Parkinson's disease gait comparative analysis method based on time sequence characteristic curve

Abstract

The invention provides a Parkinson's disease gait comparative analysis method based on a time sequence characteristic curve, and the method comprises the steps: obtaining video data containing gait information which comprises gait information before and after a user uses a medicine; performing posture recognition on a human body in the video data, and extracting position information of key body parts; calculating gait features according to the extracted key point information, and constructing a time sequence curve according to changes of the features along with time; performing data processing on the extracted gait features by using a digital filter; local frequency and amplitude are integrated into distance measurement by using a frequency amplitude and amplitude enhanced dynamic time warping method to quantify the difference of gait movement; obtaining a curve difference distance; metrics are calculated based on cumulative frequency differences and amplitude differences in the optimal regular path. The method has remarkable advantages in the aspects of accuracy, time sensitivity, robustness, adaptability, comprehensive evaluation ability, quantitative analysis, clinical application potential and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of human posture recognition, and in particular to a gait comparison and analysis method for Parkinson's disease based on a time series characteristic curve. Background Art

[0002] Parkinson's disease (PD) is one of the most common movement disorders and seriously affects the quality of life of patients. Among the typical symptoms of PD, patients have a variety of gait disorders, which often lead to falls, reduced quality of life, and shortened life span. The degree of gait abnormality is an important indicator of the patient's health status. By evaluating the patient's gait movements, doctors can understand the severity of the disease, track its progression, and monitor the effectiveness of treatment. Therefore, accurate identification and evaluation of gait is crucial for evaluating medication and treatment for PD patients.

[0003] At present, there is a lack of comparative studies on the gait of Parkinson's patients before and after taking medication, which is crucial for evaluating the effectiveness of drug treatment and adjusting treatment plans; many time series comparison methods are currently used in various fields and do not have specificity; in the time series curve comparison method, only the difference between the two curves can be obtained, and the degree of change cannot be quantified. Summary of the invention

[0004] In order to solve various problems existing in the prior art, the purpose of the present invention is to provide a Parkinson's disease gait comparison analysis method based on time series characteristic curves, which method shows significant advantages in accuracy, time sensitivity, robustness, adaptability, comprehensive evaluation ability, quantitative analysis and clinical application potential.

[0005] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0006] A method for comparative analysis of Parkinson's disease gait based on time series characteristic curves, the method comprising the following steps:

[0007] Acquire video data including gait information, the gait information including gait information of the user before and after using the drug;

[0008] Perform posture recognition on human bodies in video data and extract the location information of key body parts;

[0009] Based on the extracted key point information, gait features are calculated, and the changes of these features over time are constructed into a time series curve;

[0010] Using digital filters to process the extracted gait features;

[0011] The frequency-amplitude-enhanced dynamic time warping method is used to integrate local frequency and amplitude into the distance metric to quantify the differences in gait motion.

[0012] Get the curve difference distance;

[0013] The metrics are calculated based on the cumulative frequency differences and amplitude differences in the optimal warping path.

[0014] According to a Parkinson's disease gait comparative analysis method based on a time series characteristic curve provided by the present invention, when performing human posture recognition, the OpenPose framework is used to detect skeletal key points from video data acquired in different time periods, wherein a BODY25 model is constructed using the OpenPose framework to obtain a series of K key point coordinates from the video data.

[0015] According to a Parkinson's disease gait comparative analysis method based on a time series characteristic curve provided by the present invention, a gait feature that can be directly calculated from the coordinates of key points in each frame is defined to estimate the patient's step frequency, including:

[0016] Leg length ratio difference R legs , is defined as the difference between the length ratios of the left and right legs, where the leg length is determined using the key points detected using the OpenPose framework and is measured as the distance between the hip and ankle key points of each leg, expressed as formula (1):

[0017]

[0018] Wherein, P9 represents the coordinates of the key point of the right hip; P 11 represents the coordinates of the key point of the right ankle; P 12 Represents the coordinates of the left hip key point; P 14 Represents the coordinates of the left ankle key point.

