An attitude and wear monitoring system for limb artificial joint motion signals

Abstract

The present invention relates to the technical field of medical device data monitoring. The present invention provides a posture and wear monitoring system for the movement signals of artificial joints of the four limbs, comprising: a movement posture recognition module: collecting the movement signal data of the artificial joint, and based on the collected movement signal data, performing judgment and analysis on the movement posture according to the movement posture judgment model to identify the movement posture type; wherein, the movement posture types include: high-load movement postures, low-load movement postures, and static postures; a monitoring frequency control module: extracting movement features. The monitoring frequency control module of the present invention dynamically adjusts the wear monitoring frequency according to the movement posture and movement intensity, introduces a joint fatigue factor to comprehensively measure the movement intensity, increases the monitoring frequency during high-load movement to timely capture rapid wear changes, reduces or stops monitoring during low-load movement or at rest, reduces resource waste, realizes precise and efficient monitoring, and improves the accuracy and timeliness of monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of medical device data monitoring, and more particularly to a system for monitoring the posture and wear of the movement signals of artificial joints of the four limbs. Background Art

[0002] With the rapid development of medical technology, total joint arthroplasty of the four limbs has become an effective means for treating severe joint diseases, such as osteoarthritis, rheumatoid arthritis, traumatic arthritis, etc., greatly improving the joint function and quality of life of patients. Inevitably, wear problems will occur during the long-term use of artificial joints, which will not only affect the service life of the joints, but may also lead to complications such as joint loosening, pain, and infection. In severe cases, even a second operation may be required;

[0003] However, most of the existing monitoring technologies rely on either regular imaging examinations or fixed monitoring frequencies. Although the method of regular imaging examinations can reflect the wear condition of the joints to a certain extent, imaging examinations are static detection means and can only provide morphological information of the joints at specific time points, and cannot reflect the real-time wear process of the joints during daily activities; the method of fixed monitoring frequency fails to fully consider the differences in the joint wear rates of patients in different movement postures. When the human body is performing high-load exercises, the artificial joints are subjected to greater pressure and friction, and the wear rate will increase significantly. While during low-load exercises or at rest, the joint wear is relatively slow. Using a unified monitoring frequency cannot timely capture the rapid wear changes of the joints during high-load exercises, easily resulting in missed diagnoses and delayed treatments of wear conditions, and affecting the accuracy and effectiveness of the monitoring results;

[0004] Therefore, the present invention provides a system for monitoring the posture and wear of the movement signals of artificial joints of the four limbs. Summary of the Invention

[0005] In order to make up for the deficiencies of the prior art and solve at least one of the technical problems proposed in the background art, the monitoring frequency can be adjusted according to the actual movement postures of patients, early signs of joint wear can be detected in a timely manner, the changing trend of the wear degree and the remaining service life can be predicted, providing accurate and reliable basis for doctors to formulate personalized treatment plans and rehabilitation programs, thereby improving the use effect of artificial joints and the quality of life of patients;

[0006] The technical solution adopted by the present invention to solve its technical problems is: a system for monitoring the posture and wear of the movement signals of artificial joints of the four limbs, comprising:

[0007] A movement posture recognition module: collecting the movement signal data of the artificial joints, and based on the collected movement signal data, analyzing and judging the movement posture according to the movement posture judgment model to identify the movement posture type;

[0008] Among them, the types of motion postures include: high-load motion postures, low-load motion postures, and static postures;

[0009] Monitoring frequency control module: Extract motion features, based on the motion features, introduce joint fatigue factors for data fusion processing to obtain a motion intensity coefficient, combine the motion intensity coefficient with the real-time wear monitoring frequency, for different types of motion postures, update the subsequent wear monitoring frequency, and process the monitoring of the artificial joint based on the new wear monitoring frequency to output the wear degree;

[0010] Wear prediction module: By continuously judging whether the wear degree exceeds the wear degree warning value, if it exceeds the wear degree warning value, obtain the corresponding warning time point to predict the remaining service life of the artificial joint in the future period.

[0011] The beneficial effects of the present invention are as follows:

[0012] The motion posture recognition module of the present invention collects multi-source data through a multi-modal sensor array, fuses information such as acceleration, angular velocity, myoelectric intensity, and pressure, reduces the one-sidedness of a single sensor, and uses an adaptive feature weighting algorithm to adapt to the non-linear and non-stationary characteristics of the signal, improving the accuracy of posture recognition;

[0013] The monitoring frequency control module of the present invention dynamically adjusts the wear monitoring frequency according to the motion posture and motion intensity, introduces joint fatigue factors to comprehensively measure the motion intensity, increases the monitoring frequency during high-load motion to capture rapid wear changes in a timely manner, reduces or stops monitoring during low-load motion or static state to reduce resource waste, realizes accurate and efficient monitoring, and improves the accuracy and timeliness of monitoring;

