A method for assessing lateral ankle ligament injury

Abstract

The application discloses an ankle lateral collateral ligament injury evaluation method and relates to the technical field of ligament injury detection. The ankle lateral collateral ligament injury evaluation method specifically comprises the following steps: S1, experiment preparation; S2, ankle joint stability evaluation; S3, ankle joint ligament laxity index evaluation; S4, conclusion summary; through the combination of a leg shaking device and an inertial sensor, the precise monitoring of ankle joint dynamic movement is realized, and the accuracy and real-time performance of data acquisition are improved. Secondly, the deep learning algorithm is used for data processing and analysis, key features can be automatically extracted and data dimensions can be optimized, and the accuracy and efficiency of evaluation are effectively improved. In addition, the method not only evaluates the stability of the ankle joint, but also introduces the ligament laxity index evaluation, and more comprehensively reflects the functional state of the ankle joint. Finally, through the introduction of time-frequency analysis, wavelet transform technology and ROC curve and AUC value evaluation, the reliability and scientificity of diagnosis are further enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of ligament injury detection, and in particular to a method for assessing lateral ankle collateral ligament injury. Background Art

[0002] Ankle instability is a common sequelae of a sprained ankle. While prevalent, it's difficult to diagnose using current technology, relying solely on a physician's medical history and physical examination. After an ankle sprain, ligaments struggle to heal and restore joint function. Without surgical treatment or long-term, standardized rehabilitation, ankle instability is a common symptom. This leads to abnormal wear and tear from misaligned joint movement.

[0003] The current examination method uses a "bracket" to apply lateral force to the ankle joint. If the patient has ankle instability, meaning ligament dysfunction, X-rays will show malalignment of the interosseous structures. However, this examination method focuses primarily on the alignment of the bone structures and ignores key indicators such as ligament laxity. This results in a single assessment dimension that fails to fully reflect the functional status of the ankle joint. Furthermore, the lack of efficient data processing and analysis capabilities makes it challenging for doctors to interpret examination results. Existing examination methods are unable to automatically extract key features and optimize data dimensions, limiting assessment efficiency and accuracy. Summary of the Invention

[0004] In view of the deficiencies of the existing technology, the present invention provides a method for evaluating lateral ankle collateral ligament injury, which solves the problems of the existing technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for assessing lateral ankle collateral ligament injury, specifically comprising the following steps:

[0006] S1. Experimental preparation: Screen patients, conduct medical history and physical examination, obtain informed consent, and then fix the lower leg in the leg swinging device;

[0007] S2. Ankle stability assessment:

[0008] S2.1. Record the inertial sensor data during leg shaking and preprocess it using a deep learning algorithm.

[0009] S2.2. Automatically extract key features using deep learning networks, combined with PCA dimensionality reduction and Lasso regression feature selection to optimize data dimensions;

[0010] S2.3. Train the SVM support vector machine classification model and perform kernel function optimization and parameter tuning;

[0011] S2.4. Analyze the precision, recall, and F1 score metrics and determine the evaluation method.

[0012] S3. Assessment of ankle ligament laxity index:

[0013] S3.1. Use the same leg shaking device and inertial sensor to record leg shaking data.

[0014] S3.2. Introducing time-frequency analysis and wavelet transform technology, combined with deep learning models to perform feature fusion and relaxation index prediction;

[0015] S3.3. The t-test method was used to compare the difference in relaxation index between the healthy and diseased groups, and the ROC curve and AUC value were introduced to evaluate the diagnostic efficacy;

[0016] S4. Conclusion Summary: Based on the evaluation results of S2 and S3, the evaluation method, data segment length, deep learning model parameters, and the calculation formula and threshold of the relaxation index are determined.

[0017] Preferably, when the leg shaking device is fixed, a special strap is used to fix the inertial sensor at the first metatarsophalangeal joint of the patient's dorsum of the foot or a designated position on the inner side of the foot, the parameters of the leg shaking device are adjusted, and an automatic calibration cycle is set; the leg shaking device drives the calf to shake back and forth through an electric drive structure, and uses the inertial sensor to collect data for evaluation in subsequent steps S2 and S3.