[0019] Vertical angle of the body It is defined as the angle between the midpoints of the two ankles and a straight line passing through the neck and parallel to the y-axis, expressed as formula (2):

[0020]

[0021] Among them, x1 represents the horizontal coordinate of the neck key point; x 11,14 represents the horizontal coordinate of the midpoint between the right ankle key point and the left ankle key point; P1 represents the coordinate of the neck key point; P 11,14 Represents the coordinates of the midpoints of the left and right ankles;

[0022] Horizontal angle of ankle It is defined as the angle between the line between the two ankles and the x-axis, expressed as formula (3):

[0023]

[0024] Among them, the horizontal coordinate of the right ankle key point is represented by x11 ;y 11 Indicates the vertical coordinate of the right ankle key point; x 14 Indicates the horizontal coordinate of the left ankle key point; y 14 Indicates the vertical coordinate of the left ankle keypoint.

[0025] According to a Parkinson's disease gait comparison and analysis method based on time series characteristic curves provided by the present invention, the digital filter is a Savitzky-Golay filter.

[0026] According to a Parkinson's disease gait comparative analysis method based on a time series characteristic curve provided by the present invention, when using a Savitzky-Golay filter, let x k is the gait feature of the kth frame, y k is the smoothed data, and the smoothed curve is expressed as formula (4):

[0027]

[0028] in, is the smoothing value of the frame data, H is the normalization factor, l is the half width of the smoothing window, x k+i is the data point of the k+i frame in the window, h i It is the smoothing coefficient obtained by fitting a polynomial using the least squares method and is used to best represent the trend of the data within the window.

[0029] According to a Parkinson's disease gait comparative analysis method based on time series characteristic curves provided by the present invention, the dynamic time warping method using frequency-amplitude enhancement integrates local frequency and amplitude into the distance metric to quantify the difference in gait movement, including:

[0030] Given two time series S1 and S2, with lengths of m and n respectively, represented by S1 = (u1, u2, ..., u m ) and S2=(v1,v2,…,v n );

[0031] u in S1 i and S2's v j A fixed-size window ω is selected from the neighborhood of each point in and a subsequence is defined: i =(u i-ω / 2 ,…,u i ,…,u i+ω / 2 ) and (v j-ω / 2 ,…,v j ,…,v j+ω / 2 );

[0032] Computing local frequency and amplitude involves finding peaks in the windowed subsequence to calculate the average period and frequency;

[0033] wherein the amplitude is determined by calculating the difference between the average and minimum of these peaks, and calculating the local frequency and amplitude based on the local frequency and amplitude involves finding the peak in the windowed subsequence to calculate the average period and frequency;

[0034] According to the local frequency and amplitude, the difference between gait sequences is measured by the distance function, and the distance function is defined as formula (5):

[0035]

[0036] Among them, V j The local frequency and amplitude are f(v j ) and A(v j ); f(u i ) and A(u i ) is U i local frequency and amplitude; and λ1 and λ2 are weighting parameters that adjust the effect of frequency and amplitude on distance.

[0037] According to a Parkinson's disease gait comparative analysis method based on time series characteristic curves provided by the present invention, in order to align two time series, an n×m matrix is ​​constructed, which is a cumulative distance matrix calculated by dynamic programming, and the optimal aligned warping path is obtained by backtracking according to the following recursive function, which is expressed as formula (6):

[0038] D(i,j)=d(u i ,v j )+min(D(i-1,j),D(i,j-1),D(i-1,j-1)), (6)

[0039] According to a Parkinson's disease gait comparative analysis method based on a time series characteristic curve provided by the present invention, the regularized path needs to meet boundary conditions, continuity conditions and monotonicity conditions. Therefore, the starting point and the end point of the normalized path can only be the two endpoints of the matrix diagonal, and the step length can only be two adjacent matrix elements. The regularized path is expressed as formula (7):

[0040] W=(w1,w2,...,w k ,...,w K ),max(m,n)≤K≤m+n-1 (7)

[0041] Among them, w k is the position (i, j) of the kth observation in the regularized path; then, by minimizing the cost of the regularized path, the DTW distance with the optimal aligned regularized path is obtained, expressed as formula (8):

[0042]

[0043] in, is the set of all possible regular paths, and K is the length of the regular path. The distance is divided by K to eliminate the influence of regular paths of different lengths.