[0014] The wear prediction module of the present invention obtains warning and warning time points by comparing the real-time wear degree with the preset value, analyzes the change trend of the basic wear data, predicts the remaining service life of the artificial joint. Based on this, doctors can formulate personalized treatment plans, such as deciding whether to perform surgery in advance, adjusting the rehabilitation plan, and can also remind patients to pay attention to the motion intensity and have regular check-ups. It can not only improve the use effect of artificial joints and improve the quality of life of patients, but also help medical institutions reasonably allocate medical resources and make preparations for surgery in advance, playing an important role in medical decision-making and patient management. Description of the Drawings

[0015] The present invention will be further described below with reference to the drawings.

[0016] Figure 1 It is a framework diagram of a posture and wear monitoring system for a four-limb artificial joint motion signal of the present invention;

[0017] Figure 2It is a flowchart of the steps of a method for monitoring the posture and wear of the movement signals of artificial joints of the four limbs according to the present invention. Detailed implementation mode

[0018] In order to make the technical means, creative features, achieved purposes and effects realized by the present invention easy to understand, the present invention will be further described below in conjunction with the specific implementation modes.

[0019] Example 1

[0020] Please refer to Figure 1 As shown, a system for monitoring the posture and wear of the movement signals of artificial joints of the four limbs according to an embodiment of the present invention specifically includes:

[0021] A motion posture recognition module 10: collecting the motion signal data of the artificial joint, and based on the collected motion signal data, judging and analyzing the motion posture according to the motion posture judgment model to identify the motion posture type;

[0022] Among them, the motion posture types include: high-load motion postures, low-load motion postures, and static postures;

[0023] For the motion posture recognition module, the execution process is as follows:

[0024] It should be noted in advance that when implanting the joint, the monitoring computer screen can display the position and angle of the joint prosthesis, which can provide immediate joint posture information and support for the surgeon;

[0025] Multimodal sensors (such as accelerometers, gyroscopes, etc.) on the artificial joint need to be deployed on the main motion axes of the joint. The accurate installation of the prosthesis is the premise for the sensors to correctly collect kinematic and mechanical signals. If the position or angle of the prosthesis deviates, it may cause the sensor data to be distorted, thereby affecting the accuracy of the subsequent motion posture judgment model;

[0026] Install a multimodal sensor array on the artificial joints of the four limbs to collect the motion signal data of the artificial joints. The sensors include: accelerometers, gyroscopes, electromyography sensors, and pressure sensors; the motion signals of the artificial joints include: mechanical signals, kinematic signals, and electromyography signals; the signal data includes but is not limited to: acceleration data, angular velocity data, electromyography intensity data, and pressure data;

[0027] Optionally, for kinematic signal acquisition: High-precision triaxial accelerometers and gyroscopes are used, which are respectively deployed on the main motion axes of the artificial joint to monitor the linear acceleration and angular velocity changes of the joint in three-dimensional space in real time; for electromyogram (EMG) signal acquisition: EMG sensors are arranged in the form of a differential electrode array at the muscle bellies of the main muscle groups driving the artificial joint. By using bioelectric amplification and filtering technologies, weak electrical signals generated during muscle contraction are captured. After feature extraction, the degree of muscle activation (EMG intensity), contraction timing, and cooperative force generation pattern can be quantified; for mechanical signal acquisition: Miniature pressure sensors are implanted at the interface between the artificial joint and the bone, the contact part of the joint cavity, or integrated into the key stress-bearing areas of the joint prosthesis. Based on the piezoresistive or piezoelectric effect, the pressure distribution and stress changes of the joint during loading, friction, and impact are sensed in real time;

[0028] By arranging the sensors on the limb around the artificial joint, signals related to the movement of the artificial joint can be comprehensively and accurately acquired;

[0029] It should be noted that for the data collected by each sensor, a synchronous acquisition circuit and software algorithm combination are required to synchronize the data of each sensor in time;

[0030] Among them, the synchronous acquisition circuit includes but is not limited to: synchronous control circuit: The synchronous control logic is implemented using a field-programmable gate array (FPGA) or a microcontroller (MCU). Through programming configuration, the FPGA or MCU can generate synchronous trigger signals according to the clock signal and send them to each sensor simultaneously to ensure that they start sampling at the same moment; the software algorithm includes but is not limited to: timestamp marking algorithm: At the software level, accurate timestamps are added to the data collected by each sensor and stored and transmitted together with the data;

[0031] Preprocessing is performed on the artificial joint motion signal data collected by different sensors. Among them, the preprocessing process includes: denoising processing and normalization processing;