[0018] Preferably, the preprocessing in S2.1 includes denoising, smoothing and feature extraction; and step S2.2 specifically includes:

[0019] S2.2.1. Feature extraction: Use deep learning networks to automatically extract key features and reduce human intervention;

[0020] S2.2.2 PCA Dimensionality Reduction and Feature Selection: Combine the PCA algorithm and feature importance evaluation to perform dimensionality reduction and feature selection on the data. The feature importance evaluation uses the Lasso regression algorithm. The formulas of the PCA algorithm and the Lasso regression algorithm are expressed as follows:

[0021] Y=XW PCA and feature selection = Lasso(X,λ);

[0022] Where X is the original data matrix, W PCA is the principal component loading matrix, Y is the data matrix after dimensionality reduction, and λ is the regularization parameter of Lasso regression;

[0023] S2.2.3. SVM model training and improvement: Use the SVM support vector machine classification model to train data from different time periods, and introduce kernel function optimization and parameter tuning to improve classification accuracy.

[0024] Preferably, the SVM model training in S2.2.3 requires preparing a data set, including feature vectors and corresponding labels; extracting feature vectors from the preprocessed data, including angular velocity and acceleration, and assigning labels to each data point according to the experimental design;

[0025] The model training formula using the SVM algorithm is:

[0026]

[0027] subject to y i (w·φ(x i )+b)≥1-ξ i ,ξ i ≥0, i=1, 2, …, n;

[0028] Among them, w is the weight vector, b is the bias term, φ(x i ) is the eigenvector x i The kernel function mapped to the high-dimensional space, C is the regularization parameter, ξ i is the slack variable, n is the number of data points;

[0029] Select RBF kernel as the kernel function, the formula is:

[0030] K(x p , x q )=exp(-γ║x p -x q ║ 2 );

[0031] Among them, γ is the parameter of RBF kernel, which is determined by experiments. By adjusting the value of γ, the degree of fit of the model to the data can be changed; K(x p , x q ) represents two sample points x in the input space p and x q The inner product after mapping to the high-dimensional feature space through the kernel function.

[0032] Preferably, the following improvements are made to improve the performance of the SVM model:

[0033] Parameter tuning: Use grid search or random search methods to tune the SVM parameters to find the best parameter combination;

[0034] Feature selection: Select the features that have the greatest impact on the classification results through feature importance evaluation to reduce the number of features and improve the generalization ability of the model;

[0035] Model fusion: Use the fusion of multiple SVM models to improve the stability and accuracy of the model.

[0036] Preferably, the best SVM model is evaluated in S2.4 using the accuracy, recall, and F1 score indicators to verify the classification effect; when the accuracy is high and the recall and F1 scores are also good, it means that the model has good classification performance;

[0037] The accuracy formula is expressed as:

[0038] The recall formula is expressed as:

[0039] The F1 score formula is expressed as:

[0040] Among them, Accuracy represents the accuracy rate, Recall represents the recall rate, TP represents the number of correctly classified positive examples, TN represents the number of correctly classified negative examples, FP represents the number of incorrectly classified positive examples, and FN represents the number of incorrectly classified negative examples.

[0041] Precision refers to the proportion of samples that the classifier judges to be positive examples that are actually positive examples. The calculation formula is:

[0042] Recall refers to the proportion of samples that are actually positive examples that are correctly judged as positive examples by the classifier. The calculation formula is:

[0043] By comparing the performance of the SVM model under different parameter combinations, the optimal method for evaluating ankle stability was determined, including the optimal leg shaking frequency, data segment length, feature selection results, and parameter configuration of the SVM model.

[0044] Preferably, in the step S3.2, based on the correlation analysis results and weight adjustment, the formula for feature fusion and relaxation index prediction in combination with the deep learning model is:

[0045] Relaxation index = A × w1 + B × w2 + C × w3;

[0046] Where A is the frequency domain subfrequency of the X-axis angular velocity of the machine system, B is the 95th percentile of the wavelet reconstruction of the X-axis angular velocity of the machine system, C is the wavelet reconstruction variance of the X-axis acceleration of the machine system, and w1, w2, and w3 are weight coefficients obtained through correlation analysis and weight adjustment.

[0047] Preferably, the S3.3, using the t-test method to compare the difference in relaxation index between the healthy group and the diseased group, and introducing the ROC curve and AUC value to evaluate the diagnostic efficacy

[0048] The t-test is used to compare whether there is a significant difference between the means of two independent samples. The formula is:

[0049]

[0050] in, and is the mean of the two samples, n1 and n2 are the sample sizes, and spooled is the pooled standard deviation.

