measurement accuracy of tension and angle changes.

[0064] Furthermore, the physiological data generated by the quadriceps femoris during knee contraction of healthy subjects obtained through the pressure acquisition device include:

[0065] Wrapping the pressure collection device on the quadriceps femoris of a healthy person;

[0066] collecting a first state of the slave sensor unit when the knee joint of a healthy person is in an extended state, and a second state of the slave sensor unit when the knee joint of a healthy person is in a contracted state;

[0067] obtaining the first contraction pressure using a pressure calculation model;

[0068] collecting the first myoelectric feedback from a surface electromyographic sensor of a healthy person in a knee joint contraction state;

[0069] The first state includes a first distance from the slave sensing unit to the master sensing unit, and a first horizontal angle and a first vertical angle of the slave sensing unit relative to the master sensing unit; the second state includes a second distance from the slave sensing unit to the master sensing unit, and a second horizontal angle and a second vertical angle of the slave sensing unit relative to the master sensing unit; the calculation formula of the pressure calculation model is:

[0070]

[0071] Wherein, T represents the first contraction pressure, n represents the number of the slave sensing units, k represents the tensile elastic coefficient of the stretched fabric, i represents the index number of the slave sensing unit, Δd i represents the distance change between the first distance and the second distance from the sensing unit i, θ i represents the horizontal change between the first horizontal angle and the second horizontal angle of the slave sensing unit i, Indicates the vertical changes of the first vertical angle and the second vertical angle of the slave sensing unit i.

[0072] By capturing the position changes of the sensor unit during knee extension and contraction, we can accurately capture the changes in quadriceps tension in different states. The first contraction pressure, derived through analysis of the pressure calculation model, and the corresponding EMG signal from the EMG sensor comprehensively reflect the relationship between muscle contraction intensity and surface electrical activity.

[0073] See Figure 1 Pressure analysis module in:

[0074] Furthermore, the first pressure threshold and the second pressure threshold are obtained through normal distribution, and the first contraction pressure threshold P lower =μ-2σ, the second contraction pressure threshold P upper =μ+2σ, where μ represents the mean systolic pressure and σ represents the standard deviation of the pressure.

[0075] Specifically, the EMG feedback data can reflect the patient's acceptance of the quadriceps contraction pressure during knee contraction. In a static state, the EMG feedback is between 0-10 μV. During mild muscle contraction, the EMG feedback data is in the range of 10-50 μV. During moderate muscle contraction, the EMG feedback data is between 50-200 μV. During strong muscle contraction, the EMG feedback data is between 200-500 μV or even higher. The quadriceps contraction pressure data P1, P2, ..., P are extracted from the healthy subject database. n (first pressure data group) and corresponding electromyographic feedback data S1, S2, ..., S n(First EMG feedback data set), calculate the average contraction pressure μ and standard deviation σ, where Considering that the quadriceps contraction pressure that patients can accept is different from that of healthy people, the first contraction pressure threshold (lower limit) and the second contraction pressure threshold (upper limit) are obtained based on the normal distribution with a confidence level of 95%. At this time, the first contraction pressure threshold P lower =μ-2σ, the second contraction pressure threshold P upper =μ+2σ.

[0076] By using the normal distribution to obtain the first contraction pressure threshold and the second contraction pressure threshold, the rationality and scientific nature of the quadriceps contraction pressure range can be ensured. Calculating the threshold based on the mean contraction pressure and the pressure standard deviation can effectively cover the comfortable contraction pressure range for most patients and avoid potential discomfort or injury to patients caused by too low or too high a contraction pressure.