[0032] Specifically, the denoising process includes: using a complementary filtering algorithm to fuse and denoise the data from the accelerometer and gyroscope. The accelerometer data is accurate in the low-frequency band but is susceptible to vibration and shock. The gyroscope data is stable in the high-frequency band but has an integration drift problem. The complementary filtering algorithm combines the advantages of both. By fusing the data from the accelerometer and gyroscope, it relies on accelerometer data at low frequencies and gyroscope data at high frequencies, thus effectively removing noise and reducing drift. For the electromyogram signal, a wavelet packet decomposition and reconstruction method is used for denoising. Wavelet packet decomposition can decompose the signal into different frequency sub-bands. According to the frequency characteristics of the electromyogram signal, appropriate sub-bands are selected for reconstruction to remove noise and interference signals while retaining the characteristic information of the electromyogram signal. The pressure sensor data is subject to external environmental interference, and a median filtering algorithm can be selected to denoise it. Median filtering is a non-linear filtering method that sorts the data within the window and takes the median value as the output, which can effectively remove impulse noise and salt-and-pepper noise while retaining the edge information of the signal;

[0033] All the denoised data is normalized, and the minimum-maximum normalization method can be selected for the normalization process;

[0034] Feature extraction and fusion are performed on the artificial joint motion signal data after preprocessing;

[0035] For the feature extraction stage, the features extracted from the processed data are divided into time-domain features, frequency-domain features, time-frequency features, and combined features. Time-domain features include statistical features such as mean, variance, and peak value, which are used to describe the basic statistical characteristics and fluctuations of the signal. Frequency-domain features are obtained through Fourier transform and can reflect the distribution of data in the frequency domain, helping to distinguish different motion postures. Time-frequency features use wavelet transform to analyze the common characteristics of data in the time domain and frequency domain, providing a more detailed analysis of time variation and frequency characteristics, and having a good effect on processing non-stationary signals (such as electromyogram signals);

[0036] For the feature fusion stage, a feature fusion method based on principal component analysis (PCA) is adopted;

[0037] The fused features are input into the motion posture judgment model to output the real-time motion posture type;

[0038] Furthermore, the training process of the motion posture judgment model includes:

[0039] Obtain a historical data set, including the signals collected by the accelerometer, gyroscope, electromyogram sensor, and pressure sensor at different time points, as well as the corresponding actual motion posture types;

[0040] Divide the historical dataset into a training set, a validation set, and a test set, with a division ratio of 70% for the training set, 15% for the validation set, and 15% for the test set;

[0041] Preprocess the sensor signal data in the training set, validation set, and test set, including denoising and normalization; extract and fuse features from the preprocessed data;

[0042] Use the data in the training set to calculate the importance score of each feature as the initial feature weight vector; during the model training process, for each node sample set in the training set, calculate the discrimination of each feature in the sample set, combine the adaptive adjustment factor, calculate the adaptive weight according to the feature discrimination and the initial weight, and perform normalization processing;

[0043] Optionally, obtain the sum of the Gini impurity reduction values of each feature in all decision trees in the random forest through the Gini impurity reduction method as the feature importance, and then normalize the feature importance score to obtain the initial feature weight; measure the discrimination by calculating the variance ratio of the feature in different category samples, introduce an adaptive adjustment factor (set by those skilled in the art based on previous research experience), multiply the adaptive factor, the initial weight, and the discrimination to obtain the adaptive weight, and perform normalization processing on the adaptive weight;

[0044] Thus, it is possible to perform adaptive feature weighting in the random forest algorithm, improve the model's discrimination ability for different features, and thus improve the accuracy of the recognition of the movement postures of artificial joints of the limbs;

[0045] Select the random forest model, preferentially select the features with higher weights for node splitting according to the adaptive weight of each feature, and train the model with the training set data; input the validation set data into the training model, and evaluate the model performance according to the difference between the model output result and the actual label (the evaluation metrics can include at least one of accuracy, precision, and recall). If the performance does not meet the expectation, adjust the model parameters (such as the number of decision trees and the maximum depth of the random forest), and repeat the training process until the model reaches the best performance on the validation set to obtain the movement posture judgment model;

[0046] In summary, the movement posture recognition module deploys a multi-modal sensor array around the artificial joints of the limbs, collects the mechanical signals, kinematic signals, and electromyography signals of the artificial joints in real time. After synchronous acquisition and data preprocessing, it extracts time-domain, frequency-domain, and time-frequency domain features and fuses them through principal component analysis, and finally inputs them into a random forest model based on adaptive feature weighting to dynamically recognize the current movement posture type;

[0047] It has the following effects: integrating multi-source data of acceleration, angular velocity, myoelectric intensity, and pressure, covering the mechanics, kinematics, and muscle activation state of joint movement, reducing the one-sidedness of a single sensor, and improving the accuracy of posture recognition;