[0051] Preferably, in S3.3, in order to evaluate the performance of the SVM model in the classification task, an ROC curve is drawn and the AUC value is calculated; the ROC curve represents the model performance by plotting the true positive rate TPR against the false positive rate FPR; the ROC curve is a graph drawn with FPR as the horizontal axis and TPR as the vertical axis, showing the performance of the classifier at different thresholds. The closer the ROC curve is to the upper left corner, the better the performance of the classifier, that is, the higher the true positive rate and the lower the false positive rate;

[0052] The true positive rate TPR formula is:

[0053] The false positive rate FPR formula is:

[0054] The AUC value represents the area under the ROC curve and is used to measure the performance of the classifier. The closer the AUC value is to 1, the better the classifier performance is. Conversely, the closer the AUC value is to 0, the worse the classifier performance is. The closer the AUC value is to 1, the better the model performance is. The AUC value is calculated by numerical integration or trapezoidal method.

[0055] The present invention provides a method for assessing lateral ankle collateral ligament injuries. Compared with existing technologies, it has the following advantages:

[0056] 1. This method for assessing lateral collateral ligament injury of the ankle, by combining a leg shaking device with an inertial sensor, achieves accurate monitoring of the dynamic movement of the ankle joint, thereby improving the accuracy and real-time performance of data acquisition. Secondly, the use of deep learning algorithms for data processing and analysis can automatically extract key features and optimize data dimensions, effectively improving the accuracy and efficiency of the assessment. In addition, this method not only assesses the stability of the ankle joint, but also introduces the assessment of ligament laxity indicators to more comprehensively reflect the functional status of the ankle joint. Finally, by introducing time-frequency analysis, wavelet transform technology, ROC curve and AUC value evaluation, the reliability and scientific nature of the diagnosis are further enhanced. Overall, this method has the advantages of simple operation, comprehensive assessment, high accuracy, and strong diagnostic efficiency, providing strong technical support for the clinical diagnosis and treatment of ankle instability and lateral collateral ligament injury of the ankle.

[0057] 2. This method for assessing lateral ankle collateral ligament injuries significantly improves the accuracy and efficiency of ankle stability assessment through processes such as automatic extraction of key features using a deep learning network, PCA dimensionality reduction and Lasso regression feature selection, and SVM model training and improvement. Compared with existing inspection methods, this method not only reduces human intervention but also improves the classification performance of the SVM model by optimizing the kernel function and parameter tuning. At the same time, indicators such as accuracy, recall rate, and F1 score are introduced to evaluate the model to ensure the reliability of the classification effect. In addition, this method also determines the optimal evaluation method by comparing the model performance under different parameter combinations, providing strong support for the accurate assessment of ankle stability.

[0058] 3. This method for assessing lateral collateral ligament injuries of the ankle not only accurately assesses ankle ligament laxity, but also greatly improves assessment efficiency and accuracy. Through feature extraction, correlation analysis, and weight adjustment, a laxity index prediction model was constructed, providing strong support for clinical diagnosis and treatment. At the same time, statistical methods such as t-tests, ROC curves, and AUC values ​​were introduced to comprehensively evaluate the diagnostic efficacy of the model, ensuring the reliability and scientific nature of the assessment results. This method has the advantages of simple operation, accurate results, and wide applicability, providing new ideas and technical means for the assessment of ankle ligament laxity. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a schematic diagram of the process of the present invention;

[0060] Figure 2 is a schematic diagram of the leg shaking device of the present invention;

[0061] Figure 3 The ligament laxity index diagram of a healthy person and a ligament injury patient of the present invention;

[0062] Figure 4 Schematic diagram of the signal acquisition, data processing and analysis program of the present invention.

[0063] In the figure: frame 1, calf swing arm 2, calf fixing structure 3, crank slider reciprocating motion mechanism 4, inertial sensor 5. DETAILED DESCRIPTION

[0064] 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.

[0065] The present invention provides three technical solutions:

[0066] Figure 1-Figure 2 A first embodiment is shown: a method for assessing lateral ankle collateral ligament injury, specifically comprising the following steps:

[0067] S1. Experimental preparation: Screen patients who meet the experimental conditions and conduct detailed medical history inquiries and physical examinations. The same experienced chief physician performed ankle shaking and anterior drawer tests and judged the ankle joints as normal, slightly unstable, and severely unstable, respectively. Explain the experimental process, purpose, and potential risks to the patient, obtain their informed consent, and fix the calf to the leg shaking device; when the leg shaking device is fixed, use a special strap to fix the inertial sensor 5 at the first metatarsophalangeal joint of the patient's dorsum of the foot or a designated position on the inner side of the foot, adjust the parameters of the leg shaking device, such as the shaking frequency (the ankle joint stability assessment is set to 2.0Hz±0.1Hz, and the ligament laxity index assessment is also set to 2.0Hz±0.1Hz to maintain consistency), and set an automatic calibration cycle; the leg shaking device drives the calf to shake back and forth through an electric drive structure, and uses the inertial sensor 5 to collect data for the evaluation of subsequent steps S2 and S3;