[0044] According to a Parkinson's disease gait comparison analysis method based on a time series characteristic curve provided by the present invention, the optimal matching path W found by DTW * For each pair of regular points (u i ,v j ), M is defined as formula (9):

[0045]

[0046] in, and Respectively represent u i and v j For the qth feature of , a positive value of the gait comparison metric M indicates an improvement in gait motion.

[0047] According to a Parkinson's disease gait comparative analysis method based on a time series characteristic curve provided by the present invention, the following steps are further performed:

[0048] Select multiple gait datasets with different characteristics and sources, which contain gait information of different groups of people;

[0049] For each gait dataset, divide it into a training set and a test set, where the training set is used for the training process of the algorithm or model, and the test set is used to evaluate the performance of the algorithm or model on unseen data;

[0050] Conduct multiple repeated experiments and cross-validation to obtain the average performance of the algorithm model on different data sets, so as to more accurately evaluate its generalization ability;

[0051] Analyze and compare the evaluation results of the algorithm model on different data sets.

[0052] It can be seen that compared with the prior art, the method provided by the present invention has the following beneficial effects:

[0053] 1. The present invention can obtain the comparative difference of gait curves of Parkinson's patients before and after taking medicine and set a metric parameter to quantify gait changes through the cumulative frequency difference and amplitude difference in the best matching path, so as to accurately evaluate the gait disorder of PD patients before and after taking medicine.

[0054] 2. The present invention designs a frequency-amplitude-sensitive distance measurement method to evaluate the improvement or deterioration of gait by combining local frequency and amplitude features.

[0055] 3. The present invention can more accurately capture the dynamic changes of gait by extracting the position information of key body parts (such as joints) and constructing them into timing curves. It can better reflect the subtle differences in gait than traditional overall profile analysis, especially the subtle changes under the influence of drugs.

[0056] 4. The present invention uses a time series characteristic curve, which can analyze the changes in gait characteristics over time, which is crucial for understanding the dynamic process of drug effects on gait. Through time series analysis, the specific changes in gait at different time points can be observed, thereby more comprehensively evaluating the drug effect.

[0057] 5. The application of digital filter (Savitzky-Golay) helps to reduce the impact of noise and interference on gait feature extraction and improve the robustness of the data.

[0058] 6. The present invention adopts a frequency-amplitude enhanced DTW method, which can effectively deal with the scaling and offset problems of different gait sequences on the time axis, and can more accurately compare the gait differences of different individuals or the same individual under different conditions, even if these gait sequences are not completely aligned in time.

[0059] 7. The present invention integrates local frequency and amplitude differences into the distance metric, taking into account not only the speed change of gait, but also the amplitude change of gait. This comprehensive evaluation method can more comprehensively reflect the complexity and diversity of gait and provide richer information for drug effect evaluation.

[0060] 8. The present invention calculates the curve difference distance and the metric, and the method can quantify the gait difference into a specific value, which is convenient for statistical analysis and comparison. This helps researchers to more objectively evaluate the effect of drugs on gait, as well as the effect differences between different drugs.

[0061] 9. The present invention can provide doctors with important clinical information and guide the formulation and adjustment of treatment plans by monitoring and analyzing the changes in the patient's gait before and after taking the drug.

[0062] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a flow chart of an embodiment of a method for comparative analysis of Parkinson's disease gait based on a time series characteristic curve of the present invention.

[0064] Figure 2 It is a schematic diagram of time series data on leg ratio differences in an embodiment of a method for comparative analysis of Parkinson's disease gait based on a time series characteristic curve of the present invention.

[0065] Figure 3It is a schematic diagram of vertical angle time series data in an embodiment of a Parkinson's disease gait comparison analysis method based on a time series characteristic curve of the present invention.

[0066] Figure 4 It is a schematic diagram of time series data of horizontal angles of ankles in an embodiment of a method for comparative analysis of Parkinson's disease gait based on a time series characteristic curve of the present invention.

[0067] Figure 5 It is a schematic diagram of an original image curve that has not been filtered by a Savitzky-Golay filter in an embodiment of a Parkinson's disease gait comparative analysis method based on a time series characteristic curve of the present invention.

[0068] Figure 6 It is a schematic diagram of an image curve filtered by a Savitzky-Golay filter in an embodiment of a Parkinson's disease gait comparative analysis method based on a time series characteristic curve of the present invention.