[0048] Through an adaptive feature weighting algorithm, that is, combining Gini impurity and discrimination to dynamically adjust feature weights, adapting to the non-linear and non-stationary characteristics of signals in different motion scenarios, ensuring robustness in complex motion postures;

[0049] Identifying the type of motion posture can provide a core basis for the monitoring strategy. When in a static posture, it reduces the acquisition of invalid data. If it is identified as a high-load or low-load motion posture, it triggers the motion intensity assessment process, dynamically adjusts the wear monitoring frequency (increasing the frequency in high-load situations and decreasing the frequency in low-load situations), and conducts on-demand monitoring to ensure high-frequency real-time tracking in high-risk scenarios and energy-saving efficiency in low-risk scenarios;

[0050] Monitoring frequency control module 20: Extracts motion features, based on the motion features, introduces a joint fatigue factor for data fusion processing to obtain a motion intensity coefficient, combines the motion intensity coefficient with the real-time wear monitoring frequency, updates the subsequent wear monitoring frequency for different types of motion postures, and processes the monitoring of the artificial joint based on the new wear monitoring frequency, outputting the degree of wear;

[0051] For the monitoring frequency control module, the execution process is as follows:

[0052] Specifically, the process of updating the wear monitoring frequency is as follows:

[0053] If the motion posture type is a static posture, stop wear monitoring;

[0054] If the motion posture type is a high-load motion posture or a low-load motion posture, update and adjust the wear monitoring frequency based on the motion intensity;

[0055] Specifically, set a sliding time window, and obtain pressure data, linear acceleration data, and myoelectric intensity data within each time window;

[0056] Take the mean value of the pressure data within the time window as the joint bearing strength value, and take the peak value of the pressure data within the time window as the joint instantaneous load value;

[0057] Synthesize the linear acceleration data in three-dimensional space to obtain the synthesized linear acceleration, take the mean value of the synthesized linear acceleration within the time window as the motion amplitude value, and take the peak value of the synthesized linear acceleration within the time window as the vibration amplitude value;

[0058] Take the root mean square of the myoelectric intensity data within the time window as the muscle activation intensity value;

[0059] Normalize the joint load-bearing strength value, joint instantaneous load value, movement amplitude value, vibration amplitude value, and muscle activation strength value to obtain a motion intensity feature vector; the normalization process can select min-max normalization;

[0060] Train a differentiated dynamic weight matrix based on the historical dataset, so as to reflect the importance of different data types under the corresponding motion posture types. In this embodiment, it is mainly high-load motion postures and low-load motion postures;

[0061] The training process of the weight matrix is as follows:

[0062] Based on the multi-modal sensor data related to high-load motion postures and low-load motion postures in the historical dataset, including pressure data, acceleration data, angular velocity data, electromyogram intensity data, etc., and the corresponding actual motion intensity values or labels;

[0063] Determine the features involved in weight calculation, including joint load-bearing strength value (pressure mean), joint instantaneous load value (pressure peak), movement amplitude value (synthetic linear acceleration mean), vibration amplitude value (synthetic linear acceleration peak), and muscle activation strength value (root mean square of electromyogram intensity);

[0064] Adopt appropriate machine learning methods (such as linear regression, support vector machine, neural network) or statistical methods to construct a model with motion intensity evaluation as the goal, with the above features as the input and motion intensity-related indicators as the output;

[0065] During the model training process, adjust the weights of each feature through an optimization algorithm (such as gradient descent) so that the model output can accurately reflect the actual motion intensity under different motion postures; for high-load motion postures and low-load motion postures, train the corresponding weight matrices respectively to reflect the importance differences of each data type under different postures;

[0066] Exemplarily, under high-load motion postures, the pressure data and electromyogram intensity data may have higher weights; while under low-load motion postures, the weight of the linear acceleration data may be relatively lower;

[0067] Verification and adjustment: Use the validation set to evaluate the performance of the trained weight matrix, and adjust the model parameters or weight matrix according to the evaluation results (such as mean square error, correlation coefficient, etc.) until the weight matrix can accurately reflect the importance of different data types under the corresponding motion posture types and meet the motion intensity evaluation requirements;

[0068] Calculate the joint fatigue factor through the spectral characteristics of the electromyogram signal. The calculation process of the joint fatigue factor is as follows: extract the proportion of the medium-frequency band energy of the electromyogram signal, extract the minimum and maximum medium-frequency band energies in the historical data, and perform a difference calculation to obtain the extreme difference of the historical medium-frequency band energy. Calculate the difference between the proportion of the medium-frequency band energy and the small medium-frequency band energy, and then calculate the ratio with the extreme difference of the historical medium-frequency band energy to obtain the joint fatigue factor;

[0069] Among them, the specific frequency range of the medium-frequency band can be 50-150 Hz;