[0077] See Figure 1 The second pressure acquisition module and pressure recommendation module in:

[0078] Furthermore, obtaining the recommended contraction pressure using a graph factorization machine algorithm includes:

[0079] Constructing a graph structure according to the second contraction pressure data set, the second myoelectric feedback data set and the patient basic data in the patient database;

[0080] Using a graph neural network to embed the nodes in the graph structure, and converting the second contraction pressure data set, the second electromyography feedback data set, and the patient basic data into a low-dimensional embedding vector;

[0081] Based on the embedded representation, the factorization machine algorithm is used to learn the feature interaction information between different features;

[0082] The low-dimensional embedding vector processed by the graph neural network is integrated with the feature interaction information of the factorization machine to predict the patient's electromyographic feedback data under different contraction pressures and obtain the predicted electromyographic feedback data;

[0083] Minimizing the difference between the predicted myoelectric feedback data and the second myoelectric feedback data group by optimizing a loss function;

[0084] The patient's basic data is input, and the recommended contraction pressure of the patient's knee joint lesion area is obtained using a graph factorization machine algorithm.

[0085] Specifically, age, gender, weight, medical history, lesion area, lesion index, second contraction pressure data set, and second electromyographic feedback data set are used as graph structures, and the relationship between nodes is used as the edge of the graph to construct a graph G = (V, E); the graph neural network is used to process the node information in the graph, and the embedding representation of the node in each layer of GNN is the information aggregation of a node to its neighboring nodes. The embedding representation in represents the embedding representation of node v at the kth layer, N(v) represents the set of neighbor nodes of node v, Aggregate represents the weighted sum operation, and Embedding represents h v (k) After GNN processing, the contextual information of the node is extracted and converted into a low-dimensional embedding vector; these embedding vectors are passed as input to the factorization machine layer, and the factorization machine model is used to capture the second-order interaction of features. in <v i ,v j > is the inner product of two latent vectors, representing the interaction effect between feature i and feature j; x i x j It is the product of the eigenvalues, which represents their interaction; the node representation generated by GNN is fused with the second-order interaction result of the factorization machine to obtain the prediction result Where w0 is the bias term, w i represents the linear weight of feature i, GNN(h i ) After the GNN layer is calculated, the graph structure contribution of node i is minimized by This makes the model more accurate in prediction; when the loss function stops iterating, the quadriceps contraction pressure at the highest comfort level recommended by the graph factorization machine algorithm is obtained.

[0086] By constructing a patient's contraction pressure data, EMG feedback data, and underlying data into a graph structure and using a graph neural network for low-dimensional embedding, combined with a factorization mechanism to capture the interactions between different features, we can more accurately predict the patient's EMG feedback at different contraction pressures. Adaptive updates based on big data feedback and an optimized loss function ensure dynamic and personalized recommended contraction pressures, ultimately providing each patient with a reasonable recommended quadriceps contraction pressure.

[0087] See Figure 1 Valid counting module in:

[0088] Furthermore, if Figure 2As shown, the effective counting module includes a real-time contraction pressure display device, a recommended contraction pressure display device, an effective contraction number display device and an effective number prompting device; the real-time contraction pressure display device is used to display the real-time contraction pressure; the recommended contraction pressure display device is used to display the recommended contraction pressure; the effective contraction number display device is used to display the effective contraction number, and the effective contraction number is the number of times the real-time contraction pressure is greater than the recommended contraction pressure and the contraction time is greater than the contraction time threshold; the effective number prompting device is used to prompt whether the real-time contraction pressure is greater than the recommended contraction pressure.

[0089] In a feasible implementation scheme, the effective counting module obtains the first contraction pressure of 50N collected by the first pressure collection module in real time, that is, the real-time contraction pressure is 50N, then the real-time contraction pressure display device will display 50N on the screen; when the pressure recommendation module obtains the recommended contraction pressure, the recommended contraction pressure display device will display 60N on the screen; when the recommended contraction pressure is 60N, the contraction time threshold is 5 seconds, the patient's quadriceps real-time contraction pressure is 65N, and the contraction time is 10 seconds, then the effective contraction number displayed by the effective contraction number display device is increased by one; when the patient's quadriceps real-time contraction pressure is 65N, the real-time contraction pressure is greater than the recommended contraction pressure of 60N, then the red prompt light of the effective number prompt device is on; when the patient's quadriceps real-time contraction pressure is 55N, the real-time contraction pressure is less than the recommended contraction pressure of 60N, then the green prompt light of the effective number prompt device is on.