[0069] Figure 7 It is a schematic diagram of the amplitude of time series data of the difference in gait characteristics of a user before and after using a drug in an embodiment of a Parkinson's disease gait comparison and analysis method based on a time series characteristic curve of the present invention.

[0070] Figure 8 It is a frequency diagram of time series data of the difference in gait characteristics of a user before and after using a drug in an embodiment of a Parkinson's disease gait comparison and analysis method based on a time series characteristic curve of the present invention.

[0071] Fig. 9 It is a schematic diagram of the distance matrix of FAE-DTW in an embodiment of a method for comparative analysis of Parkinson's disease gait based on a time series characteristic curve of the present invention.

[0072] Fig.10 It is a schematic diagram of a distance matrix of a gait comparison metric M on a path in an embodiment of a Parkinson's disease gait comparison analysis method based on a time series characteristic curve of the present invention. DETAILED DESCRIPTION

[0073] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0074] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0075] See also Figures 1 to 10 This embodiment provides a method for comparing and analyzing Parkinson's disease gait based on a time series characteristic curve, the method comprising the following steps:

[0076] Step S1, obtaining video data containing gait information, where the gait information includes gait information of the user before and after taking the drug;

[0077] Step S2, performing posture recognition on the human body in the video data to extract the position information of key body parts;

[0078] Step S3, calculating gait features based on the extracted key point information, and constructing a time series curve of the changes of these features over time;

[0079] Step S4, using a digital filter to perform data processing on the extracted gait features;

[0080] Step S5, integrating local frequency and amplitude into the distance metric using a frequency-amplitude-enhanced dynamic time warping method to quantify the difference in gait motion;

[0081] Step S6, obtaining the curve difference distance;

[0082] Step S7, calculating the metric based on the cumulative frequency difference and amplitude difference in the optimal regularized path.

[0083] In the above step S2, when performing human posture recognition, the OpenPose framework is used to detect skeletal key points from the walking videos of Parkinson's disease patients captured at different time periods before and after drug treatment.

[0084] Among them, OpenPose is an open source library based on convolutional neural networks and supervised learning, which can detect key points of the face, torso and limbs. It is suitable for single-person and multi-person posture recognition scenarios. The OpenPose model has been trained with a large-scale human skeleton key point dataset to ensure the accuracy of key point detection. This embodiment uses the OpenPose framework to build a BODY25 model to obtain a series of 25 key point coordinates from video data.

[0085] Since it is challenging to directly obtain the motion characteristics of the cadence from the skeleton key points of the BODY25 model, this embodiment defines three gait features that can be directly calculated from the key point coordinates of each frame to estimate the patient's cadence. These gait features are defined as follows:

[0086] 1. Leg length ratio difference R legs : The leg length ratio difference is defined as the difference between the left and right leg length ratios, which is calculated by calculating the ratio of the left and right leg lengths and then finding the difference between these ratios. The leg length is determined using the key points detected using the OpenPose framework. Specifically, the leg length is measured as the distance between the hip and ankle key points of each leg, expressed as formula (1):

[0087]

[0088] Wherein, P9 represents the coordinates of the key point of the right hip; P 11 represents the coordinates of the key point of the right ankle; P 12 Represents the coordinates of the left hip key point; P 14 Represents the coordinates of the left ankle key point.

[0089] 2. The vertical angle of the body It is defined as the angle between the midpoints of the two ankles and a straight line passing through the neck and parallel to the y-axis, expressed as formula (2):

[0090]

[0091] Among them, x1 represents the horizontal coordinate of the neck key point; x 11,14 represents the horizontal coordinate of the midpoint between the right ankle key point and the left ankle key point; P1 represents the coordinate of the neck key point; P 11,14 Represents the coordinates of the midpoints of the left and right ankles;

[0092] 3. Horizontal angle of the ankle It is defined as the angle between the line between the two ankles and the x-axis, expressed as formula (3):

[0093]

[0094] Among them, the horizontal coordinate of the right ankle key point is represented by x 11 ;y 11 Indicates the vertical coordinate of the right ankle key point; x 14 Indicates the horizontal coordinate of the left ankle key point; y 14 Indicates the vertical coordinate of the left ankle keypoint.