[0068] like Figure 2 The leg-shaking device shown here comprises a frame 1, a calf swing arm 2, a calf fixing structure 3, a crank-slider reciprocating mechanism 4, and an inertial sensor 5. The top of the calf swing arm 2 is rotatably connected to the frame 1. The crank-slider reciprocating mechanism 4 is driven by a closed-loop controlled 57-stepping motor with a torque of 3.6 Nm. This reciprocating motion drives the calf swing arm 2 back and forth, thereby driving the calf to shake. The torque is amplified to 10.8 Nm by a 1:3 planetary reducer. Tests have shown that it can drive an adult male's calf to perform leg-shaking exercises at actual exercise frequencies of 1.5 Hz and 2 Hz.

[0069] S2. Ankle stability assessment:

[0070] S2.1. Record the data from the inertial sensor 5 during the leg shaking process and pre-process it using a deep learning algorithm;

[0071] S2.2. Automatically extract key features using deep learning networks, combined with PCA dimensionality reduction and Lasso regression feature selection to optimize data dimensions;

[0072] S2.3. Train the SVM support vector machine classification model and perform kernel function optimization and parameter tuning to improve classification accuracy;

[0073] S2.4. Analyze the precision, recall, and F1 score metrics and determine the evaluation method.

[0074] S3. Assessment of ankle ligament laxity index:

[0075] S3.1. Using the same leg shaking device and inertial sensor 5, record leg shaking data;

[0076] S3.2. Introducing time-frequency analysis and wavelet transform technology, combined with deep learning models to perform feature fusion and relaxation index prediction;

[0077] S3.3. The t-test method was used to compare the difference in relaxation index between the healthy and diseased groups, and the ROC curve and AUC value were introduced to evaluate the diagnostic efficacy;

[0078] S4. Conclusion Summary: Based on the evaluation results of S2 and S3, the evaluation method, data segment length, deep learning model parameters, and the calculation formula and threshold of the relaxation index were determined for subsequent diagnosis and treatment.

[0079] By combining the leg shaking device with the inertial sensor 5, accurate monitoring of the dynamic movement of the ankle joint is achieved, and the accuracy and real-time performance of data acquisition are improved. Secondly, the use of deep learning algorithms for data processing and analysis can automatically extract key features and optimize data dimensions, effectively improving the accuracy and efficiency of the evaluation. In addition, this method not only evaluates the stability of the ankle joint, but also introduces the evaluation of ligament laxity indicators to more comprehensively reflect the functional status of the ankle joint. Finally, by introducing time-frequency analysis, wavelet transform technology, ROC curve and AUC value evaluation, the reliability and scientificity of the diagnosis are further enhanced. Overall, this method has the advantages of simple operation, comprehensive evaluation, high accuracy, and strong diagnostic efficiency, providing strong technical support for the clinical diagnosis and treatment of ankle instability and lateral ankle collateral ligament injury.

[0080] The second embodiment differs from the first embodiment mainly in that the preprocessing in S2.1 includes denoising, smoothing, and feature extraction; and step S2.2 specifically includes:

[0081] S2.2.1. Feature extraction: Use deep learning networks (such as convolutional neural networks (CNNs)) to automatically extract key features and reduce human intervention;

[0082] S2.2.2 PCA Dimensionality Reduction and Feature Selection: Combine the PCA algorithm and feature importance evaluation (such as Lasso regression) to perform dimensionality reduction and feature selection on the data. The feature importance evaluation uses the Lasso regression algorithm. The formulas for the PCA algorithm and Lasso regression algorithm are expressed as follows:

[0083] Y=XW PCA and feature selection = Lasso(X,λ);

[0084] Where X is the original data matrix, W PCA is the principal component loading matrix, Y is the data matrix after dimensionality reduction, and λ is the regularization parameter of Lasso regression;

[0085] S2.2.3. SVM model training and improvement: Use the SVM support vector machine classification model to train data from different time periods, and introduce kernel function optimization (such as RBF kernel) and parameter tuning (such as grid search) to improve classification accuracy.

[0086] SVM model training in S2.2.3 requires preparing a dataset, including feature vectors and corresponding labels. Feature vectors, including angular velocity and acceleration, are extracted from the preprocessed data, and labels (e.g., high stability, medium stability, low stability, etc.) are assigned to each data point based on the experimental design.