[0090] The effective counting module monitors and provides feedback on the effectiveness of real-time contraction pressure. The effective contraction count display device provides the number of times the real-time contraction pressure exceeds the recommended contraction pressure, helping users to intuitively understand the effectiveness of quadriceps contraction. The effective contraction count reminder device immediately reminds users whether the real-time contraction pressure has reached the recommended contraction pressure, ensuring that the compression process remains within the optimal effect range.

[0091] See Figure 1 Adaptive adjustment module in:

[0092] Furthermore, the pressure adaptive adjustment model calculation formula is:

[0093]

[0094] Among them, P recommended Indicates the updated recommended systolic pressure, P current represents the real-time contraction pressure, α0 represents the adjustment coefficient, ln(·) represents the logarithmic function, e represents the natural exponent, T represents the contraction time, N represents the number of contractions, N effective Indicates the effective number of contractions.

[0095] By establishing an adaptive pressure adjustment model based on real-time contraction pressure, contraction time, contraction frequency, and effective contraction frequency during knee rehabilitation, the recommended contraction pressure is dynamically updated, ensuring precise adjustments to contraction pressure as rehabilitation progresses. This model continuously optimizes contraction pressure settings based on real-time patient feedback and rehabilitation progress, providing a more personalized and scientific contraction pressure implementation plan for each patient.

[0096] Furthermore, if Figure 1 As shown, the monitoring system further includes:

[0097] The manual adjustment module is used to manually adjust the recommended contraction pressure when the recommended contraction pressure is not within the user's comfort range.

[0098] In a feasible implementation scheme, the user comfort is fed back through electromyographic feedback data, and the electromyographic feedback data corresponding to the user comfort range is [10μV, 500μV]. The adjustment device can achieve upward and downward adjustment, and the gradient of each manual adjustment is 1N.

[0099] The manual adjustment module allows users to manually adjust the recommended pressure based on personal feelings or doctor's advice through the up and down functions.

[0100] Example 2

[0101] A pressure monitoring method based on knee joint rehabilitation, such as Figure 3 As shown, including:

[0102] S910: Acquire physiological data generated by the quadriceps femoris when the knee joint of a healthy person contracts, and upload the physiological data to a healthy person database; the physiological data includes a first contraction pressure and a first myoelectric feedback;

[0103] S920: Obtaining a first contraction pressure threshold and a second contraction pressure threshold according to the first contraction pressure and the first myoelectric feedback;

[0104] S930: Gradual increase of the contraction pressure at the quadriceps femoris of the patient according to the first contraction pressure threshold and the second contraction pressure threshold, obtaining a second contraction pressure data set and a second myoelectric feedback data set, and uploading the second contraction pressure data set, the second myoelectric feedback data set, and the patient's basic data to the patient database;

[0105] S940: Obtaining a recommended contraction pressure using a graph factorization machine algorithm based on the second contraction pressure data set, the second electromyography feedback data set, and the patient's basic data in the patient database;

[0106] S950: Record the real-time contraction pressure, contraction time, contraction number and effective contraction number of the quadriceps femoris during the implementation phase of the recommended contraction pressure, and establish a pressure adaptive adjustment model based on the real-time contraction pressure, contraction time, contraction number and effective contraction number to adaptively update the recommended pressing pressure.

[0107] Furthermore, the pressure acquisition device includes a stretch fabric, a surface electromyography sensor and an IMU sensor group. The IMU sensor group includes a main sensor unit and multiple slave sensor units. The slave sensor units are evenly and equidistantly distributed with the main sensor unit as the center.