[0070] The role of introducing the joint fatigue factor is as follows: The exercise intensity not only depends on external physical data such as pressure and acceleration, but the muscle fatigue state is also a key factor affecting joint wear. When the muscle is fatigued, its protective and supporting effects on the joint will weaken, resulting in an increase in the additional stress borne by the joint, thereby accelerating joint wear. By introducing the joint fatigue factor, this internal factor of muscle fatigue can be incorporated into the evaluation system of exercise intensity, making the measurement of exercise intensity more comprehensive and accurate, and thus more accurately reflecting the actual impact of exercise on joints;

[0071] When updating the wear monitoring frequency based on the exercise intensity, the joint fatigue factor is used as an adjustment parameter. When the joint fatigue factor is relatively high, it indicates that the joint is in a relatively vulnerable state and the wear risk increases. It is necessary to increase the monitoring frequency to more timely detect changes in joint wear;

[0072] Perform data fusion by combining the exercise intensity feature vector, dynamic weight matrix, and joint fatigue factor, and calculate the exercise intensity coefficient. The calculation process of the exercise intensity coefficient is as follows:

[0073] Perform weighted summation on the exercise intensity feature vector and the weight vector to obtain the basic exercise intensity. Calculate the exercise intensity coefficient k based on the basic exercise intensity and the joint fatigue factor; The weight coefficients of the basic exercise intensity and the joint fatigue factor are calculated by the entropy weight method, and the entropy weight method is common knowledge that can be implemented by those skilled in the art;

[0074] Obtain the real-time wear monitoring frequency , and calculate the updated new wear monitoring frequency;

[0075] For high-load exercise postures, the calculation formula is: ; Among them, is the new wear monitoring frequency, and k is the exercise intensity coefficient; For high-load exercise postures, it is necessary to increase the monitoring frequency;

[0076] For low-load exercise postures, the calculation formula is: ; Among them, is the new wear monitoring frequency, and k is the exercise intensity coefficient; for low-load exercise postures, the monitoring frequency needs to be reduced;

[0077] It should be noted that there is a maximum and minimum range for the monitoring frequency setting, and the updated monitoring frequency needs to be within this range. If the calculated monitoring frequency is not within this range, then the extreme value corresponding to the maximum or minimum range is selected;

[0078] Specifically, the process of outputting the wear degree is as follows:

[0079] The wear amount is calculated using a classical wear model; optionally, the classical wear model is the Archard model;

[0080] Among them, the wear amount The calculation formula is: ; where M is the material wear coefficient, which is a preset parameter set according to the characteristics of the artificial joint material, represents the average pressure in the i-th monitoring period, represents the sliding distance in the i-th monitoring period, and T is the monitoring period;

[0081] The sliding distance is the vector sum of the rotational displacement and the total translational displacement of the artificial joint;

[0082] The angular velocity in the monitoring period is integrated over time to obtain the cumulative rotation angle of the joint. The product of the cumulative rotation angle and the radius of the joint contact surface (determined by the design parameters of the artificial joint) is the rotational displacement; the accelerometer collects three-dimensional linear acceleration signals, and the three-dimensional translational displacements are calculated through two integrations respectively. The three-dimensional translational positions are synthesized to obtain the total translational displacement;

[0083] For the wear amount calculated in each monitoring period, when comparing adjacent periods, it is used as the wear increment of this period relative to the previous period, that is, the wear increment in the i-th monitoring period is the wear amount in the i-th monitoring period minus the wear amount in the (i - 1)-th monitoring period ;

[0084] Calculate the wear increment for each monitoring period , and accumulate the historical wear increments starting from the initial monitoring period to obtain the real-time wear degree. The calculation formula is: ; where is the real-time wear degree, is the basic wear degree at the initial installation, n is the monitoring period, and i represents the i-th monitoring period;

[0085] In summary, the monitoring frequency control module works as follows: First, it sets a sliding time window according to the type of motion posture, obtains motion characteristics such as joint bearing strength value, joint instantaneous load value, motion amplitude value, vibration amplitude value, and muscle activation strength value, and obtains a motion intensity feature vector through normalization processing. Then, it trains a differential dynamic weight matrix based on the historical data set, calculates the joint fatigue factor through the spectral characteristics of the electromyogram signal, and fuses the motion intensity feature vector, the weight matrix, and the joint fatigue factor to calculate the motion intensity coefficient, thereby updating the wear monitoring frequency. The high-load motion posture increases the frequency, the low-load motion posture decreases the frequency, and the static posture stops monitoring. At the same time, the Archard model is used to calculate the wear amount, and the wear increment of the real-time motion is accumulated to obtain the real-time wear degree;