[0087] The model training formula using the SVM algorithm is:

[0088]

[0089] subject to y i (w·φ(x i )+b)≥1-ξ i ,ξ i ≥0, i=1, 2, …, n;

[0090] Among them, w is the weight vector, b is the bias term, φ(x i ) is the eigenvector x i The kernel function mapped to the high-dimensional space, C is the regularization parameter, ξ i is the slack variable, n is the number of data points;

[0091] Common kernel functions include linear kernel, polynomial kernel, radial basis function (RBF) kernel, etc. In this experiment, RBF kernel is selected as the kernel function, and the formula is:

[0092] K(x p , x q )=exp(-γ║x p -x q ║ 2 );

[0093] Among them, γ is the parameter of RBF kernel, which is determined by experiments. By adjusting the value of γ, the degree of fit of the model to the data can be changed; K(x p , x q ) represents two sample points x in the input space p and x q The inner product after the kernel function is mapped to the high-dimensional feature space. In SVM, this inner product is used to calculate the distance or similarity between sample points, thereby helping the model to perform classification.

[0094] To improve the performance of the SVM model, the following improvements are made:

[0095] Parameter tuning: Use grid search or random search methods to tune the SVM parameters (such as C, γ) to find the best parameter combination;

[0096] Feature selection: Select the features that have the greatest impact on the classification results through feature importance evaluation (such as Lasso regression) to reduce the number of features and improve the generalization ability of the model;

[0097] Model fusion: Use the fusion of multiple SVM models (such as Bagging, Boosting, etc.) to improve the stability and accuracy of the model.

[0098] In S2.4, the best SVM model is evaluated using the accuracy, recall, and F1 score metrics to verify the classification performance. A high accuracy, recall, and F1 score indicate that the model has good classification performance.

[0099] The accuracy formula is expressed as:

[0100] The recall formula is expressed as:

[0101] The F1 score formula is expressed as:

[0102] Among them, Accuracy represents the accuracy rate, Recall represents the recall rate, TP represents the number of correctly classified positive examples, TN represents the number of correctly classified negative examples, FP represents the number of incorrectly classified positive examples, and FN represents the number of incorrectly classified negative examples.

[0103] Precision refers to the proportion of samples that the classifier judges to be positive examples that are actually positive examples. The calculation formula is:

[0104] Recall refers to the proportion of samples that are actually positive examples that are correctly judged as positive examples by the classifier. The calculation formula is:

[0105] By comparing the performance of the SVM model under different parameter combinations, the optimal method for evaluating ankle stability was determined, including the optimal leg shaking frequency, data segment length, feature selection results, and parameter configuration of the SVM model.

[0106] This example verifies the effect of data segment length on analysis results. The experimental process is as follows:

[0107] Experimental Methods and Equipment: During the data processing phase, each patient's motion data was segmented into different time segments. A support vector machine (SVM) classification model was trained for each segment of the original data, with each segment divided into 5-second, 10-second, and 15-second segments, as well as without segmentation. The maximum accuracy achieved by the model reflects the impact of segment length on the analysis results.

[0108] Experimental results:

[0109] SVM classifiers were trained using data segmented every 5, 10, 15, and 20 seconds, as well as unsegmented data. Each segmentation number was recorded at 1.5 Hz and 2.0 Hz. PCA was used to reduce the data features from 288 to 36 dimensions, and 20% of the data was randomly selected for testing.

[0110] Among them, 1.5 / 2.0 represent the frequency of the leg shaking device used when collecting this segment of data, and 5000, 10000, 20000, and unseged represent the length of the segment. The first three represent 5 / 10 / 20 second segments, respectively, and unseged means no segment. Judging from the confusion matrix and accuracy of the classification results, the 2.0Hz vibration frequency is better than 1.5Hz in all segment lengths. In terms of segment length, both too short and too long segment lengths have a negative impact on classification accuracy. The effect of no segmentation is the worst. In terms of specific performance, the 5-second segment is slightly better than the 20-second segment, and among the several selected segment length options, the 10-second segment appears to be the best.

[0111] Therefore, a vibration frequency close to the resonant frequency of the human foot, which produces a more pronounced difference in foot vibration amplitude between patients and healthy subjects during the leg-shaking experiment, can effectively differentiate the experimental data from patients and healthy subjects, facilitating SVM classification. Experimental results show that 2.0 Hz is significantly closer to the resonant frequency of the human foot than 1.5 Hz, and provides sufficient feature specificity for acceleration data.