[0095] In the above step S4, the digital filter selected in this embodiment is a Savitzky-Golay filter. Among them, this embodiment selects the front walking video of the Parkinson's disease patient for analysis, such as Figure 5 As shown, it can be observed that the time series data fluctuates greatly, which increases the computational load. Therefore, the data needs to be smoothed to remove noise while retaining the key features of the signal. The Savitzky-Golay filter is a digital filter designed to smooth curves, which can enhance the accuracy of the data without changing the trend and width of the original signal.

[0096] In this embodiment, when using the Savitzky-Golay filter, let x k is the gait feature of the kth frame, y k is the smoothed data, and the smoothed curve is expressed as formula (4):

[0097]

[0098] in, is the smoothing value of the frame data, H is the normalization factor, l is the half width of the smoothing window, x k+i is the data point of the k+i frame in the window, h i It is the smoothing coefficient obtained by fitting a polynomial using the least squares method and is used to best represent the trend of the data within the window.

[0099] In the above-mentioned step S5, conventional dynamic time warping (DTW) algorithms mainly focus on dynamically aligning two time series data points to find the shortest distance and the optimal matching path. However, these algorithms generally fail to take into account potential filtering features such as frequency and amplitude. In the case of a single PD patient before and after drug treatment, it is also aimed to quantify the changes in gait characteristic curves to identify gait improvements and deteriorations. To address this limitation, the present embodiment introduces a novel frequency-amplitude enhanced dynamic time warping (FAE-DTW) method for quantifying differences in gait motion. By integrating local frequency and amplitude into the distance metric, FAE-DTW exhibits superior performance in aligning time series with significant harmonic characteristics.

[0100] Specifically, given two time series S1 and S2, with lengths of m and n respectively, represented by S1 = (u1, u2, ..., u m ) and S2=(v1,v2,…,v n ); u in S1 i and S2's v j A fixed-size window ω is selected from the neighborhood of each point in and a subsequence is defined: i =(u i-ω / 2 ,…,u i,…,u i+ω / 2 ) and (v j-ω / 2 ,…,v j ,…,v j+ω / 2 ). Calculating the local frequency and amplitude involves finding peaks in the windowed subsequence to calculate the average period and frequency. The amplitude is determined by calculating the difference between the average and minimum of these peaks. Calculating the local frequency and amplitude based on the local frequency and amplitude involves finding peaks in the windowed subsequence to calculate the average period and frequency. Based on the local frequency and amplitude, the difference between gait sequences is measured by a distance function, which is defined as formula (5):

[0101]

[0102] Among them, V j The local frequency and amplitude are f(v j ) and A(v j ); f(u i ) and A(u i ) is U i local frequency and amplitude; and λ1 and λ2 are weighting parameters that adjust the effect of frequency and amplitude on distance.

[0103] In this embodiment, in order to align two time series, an n×m matrix is ​​constructed, which is the cumulative distance matrix calculated by dynamic programming, and the optimal aligned warping path is obtained by backtracking according to the following recursive function, which is expressed as formula (6):

[0104] D(i,j)=d(u i ,v j )+min(D(i-1,j),D(i,j-1),D(i-1,j-1)), (6)

[0105] In this embodiment, the regularized path needs to satisfy boundary conditions, continuity conditions, and monotonicity conditions. Therefore, the starting point and the end point of the normalized path can only be the two endpoints of the matrix diagonal, and the step length can only be two adjacent matrix elements. The regularized path is expressed as formula (7):

[0106] W=(w1,w2,...,w k ,...,w K ),max(m,n)≤K≤m+n-1 (7)

[0107] Among them, w k is the position (i, j) of the kth observation in the regularized path; then, by minimizing the cost of the regularized path, the DTW distance with the optimal aligned regularized path is obtained, expressed as formula (8):

[0108]

[0109] in, is the set of all possible regular paths, and K is the length of the regular path. The distance is divided by K to eliminate the influence of regular paths of different lengths.

[0110] Specifically, since traditional dynamic time warping (DTW) can only provide gait differences with positive distance values, it cannot indicate whether the motor ability has improved after drug treatment. In order to further evaluate the improvement or deterioration of gait movement, this embodiment designs a novel metric that can be calculated based on the cumulative frequency difference and amplitude difference in the optimal warping path. This method not only measures the similarity between gait sequences, but also indicates whether the gait is improving through positive and negative values. The optimal matching path W found by DTW is * For each pair of regular points (u i ,v j ), M is defined as follows:

[0111]

[0112] in, and Respectively represent u i and v j For the qth feature of , a positive value of the gait comparison metric M indicates an improvement in gait motion.