[0086] Therefore, the wear monitoring frequency can be dynamically adjusted according to different motion postures and motion intensities to achieve on-demand monitoring. When the motion is at a high load, the monitoring frequency is increased, which can timely capture rapid wear changes, reduce missed diagnoses and delayed treatments. When the motion is at a low load or static, the monitoring is reduced or stopped, which can reduce unnecessary data collection and achieve energy-saving and efficient monitoring. Introducing the joint fatigue factor makes the evaluation of motion intensity more comprehensive and accurate, and thus can more accurately reflect the actual impact of motion on joint wear, improving the accuracy and effectiveness of monitoring;

[0087] Wear prediction module 30: By continuously judging whether the wear degree exceeds the wear degree warning value, if it exceeds the wear degree warning value, obtain the corresponding warning time point, and predict the remaining service life of the artificial joint in the future time period;

[0088] For the wear prediction module, the execution process is as follows:

[0089] During the process of monitoring the wear of the artificial joint, obtain the real-time wear degree, compare it with the wear degree warning value. If the real-time wear degree is less than the wear warning value, continue the wear monitoring;

[0090] If the real-time wear degree is greater than or equal to the wear degree warning value, obtain the corresponding warning time point; and obtain the time point corresponding to the wear degree warning value as the warning time point;

[0091] Among them, it should be noted that the wear degree warning value is the mean of the wear degree early warning value and the wear degree threshold value; the wear degree early warning value is a preset reference value of the wear degree, which is used to indicate that the wear state of the artificial joint begins to enter the range that needs attention. For example, when the wear degree is close to the early warning value, it means that there may be an abnormal change trend in the joint wear. Although it has not reached the dangerous level, it is necessary to increase the monitoring frequency and closely observe the development of the wear situation in order to detect potential problems in time. The wear degree threshold value is the limit value reached by the wear of the artificial joint. Once the wear degree reaches or exceeds this threshold value, the performance of the artificial joint will be seriously affected, and problems such as joint loosening, increased pain, and functional limitation may occur, and even a second operation may be required to replace the joint;

[0092] Optionally, after the warning time point, the monitoring frequency can be increased, so as to closely monitor the development of the wear situation, which helps to detect potential problems early and prevent the occurrence of serious joint complications;

[0093] Obtain the wear degree corresponding to the early warning time point to the warning time point as the basic wear degree data;

[0094] Conduct a trend analysis on the basic wear degree data, including the judgment of whether the basic wear degree data is linearly changing or non-linearly changing;

[0095] Optionally, use the methods of statistical fitting and residual analysis to make a judgment, and perform linear fitting and non-linear fitting on the basic wear degree data respectively;

[0096] Perform a univariate linear regression on the basic wear data, fit a univariate linear regression equation, and calculate the sum of squared linear residuals;

[0097] At the same time, perform a quadratic polynomial fitting on the basic wear data and calculate the sum of squared non-linear residuals; in addition to quadratic polynomial fitting, exponential fitting can also be used;

[0098] Compare the linear fitting and the non-linear fitting through the AIC information criterion. Among them, the AIC information criterion, that is, the Akaike information criterion, is a standard for measuring the goodness of fit of a statistical model. Since it was founded and developed by the Japanese statistician Hirotsugu Akaike, it is also called the Akaike information criterion. It is based on the concept of entropy and can balance the complexity of the estimated model and the goodness of fit of this model to the data;

[0099] If the sum of squared non-linear residuals is less than the sum of squared linear residuals and the AIC value corresponding to the non-linearity is smaller, it is determined to be non-linearly changing. If the sum of squared residuals is randomly distributed and there is no obvious curvature, it is determined to be linearly changing;

[0100] It should be noted that the sum of squared residuals is an index to measure the degree of difference between the predicted values and the actual values of the model. When fitting the basic wear degree data, whether it is linear fitting or non - linear fitting, predicted values will be generated. The difference between the predicted value and the actual wear degree data is the residual. Summing the squares of all residuals gives the sum of squared residuals. When the non - linear sum of squared residuals is less than the linear sum of squared residuals, it means that the predicted values of the non - linear model are generally closer to the actual wear data than those of the linear model, that is, the non - linear model has a better fitting effect on the data. If the sum of squared residuals shows a random distribution and no obvious curvature, it indicates that the linear model can reasonably describe the variation law of the data. In this case, the trend of data change can be represented by a simple linear relationship, so it is determined as a linear change;

[0101] For the basic wear degree showing a linear change, it means that within the time period from the warning time point to the alarm time point, the wear rate of the artificial joint is relatively stable. The future wear situation can be predicted and the remaining life can be calculated according to the corresponding stable wear rate;

[0102] The linear prediction equation is obtained by least - squares fitting, and the slope of the linear prediction equation is the wear rate. Calculate the difference between the wear degree threshold and the wear degree value corresponding to the warning time point, and then calculate the ratio of the difference to the wear rate to obtain the remaining service life;