[0112] The first function of data segmentation is to perform data enhancement and expand the amount of input data. The second function is to prevent individual outliers from having too much impact on the entire segment of data and to reduce the impact of overall baseline drift on feature calculations. In addition, data segmentation can also play a role in segment voting in individual tests, which can improve the overall judgment accuracy. From the experimental results, it can be seen that too short a segment length will reduce the accuracy of the model, which may be due to the fact that the shorter segment length contains fewer motion cycles. The poor accuracy of overly long segments may be due to the small amount of training data and the overall offset caused by long-term error accumulation, which is reflected in the feature classification.

[0113] Because PCA has input data dimensionality limitations, although this experiment showed that 2.0 Hz, 10-second segmentation is currently the optimal parameter choice, the suboptimal 2.0 Hz, 5-second segmentation was ultimately selected. PCA dimensionality reduction requires the projected dimension to be between 0 and the minimum of the number of data and features. Therefore, choosing 5-second segmentation maintains a reasonable PCA reduced dimension of 12. Excessively low reduced dimensions can also reduce the classification accuracy of the final model.

[0114] Through processes such as automatic extraction of key features using a deep learning network, PCA dimensionality reduction and Lasso regression feature selection, and SVM model training and improvement, the accuracy and efficiency of ankle stability assessment have been significantly improved. Compared with existing examination methods, this method not only reduces human intervention but also improves the classification performance of the SVM model by optimizing the kernel function and parameter tuning. At the same time, indicators such as accuracy, recall rate, and F1 score are introduced to evaluate the model to ensure the reliability of the classification effect. In addition, by comparing the model performance under different parameter combinations, this method determines the optimal evaluation method, providing strong support for the accurate assessment of ankle stability.

[0115] Figure 3-Figure 4 A third embodiment is shown, which differs from the first embodiment mainly in that the experimental purpose of step S3 is to find an indicator that can determine the laxity of the ankle joint ligament.

[0116] Experimental Method and Equipment: Leg shaking device, inertial sensor, and straps. Secure your leg to the leg shaking device with the straps. Use the straps to secure the inertial sensor to the gap between your thumbs on the inside of your foot. Adjust the motor so that the leg shaking device shakes your leg at a frequency of 2 Hz.

[0117] Experimental results and analysis: Experimental data recording and processing: Use software to record and save the original data of the inertial conductor shaking with the foot, obtain features through feature extraction, and perform correlation analysis on each feature with the health and injury levels. Select features with higher correlation and combine them and adjust the weight to obtain the relaxation index. At the same time, due to the small sample size, a t-test analysis was performed.

[0118] In step S3.2, based on the correlation analysis results and weight adjustment, the formula for feature fusion and relaxation index prediction is combined with a deep learning model (such as a neural network):

[0119] Relaxation index = A × w1 + B × w2 + C × w3;

[0120] Where A is the frequency domain subfrequency of the X-axis angular velocity of the machine system, B is the 95th percentile of the wavelet reconstruction of the X-axis angular velocity of the machine system, C is the wavelet reconstruction variance of the X-axis acceleration of the machine system, and w1, w2, and w3 are weight coefficients obtained through correlation analysis and weight adjustment.

[0121] S3.3. The t-test method was used to compare the difference in relaxation index between the healthy and diseased groups, and the ROC curve and AUC value were introduced to evaluate the diagnostic efficacy;

[0122] The t-test is used to compare whether there is a significant difference between the means of two independent samples. The formula is:

[0123]

[0124] in, and is the mean of the two samples, n1 and n2 are the sample sizes, and spooled is the pooled standard deviation.

[0125] This model was tested on 22 subjects (15 patients and 7 healthy subjects). The t-test revealed a p-value of 1.22e-06 for the healthy and patient data, and 0.0176 for the mildly injured and severely injured data. Both values ​​were less than 0.05, indicating significant differences between the healthy and patient data, and between the mildly injured and severely injured data. However, due to the significant difference in the number of healthy and mildly injured subjects, a t-test was performed. Therefore, it can be concluded that the laxity index calculated by this method is generally sufficient to represent ankle laxity.

[0126] In S3.3, to evaluate the performance of the SVM model in the classification task, a ROC (Receiver Operating Characteristic) curve is plotted and the AUC (Area Under the Curve) value is calculated. The ROC curve represents the model performance by plotting the True Positive Rate (TPR) against the False Positive Rate (FPR). The ROC curve is a graph with the FPR as the horizontal axis and the TPR as the vertical axis, showing the performance of the classifier at different thresholds. The closer the ROC curve is to the upper left corner, the better the classifier performance, that is, the higher the True Positive Rate and the lower the False Positive Rate.