[0108] Furthermore, the physiological data generated by the quadriceps femoris during knee contraction of healthy subjects obtained through the pressure acquisition device include:

[0109] Wrapping the pressure collection device on the quadriceps femoris of a healthy person;

[0110] collecting a first state of the slave sensor unit when the knee joint of a healthy person is in an extended state, and a second state of the slave sensor unit when the knee joint of a healthy person is in a contracted state;

[0111] obtaining the first contraction pressure using a pressure calculation model;

[0112] collecting the first myoelectric feedback from a surface electromyographic sensor of a healthy person in a knee joint contraction state;

[0113] The first state includes a first distance from the slave sensing unit to the master sensing unit, and a first horizontal angle and a first vertical angle of the slave sensing unit relative to the master sensing unit; the second state includes a second distance from the slave sensing unit to the master sensing unit, and a second horizontal angle and a second vertical angle of the slave sensing unit relative to the master sensing unit; the calculation formula of the pressure calculation model is:

[0114]

[0115] Wherein, T represents the first contraction pressure, n represents the number of the slave sensing units, k represents the tensile elastic coefficient of the stretched fabric, i represents the index number of the slave sensing unit, Δd i represents the distance change between the first distance and the second distance from the sensing unit i, θ i represents the horizontal change between the first horizontal angle and the second horizontal angle of the slave sensing unit i, Indicates the vertical changes of the first vertical angle and the second vertical angle of the slave sensing unit i.

[0116] Furthermore, the first pressure threshold and the second pressure threshold are obtained through normal distribution, and the first contraction pressure threshold Plower =μ-2σ, the second contraction pressure threshold P upper =μ+2σ, where μ represents the mean systolic pressure and σ represents the standard deviation of the pressure.

[0117] Furthermore, obtaining the recommended contraction pressure using a graph factorization machine algorithm includes:

[0118] Constructing a graph structure according to the second contraction pressure data set, the second myoelectric feedback data set and the patient basic data in the patient database;

[0119] Using a graph neural network to embed the nodes in the graph structure, and converting the second contraction pressure data set, the second electromyography feedback data set, and the patient basic data into a low-dimensional embedding vector;

[0120] Based on the embedded representation, the factorization machine algorithm is used to learn the feature interaction information between features;

[0121] The low-dimensional embedding vector processed by the graph neural network is integrated with the feature interaction information of the factorization machine to predict the patient's electromyographic feedback data under different contraction pressures and obtain the predicted electromyographic feedback data;

[0122] Minimizing the difference between the predicted myoelectric feedback data and the second myoelectric feedback data group by optimizing a loss function;

[0123] The patient's basic data is input, and a graph factorization machine algorithm is used to obtain the recommended contraction pressure generated by the quadriceps femoris when the patient's knee joint contracts.

[0124] Furthermore, the effective counting module includes a real-time contraction pressure display device, a recommended contraction pressure display device, an effective contraction number display device and an effective number prompt device; the real-time contraction pressure display device is used to display the real-time contraction pressure; the recommended contraction pressure display device is used to display the recommended contraction pressure; the effective contraction number display device is used to display the effective contraction number, and the effective contraction number is the number of times the real-time contraction pressure is greater than the recommended contraction pressure and the contraction time is greater than the contraction time threshold; the effective number prompt device is used to prompt whether the real-time contraction pressure is greater than the recommended contraction pressure.

[0125] Furthermore, the pressure adaptive adjustment model calculation formula is:

[0126]

[0127] Among them, P recommended Indicates the updated recommended systolic pressure, P currentrepresents the real-time contraction pressure, α0 represents the adjustment coefficient, ln(·) represents the logarithmic function, e represents the natural exponent, T represents the contraction time, N represents the number of contractions, N effective Indicates the effective number of contractions.

[0128] Furthermore, if Figure 3 As shown, the monitoring method further includes:

[0129] S960: When the recommended contraction pressure is not within the user's comfort range, manually adjust the recommended contraction pressure.

[0130] Example 3

[0131] A patient suffers from knee joint injury due to excessive exercise. In order to recover as soon as possible, effective quadriceps contraction pressure is needed to control knee joint contraction and thus assist in rapid joint recovery. Therefore, a pressure monitoring system based on knee joint rehabilitation proposed in the present invention is adopted.