[0103] For the basic wear degree showing a non - linear change, when the basic wear degree shows a non - linear change, the wear rate is not constant and may change with time;

[0104] The non - linear prediction equation is obtained by quadratic polynomial fitting or exponential fitting. According to the actual basic wear degree data, by comparing the sum of squared residuals and the AIC value of different non - linear models, select the model with the best fitting effect. For example, select the model with a smaller sum of squared residuals and a lower AIC value;

[0105] Based on the non - linear prediction equation, taking the wear degree threshold as known, calculate the corresponding predicted time value, and then calculate the difference between the predicted time value and the alarm time value to obtain the remaining service life;

[0106] Based on the remaining service life, doctors can formulate personalized treatment plans according to the remaining life, such as whether to perform surgery in advance, adjust the rehabilitation plan, and can remind patients to pay attention to the exercise intensity, reduce high - load activities, have regular check - ups, predict the replacement time, and reasonably allocate resources;

[0107] In summary, the wear prediction module first compares the real-time wear degree with the preset wear warning value. If it exceeds the warning value, it obtains the corresponding warning time point and alarm time point, and extracts the basic wear data between the two time points. Subsequently, through statistical fitting, residual analysis, and AIC information criterion, it determines whether the wear trend is linear or non-linear. If it is a linear change, it fits the wear rate by the least squares method and calculates the remaining life. If it is a non-linear change, it selects the non-linear model with the best fitting effect to predict the time to reach the wear threshold, and finally outputs the remaining service life.

[0108] Based on the dual determination of the wear warning value and the warning value, the abnormal wear stage is identified in advance. Then, according to the change trend (linear or non-linear) of the basic wear degree data, the corresponding fitting method is used to predict the remaining service life of the artificial joint, enabling doctors and patients to know the joint status in advance and providing a key basis for subsequent medical decisions.

[0109] Embodiment 2

[0110] The present application also provides a method for monitoring the posture and wear of the motion signal of a four-limbed artificial joint. This method is applicable to the system for monitoring the posture and wear of the motion signal of this four-limbed artificial joint. Please refer to Figure 2 As shown, it is a schematic flowchart of a method for monitoring the posture and wear of the motion signal of a four-limbed artificial joint provided by the present application, including the following steps:

[0111] Step S100: Collect the motion signal data of the artificial joint, and based on the collected motion signal data, perform judgment and analysis on the motion posture according to the motion posture judgment model to identify the motion posture type.

[0112] Step S200: High-load motion posture, low-load motion posture, and static posture;

[0113] Monitoring frequency control module: Extract motion features, according to the motion features, introduce a joint fatigue factor for data fusion processing to obtain a motion intensity coefficient, combine the motion intensity coefficient with the real-time wear monitoring frequency, and update the subsequent wear monitoring frequency for different motion posture types. Based on the new wear monitoring frequency, the monitoring of the artificial joint is processed, and the wear degree is output.

[0114] Step S300: By continuously judging whether the wear degree exceeds the wear degree warning value, if it exceeds the wear degree warning value, obtain the corresponding warning time point, and predict the remaining service life of the artificial joint in the future period.

[0115] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification is only to illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. An attitude and wear monitoring system for limb artificial joint motion signals, characterized in that: Including: A motion posture recognition module: collecting motion signal data of an artificial joint, and based on the collected motion signal data, judging and analyzing the motion posture according to a motion posture judgment model to identify the type of motion posture; Among them, the types of motion postures include: high-load motion postures, low-load motion postures, and static postures; A monitoring frequency control module: extracting motion features, according to the motion features, introducing a joint fatigue factor for data fusion processing to obtain a motion intensity coefficient, combining the motion intensity coefficient with the real-time wear monitoring frequency, for different types of motion postures, updating the subsequent wear monitoring frequency, and processing the monitoring of the artificial joint based on the new wear monitoring frequency to output the wear degree; A wear prediction module: by judging in real time whether the wear degree exceeds the wear degree warning value, if it exceeds the wear degree warning value, obtaining the corresponding warning time point, and predicting the remaining service life of the artificial joint in the future period.

2. The attitude and wear monitoring system for the motion signals of artificial joints of the four limbs according to claim 1, characterized in that: The process of identifying the type of motion posture is: Collecting motion signal data of the artificial joint and performing data preprocessing; extracting and fusing features from the motion signal data of the artificial joint after preprocessing, inputting the fused features into the motion posture judgment model, and outputting the real-time motion posture type; Among them, when training the motion posture judgment model using a historical data set, the random forest algorithm is used, and the adaptive feature weighting algorithm is combined to calculate the training features.