[0127] The true positive rate TPR formula is:

[0128] The false positive rate FPR formula is:

[0129] The AUC value represents the area under the ROC curve and is used to measure the performance of the classifier. The closer the AUC value is to 1, the better the classifier performance is. Conversely, the closer the AUC value is to 0, the worse the classifier performance is. The closer the AUC value is to 1, the better the model performance is. The AUC value is calculated by numerical integration or trapezoidal method.

[0130] This embodiment not only achieves an accurate assessment of ankle ligament laxity, but also greatly improves the efficiency and accuracy of the assessment. Through feature extraction, correlation analysis and weight adjustment, a laxity index prediction model is constructed, which provides strong support for clinical diagnosis and treatment. At the same time, statistical methods such as t-test, ROC curve and AUC value are introduced to comprehensively evaluate the diagnostic efficacy of the model, ensuring the reliability and scientificity of the evaluation results. This method has the advantages of simple operation, accurate results and wide applicability, and provides new ideas and technical means for the assessment of ankle ligament laxity.

[0131] In the machine learning model using SVM screening and classification, the data of 22 subjects were divided into training and validation sets, and the accuracy was 80%. The results of the training and validation sets are shown in Table 1-2 below:

[0132] Table 1 Training set results 2

[0133]

[0134] Table 2 Validation set results 2

[0135]

[0136] Figure 4 A schematic diagram of the signal acquisition, data processing and analysis program is shown, which implements the first two steps of feature extraction of data on the Matlab side. The main time-frequency features include: effective value, mean, peak value, peak factor, kurtosis, etc., as well as center of gravity frequency, PSD, frequency standard deviation, etc.; the processing of outliers is optimized.

[0137] To facilitate code packaging, the code is ported to a .py file, and wavelet transform feature extraction is supplemented to facilitate subsequent model use.

[0138] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0139] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0140] 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 method for assessing lateral ankle collateral ligament injury, characterized in that: The specific steps include: S1. Experimental preparation: Screen patients, conduct medical history and physical examination, obtain informed consent, and then fix the lower leg in the leg swinging device; S2. Ankle stability assessment: S2.

1. Record the inertial sensor data during leg shaking and preprocess it using a deep learning algorithm. S2.

2. Automatically extract key features using deep learning networks, combining PCA dimensionality reduction and Lasso regression feature selection to optimize data dimensions; specifically, S2.2.

1. Feature extraction: Use deep learning networks to automatically extract key features and reduce human intervention; S2.2.2 PCA Dimensionality Reduction and Feature Selection: Combine the PCA algorithm and feature importance evaluation to perform dimensionality reduction and feature selection on the data. The feature importance evaluation uses the Lasso regression algorithm. The formulas of the PCA algorithm and the Lasso regression algorithm are expressed as follows: Y=XW PCA and feature selection = Lasso(X,λ); Where X is the original data matrix, W PCA is the principal component loading matrix, Y is the data matrix after dimensionality reduction, and λ is the regularization parameter of Lasso regression; S2.2.

3. SVM model training and improvement: Use the SVM support vector machine classification model to train data from different time periods, and introduce kernel function optimization and parameter tuning to improve classification accuracy; S2.

3. Train the SVM support vector machine classification model and perform kernel function optimization and parameter tuning; S2.

4. Analyze the precision, recall, and F1 score metrics and determine the evaluation method. S3. Assessment of ankle ligament laxity index: S3.

1. Use the same leg shaking device and inertial sensor to record leg shaking data. S3.

2. Introducing time-frequency analysis and wavelet transform technology, combined with deep learning models to perform feature fusion and relaxation index prediction; S3.

3. The t-test method was used to compare the difference in relaxation index between the healthy and diseased groups, and the ROC curve and AUC value were introduced to evaluate the diagnostic efficacy; S4. Conclusion Summary: Based on the evaluation results of S2 and S3, the evaluation method, data segment length, deep learning model parameters, and the calculation formula and threshold of the relaxation index are determined.

2. The method for assessing lateral ankle collateral ligament injury according to claim 1, wherein: When the leg shaking device is fixed, a special strap is used to fix the inertial sensor at the first metatarsophalangeal joint of the patient's dorsum of the foot or a designated position on the inner side of the foot, the parameters of the leg shaking device are adjusted, and an automatic calibration cycle is set; the leg shaking device drives the calf to shake back and forth through an electric drive structure, and uses the inertial sensor to collect data for evaluation in subsequent steps S2 and S3.

3. The method for assessing lateral ankle collateral ligament injury according to claim 1, wherein: The preprocessing in S2.1 includes denoising, smoothing and feature extraction.