[0132] Table 1. First and second states of the slave sensing unit

[0133]

[0134] The first contraction pressure generated by the quadriceps femoris when the knee joint of a healthy person contracts was obtained by a pressure acquisition device, wherein the pressure acquisition device includes a stretch fabric, a surface electromyography sensor, and an IMU sensor group; the elastic coefficient of the stretch fabric is 120N / cm, and the IMU sensor group includes 1 main sensor unit and 8 slave sensor units. The slave sensor units are evenly and equidistantly distributed with a radius of 5cm with the main sensor unit as the center; the first state of the slave sensor unit in the healthy person's knee joint extension state and the second state of the slave sensor unit in the healthy person's knee joint contraction state are collected, wherein the data of the first state and the second state of the slave sensor unit are shown in Table 1; according to the tension calculation formula Calculation shows that the first contraction pressure at this moment is 55N.

[0135] The first contraction pressure data and first electromyographic feedback data generated by the quadriceps femoris during knee contraction of healthy subjects are collected and uploaded to the healthy subject database. Part of the data in the healthy subject database is shown in Table 2. Based on the first contraction pressure data and first electromyographic feedback data in the healthy subject database, the first contraction pressure threshold and the second contraction pressure threshold are obtained; the first contraction pressure threshold and the second contraction pressure threshold are obtained through normal distribution, and the first contraction pressure threshold P lower =μ-2σ, the second contraction pressure threshold P upper =μ+2σ, the first contraction pressure threshold is 30N, and the second contraction pressure threshold is 90N.

[0136] Table 2. Some first contraction pressure data and first electromyographic feedback data in the healthy subjects database

[0137] Healthy person number First contraction pressure (N) First EMG feedback (μV) 1 20 10 1 25 30 ... ... ... 1 60 150 1 65 160 ... ... ... 2 70 135 ... ... ...

[0138] Based on the first and second contraction pressure thresholds, the quadriceps contraction pressure during knee contraction was gradually increased in a gradient of 5 N. A second contraction pressure data set and a second electromyographic feedback data set were obtained. These data sets, along with the patient's baseline data, were uploaded to the patient database. Table 3 shows a portion of the patient database data. A graph factorization machine recommendation algorithm was used to obtain a recommended compression pressure based on the second contraction pressure data set, the second electromyographic feedback data set, and the patient's baseline data in the patient database. The patient's baseline data included an age of 28, male sex, weight of 75 kg, and no prior medical history. Based on the patient database and the graph factorization machine recommendation algorithm, a recommended quadriceps contraction pressure of 60 N was obtained.

[0139] During the implementation phase of the recommended contraction pressure, the pressure monitoring system based on knee joint rehabilitation proposed by the present invention provides real-time contraction pressure P current The contraction time T is 36 minutes, the number of contractions N is 150 times and the effective number of contractions N is 60N. effective The pressure is 120 times; a pressure adaptive adjustment model is established based on the pressing pressure, pressing time, pressing times and effective times in the knee joint rehabilitation stage, and the recommended pressing pressure is updated adaptively. The calculation formula of the pressure adaptive adjustment model is:

[0140]

[0141] Adjustment coefficient α0 = 0.8, substitute the recorded data to get:

[0142]

[0143] The patient's electromyographic feedback on the recommended contraction pressure is 100, and the patient's comfort range is [130, 200]. Therefore, the recommended contraction pressure can be manually adjusted to obtain a better experience.

[0144] Table 3. Partial data of patient database

[0145]

[0146] The recommended contraction pressures and the corresponding electromyographic feedback at the contraction pressures for a group of patients are shown in Table 4. As can be seen from the table, the recommended contraction pressures are all within the patients' comfort range, indicating that the system and method proposed in the present invention can accurately recommend quadriceps contraction pressures that meet the patients' needs based on the contraction pressure monitoring data and the patients' basic data.