3. The attitude and wear monitoring system for the movement signals of artificial joints of four limbs according to claim 1, characterized in that: The process of obtaining motion features is: Motion features include: joint bearing strength value, joint instantaneous load value, motion amplitude value, vibration amplitude value, and muscle activation strength value; Setting a sliding time window, and obtaining pressure data, linear acceleration data, and electromyogram intensity data within each time window; Taking the mean value of the pressure data within the time window as the joint bearing strength value, and taking the peak value of the pressure data within the time window as the joint instantaneous load value; Synthesizing the linear acceleration data in three-dimensional space to obtain a synthesized linear acceleration, taking the mean value of the synthesized linear acceleration within the time window as the motion amplitude value, and taking the peak value of the synthesized linear acceleration within the time window as the vibration amplitude value; Taking the root mean square of the electromyogram intensity data within the time window as the muscle activation strength value.

4. The attitude and wear monitoring system for the movement signals of artificial joints of the four limbs according to claim 1, wherein: The process of obtaining the motion intensity coefficient is: Normalizing the motion features to obtain a motion intensity feature vector; obtaining a historical data set and training a differentiated weight matrix based on the historical data set; Performing weighted fusion by combining the motion intensity feature vector and the weight matrix to obtain a basic motion intensity; calculating the weight of the basic motion intensity and the joint fatigue factor to obtain the motion intensity coefficient.

5. The attitude and wear monitoring system for the motion signals of artificial joints of the four limbs according to claim 1, characterized in that: The process of obtaining the joint fatigue factor: Extracting the proportion of the middle frequency band energy corresponding to the electromyogram signal in the motion signal data of the artificial joint, extracting the minimum middle frequency band energy and the maximum middle frequency band energy in the historical data, and performing a difference calculation to obtain the historical middle frequency band energy extreme difference, performing a difference calculation between the middle frequency band energy proportion and the minimum middle frequency band energy, and then performing a ratio calculation with the historical middle frequency band energy extreme difference to obtain the joint fatigue factor.

6. The attitude and wear monitoring system for the movement signals of artificial joints of four limbs according to claim 1, characterized in that: The process of updating the wear monitoring frequency is: If the motion posture type is a static posture, stop wear monitoring; If the motion posture type is a high-load motion posture or a low-load motion posture, obtain the real-time wear monitoring frequency , and calculate the new wear monitoring frequency after update; For high-load exercise postures, the calculation formula is: ; where is the new wear monitoring frequency, and k is the exercise intensity coefficient. For a low-load exercise posture, the calculation formula is: ; where is the new wear monitoring frequency, and k is the exercise intensity coefficient.

7. The attitude and wear monitoring system for the movement signals of artificial joints of the four limbs according to claim 1, characterized in that: The process of outputting the wear degree is: Collect the motion signal data of the artificial joint according to the new wear monitoring frequency, and calculate the wear amount using the Archard model in the classical wear model based on the collected motion signal data of the artificial joint; calculate the wear increment of the real-time motion, accumulate the historical wear increments, and output the wear degree.

8. The attitude and wear monitoring system for the movement signals of artificial joints of the four limbs according to claim 1, characterized in that: Predict the remaining service life of the artificial joint in the future period. The process is as follows: Obtain the time point corresponding to the wear degree warning value as the warning time point, and obtain the wear degree corresponding to the warning time point to the warning time point as the basic wear degree data; Conduct a trend analysis of the basic wear degree data, including judging whether the basic wear degree data is linearly changed or non-linearly changed; For the linearly changed basic wear degree, fit to obtain a linear prediction equation. The slope of the linear prediction equation is the wear rate. Calculate the difference between the wear degree threshold and the wear degree value corresponding to the warning time point, and calculate the ratio of the difference to the wear rate to obtain the remaining service life; For the non-linearly changed basic wear degree, fit to obtain a non-linear prediction equation; Based on the non-linear prediction equation, take the wear degree threshold as known, calculate the corresponding predicted time value, and calculate the difference between the predicted time value and the warning time value to obtain the remaining service life.

9. The attitude and wear monitoring system for the motion signals of artificial joints of the four limbs according to claim 8, characterized in that: Conduct a trend analysis. The process is as follows: Use the methods of statistical fitting and residual analysis to judge, and conduct linear fitting and non-linear fitting on the basic wear degree data respectively; Conduct a unary linear regression on the basic wear data, fit a unary linear regression equation, and calculate the sum of squared linear residuals; at the same time, conduct a quadratic polynomial fitting on the basic wear data and calculate the sum of squared non-linear residuals; Compare the linear fitting and non-linear fitting through the AIC information criterion; if the sum of squared non-linear residuals is less than the sum of squared linear residuals and the AIC value corresponding to the non-linearity is smaller, it is determined to be non-linearly changed. If the residuals are randomly distributed and there is no obvious curvature, it is determined to be linearly changed.

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