4. The method for assessing lateral ankle collateral ligament injury according to claim 3, wherein: The SVM model training in S2.2.3 requires preparing a data set, including feature vectors and corresponding labels; extracting feature vectors from the preprocessed data, including angular velocity and acceleration, and assigning labels to each data point according to the experimental design; The model training formula using the SVM algorithm is: ； ； Among them, w is the weight vector, b is the bias term, is to transform the eigenvector x i The kernel function mapped to the high-dimensional space, C is the regularization parameter, is the slack variable, n is the number of data points; Select RBF kernel as the kernel function, the formula is: ； in, is the parameter of the RBF kernel, which is determined by experiments. By adjusting the value of γ, the degree of fit of the model to the data can be changed; K(x p , x q ) represents two sample points x in the input space p and x q The inner product after mapping to the high-dimensional feature space through the kernel function.

5. The method for assessing lateral ankle collateral ligament injury according to claim 4, wherein: To improve the performance of the SVM model, the following improvements are made: Parameter tuning: Use grid search or random search methods to tune the SVM parameters to find the best parameter combination; Feature selection: Select the features that have the greatest impact on the classification results through feature importance evaluation to reduce the number of features and improve the generalization ability of the model; Model fusion: Use the fusion of multiple SVM models to improve the stability and accuracy of the model.

6. The method for assessing lateral ankle collateral ligament injury according to claim 5, wherein: In S2.4, the best SVM model is evaluated using the accuracy, recall, and F1 score indicators to verify the classification effect. When the accuracy is high and the recall and F1 scores are also good, it means that the model has good classification performance. The accuracy formula is: Accuracy = ; The recall formula is: Recall = ; The F1 score formula is: F1 = ; Among them, Accuracy represents the accuracy rate, Recall represents the recall rate, TP represents the number of correctly classified positive examples, TN represents the number of correctly classified negative examples, FP represents the number of incorrectly classified positive examples, and FN represents the number of incorrectly classified negative examples. Precision refers to the proportion of samples that the classifier judges as positive examples that are actually positive examples. The calculation formula is: Precision = ; Recall refers to the proportion of samples that are actually positive examples that are correctly judged as positive examples by the classifier. The calculation formula is: Recall = ; By comparing the performance of the SVM model under different parameter combinations, the optimal method for evaluating ankle stability was determined, including the optimal leg shaking frequency, data segment length, feature selection results, and parameter configuration of the SVM model.

7. The method for assessing lateral ankle collateral ligament injury according to claim 1, wherein: In step S3.2, based on the correlation analysis results and weight adjustment, the formula for feature fusion and relaxation index prediction combined with the deep learning model is: Relaxation index = A×w1+B×w2+C×w3; Where A is the frequency domain subfrequency of the X-axis angular velocity of the machine system, B is the 95th percentile of the wavelet reconstruction of the X-axis angular velocity of the machine system, C is the wavelet reconstruction variance of the X-axis acceleration of the machine system, and w1, w2, and w3 are weight coefficients obtained through correlation analysis and weight adjustment.

8. The method for assessing lateral ankle collateral ligament injury according to claim 1, wherein: S3.3: The t-test method was used to compare the difference in relaxation index between the healthy group and the diseased group, and the ROC curve and AUC value were introduced to evaluate the diagnostic efficacy. The t-test is used to compare whether there is a significant difference between the means of two independent samples. The formula is: t= ； in, and is the mean of the two samples, n1 and n2 are the sample sizes, and spooled is the pooled standard deviation.

9. The method for assessing lateral ankle collateral ligament injury according to claim 6, wherein: In S3.3, in order to evaluate the performance of the SVM model in the classification task, an ROC curve is drawn and the AUC value is calculated. The ROC curve represents the model performance by plotting the true positive rate (TPR) against the false positive rate (FPR). The ROC curve is a graph drawn with FPR as the horizontal axis and TPR as the vertical axis, showing the performance of the classifier at different thresholds. The closer the ROC curve is to the upper left corner, the better the classifier performance, that is, the higher the true positive rate and the lower the false positive rate. The true positive rate TPR formula is: TPR= ; The false positive rate FPR formula is: FPR= ; The AUC value represents the area under the ROC curve and is used to measure the performance of the classifier. The closer the AUC value is to 1, the better the classifier performance is. Conversely, the closer the AUC value is to 0, the worse the classifier performance is. The closer the AUC value is to 1, the better the model performance is. The AUC value is calculated by numerical integration or trapezoidal method.

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