[0147] Table 4. Recommended contraction pressure for patients and corresponding electromyographic feedback at contraction pressure

[0148]

[0149] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A pressure monitoring system based on knee joint rehabilitation, characterized in that: include: A first pressure acquisition module is used to acquire physiological data generated by the quadriceps femoris of a healthy person when the knee joint contracts through a pressure acquisition device, and upload the physiological data to a healthy person database; The physiological data generated by the quadriceps femoris during knee contraction in healthy subjects obtained through pressure acquisition equipment include: Wrapping the pressure collection device on the quadriceps femoris of a healthy person; collecting a first state of the slave sensor unit when the knee joint of a healthy person is in an extended state, and a second state of the slave sensor unit when the knee joint of a healthy person is in a contracted state; Obtaining a first contraction pressure using a pressure calculation model; Collect the first electromyographic feedback of the surface electromyographic sensor under the knee contraction state of healthy subjects; The first state includes a first distance from the slave sensing unit to the master sensing unit, and a first horizontal angle and a first vertical angle of the slave sensing unit relative to the master sensing unit; the second state includes a second distance from the slave sensing unit to the master sensing unit, and a second horizontal angle and a second vertical angle of the slave sensing unit relative to the master sensing unit; the calculation formula of the pressure calculation model is: ; Wherein, T represents the first contraction pressure, n represents the number of the slave sensing units, k represents the tensile elastic coefficient of the stretched fabric, and i represents the index number of the slave sensing unit. represents the distance change between the first distance and the second distance from the sensing unit i, represents the horizontal change between the first horizontal angle and the second horizontal angle of the slave sensing unit i, indicating vertical changes of the first vertical angle and the second vertical angle of the slave sensing unit i; the physiological data including a first contraction pressure and a first myoelectric feedback; a pressure analysis module, configured to obtain a first contraction pressure threshold and a second contraction pressure threshold according to the physiological data; a second pressure acquisition module, configured to gradually increase the contraction pressure at the quadriceps femoris of the patient according to the first contraction pressure threshold and the second contraction pressure threshold, obtain a second contraction pressure data set and a second myoelectric feedback data set, and upload the second contraction pressure data set, the second myoelectric feedback data set and the patient's basic data to a patient database; a pressure recommendation module, configured to obtain a recommended contraction pressure using a graph factorization machine algorithm based on the second contraction pressure data group, the second electromyography feedback data group, and the patient basic data in the patient database; an effective counting module, configured to record the real-time contraction pressure, contraction time, contraction times, and effective contraction times of the quadriceps femoris during the implementation phase of the recommended contraction pressure, and to display the real-time contraction pressure, the recommended contraction pressure, and the effective contraction times; The adaptive adjustment module is used to establish a pressure adaptive adjustment model according to the real-time contraction pressure, the contraction time, the number of contractions and the effective number of contractions, and adaptively update the recommended contraction pressure.

2. A pressure monitoring system based on knee joint rehabilitation according to claim 1, characterized in that: The pressure acquisition device includes a stretch fabric, a surface electromyography sensor and an IMU sensor group. The IMU sensor group includes a main sensor unit and multiple slave sensor units. The slave sensor units are evenly and equidistantly distributed with the main sensor unit as the center of the circle.

3. A pressure monitoring system based on knee joint rehabilitation according to claim 1, characterized in that: The first contraction pressure threshold and the second contraction pressure threshold are obtained through normal distribution. , the second contraction pressure threshold ,in represents the mean systolic pressure, Indicates the pressure standard deviation.

4. A pressure monitoring system based on knee joint rehabilitation according to claim 1, characterized in that: Obtaining the recommended contraction pressure using the graph factorization machine algorithm includes: Constructing a graph structure according to the second contraction pressure data set, the second myoelectric feedback data set and the patient basic data in the patient database; Using a graph neural network to embed the nodes in the graph structure, and converting the second contraction pressure data set, the second electromyography feedback data set, and the patient basic data into a low-dimensional embedding vector; Based on the embedded representation, the factorization machine algorithm is used to learn the feature interaction information between features; The low-dimensional embedding vector processed by the graph neural network is integrated with the feature interaction information of the factorization machine to predict the patient's electromyographic feedback data under different contraction pressures and obtain the predicted electromyographic feedback data; Minimizing the difference between the predicted myoelectric feedback data and the second myoelectric feedback data group by optimizing a loss function; The patient's basic data is input, and a graph factorization machine algorithm is used to obtain the recommended contraction pressure generated by the quadriceps femoris when the patient's knee joint contracts.

5. The pressure monitoring system based on knee joint rehabilitation according to claim 1, characterized in that: The effective counting module includes a real-time contraction pressure display device, a recommended contraction pressure display device, an effective contraction number display device and an effective number prompt device; the real-time contraction pressure display device is used to display the real-time contraction pressure; the recommended contraction pressure display device is used to display the recommended contraction pressure; The effective contraction number display device is used to display the effective contraction number, which is the number of times the real-time contraction pressure is greater than the recommended contraction pressure and the contraction time is greater than the contraction time threshold; The effective number prompting device is used to prompt whether the real-time contraction pressure is greater than the recommended contraction pressure.

6. A pressure monitoring system based on knee joint rehabilitation according to claim 1, characterized in that: The monitoring system further comprises: A manual adjustment module is used to manually adjust the recommended contraction pressure when the recommended contraction pressure is not within the user's comfort range. The manual adjustment module includes an adjustment device.

7. A pressure monitoring method based on knee joint rehabilitation, characterized in that: include: Acquire physiological data generated by the quadriceps femoris when the knee joint of a healthy person contracts, and upload the physiological data to a healthy person database; The physiological data generated by the quadriceps femoris during knee contraction in healthy subjects obtained through pressure acquisition equipment include: Wrapping the pressure collection device on the quadriceps femoris of a healthy person; collecting a first state of the slave sensor unit when the knee joint of a healthy person is in an extended state, and a second state of the slave sensor unit when the knee joint of a healthy person is in a contracted state; Obtaining a first contraction pressure using a pressure calculation model; Collect the first electromyographic feedback of the surface electromyographic sensor under the knee contraction state of healthy subjects; The first state includes a first distance from the slave sensing unit to the master sensing unit, and a first horizontal angle and a first vertical angle of the slave sensing unit relative to the master sensing unit; the second state includes a second distance from the slave sensing unit to the master sensing unit, and a second horizontal angle and a second vertical angle of the slave sensing unit relative to the master sensing unit; the calculation formula of the pressure calculation model is: Wherein, T represents the first contraction pressure, n represents the number of the slave sensing units, k represents the tensile elastic coefficient of the stretched fabric, and i represents the index number of the slave sensing unit. represents the distance change between the first distance and the second distance from the sensing unit i, represents the horizontal change between the first horizontal angle and the second horizontal angle of the slave sensing unit i, indicating vertical changes of the first vertical angle and the second vertical angle of the slave sensing unit i; the physiological data including a first contraction pressure and a first myoelectric feedback; Obtaining a first contraction pressure threshold and a second contraction pressure threshold according to the first contraction pressure and the first myoelectric feedback; Gradual increase of the contraction pressure at the quadriceps femoris of the patient according to the first contraction pressure threshold and the second contraction pressure threshold, obtaining a second contraction pressure data set and a second myoelectric feedback data set, and uploading the second contraction pressure data set, the second myoelectric feedback data set and the patient's basic data to a patient database; Obtaining a recommended contraction pressure using a graph factorization machine algorithm according to the second contraction pressure data set, the second electromyography feedback data set, and the patient's basic data in the patient database; The real-time contraction pressure, contraction time, number of contractions and effective number of contractions of the quadriceps femoris during the implementation of the recommended contraction pressure are recorded, and a pressure adaptive adjustment model is established based on the real-time contraction pressure, contraction time, number of contractions and effective number of contractions to adaptively update the recommended pressing pressure.

8. The pressure monitoring method based on knee joint rehabilitation according to claim 7, characterized in that: The method further comprises: When the recommended contraction pressure is not within the user's comfort range, manually adjust the recommended contraction pressure.

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