[0113] In this embodiment, the following is also performed:

[0114] Select multiple gait datasets with different characteristics and sources, which contain gait information of different groups of people;

[0115] For each gait dataset, divide it into a training set and a test set, where the training set is used for the training process of the algorithm or model, and the test set is used to evaluate the performance of the algorithm or model on unseen data;

[0116] Conduct multiple repeated experiments and cross-validation to obtain the average performance of the algorithm model on different data sets, so as to more accurately evaluate its generalization ability;

[0117] Analyze and compare the evaluation results of the algorithm model on different data sets.

[0118] In summary, the present invention can obtain the comparative difference of gait curves of Parkinson's patients before and after medication and set a metric parameter to quantify gait changes through the cumulative frequency difference and amplitude difference in the best matching path, so as to accurately evaluate the gait disorder of PD patients before and after medication. The present invention designs a frequency-amplitude sensitive distance measurement method to evaluate the improvement or deterioration of gait by combining local frequency and amplitude characteristics.

[0119] Furthermore, the present invention can more accurately capture the dynamic changes of gait by extracting the position information of key body parts and constructing them into time-series curves. It can better reflect the subtle differences in gait, especially the subtle changes under the influence of drugs, than traditional overall profile analysis.

[0120] Furthermore, the present invention utilizes a time series characteristic curve, and the method can analyze the changes of gait characteristics over time, which is crucial for understanding the dynamic process of drug effects on gait. Through time series analysis, the specific changes of gait at different time points can be observed, thereby more comprehensively evaluating the drug effect. Among them, the application of digital filters (Savitzky-Golay) helps to reduce the impact of noise and interference on gait feature extraction and improve the robustness of data.

[0121] Furthermore, the present invention adopts a frequency-amplitude enhanced DTW method, which can effectively handle the scaling and offset problems of different gait sequences on the time axis, and can more accurately compare the gait differences of different individuals or the same individual under different conditions, even if these gait sequences are not completely aligned in time.

[0122] Furthermore, the present invention integrates local frequency and amplitude differences into the distance metric, taking into account not only the speed change of gait, but also the amplitude change of gait. This comprehensive evaluation method can more comprehensively reflect the complexity and diversity of gait and provide richer information for drug effect evaluation.

[0123] Furthermore, by calculating the curve difference distance and the metric, the present invention can quantify the gait difference into a specific value, which is convenient for statistical analysis and comparison. This helps researchers to more objectively evaluate the effect of drugs on gait and the difference in effect between different drugs.

[0124] Furthermore, the present invention can provide important clinical information to doctors and guide the formulation and adjustment of treatment plans by monitoring and analyzing the gait changes of patients before and after taking drugs.

[0125] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0126] The above-mentioned embodiments are only preferred embodiments of the present invention and cannot be used to limit the scope of protection of the present invention. Any non-substantial changes and substitutions made by technicians in this field on the basis of the present invention shall fall within the scope of protection required by the present invention.

Claims

1. A method for comparative analysis of Parkinson's disease gait based on time series characteristic curves, characterized in that: The method comprises the following steps: Acquire video data including gait information, the gait information including gait information of the user before and after using the drug; Perform posture recognition on human bodies in video data and extract the location information of key body parts; Based on the extracted key point information, gait features are calculated, and the changes of these features over time are constructed into a time series curve; Using digital filters to process the extracted gait features; The frequency-amplitude-enhanced dynamic time warping method is used to integrate local frequency and amplitude into the distance metric to quantify the differences in gait motion. Get the curve difference distance; The metrics are calculated based on the cumulative frequency differences and amplitude differences in the optimal warping path.

2. The method according to claim 1, characterized in that: When performing human posture recognition, the OpenPose framework is used to detect skeleton key points from video data obtained in different time periods. The BODY25 model is constructed using the OpenPose framework to obtain a series of K key point coordinates from the video data.

3. The method according to claim 1, characterized in that: Define gait features that can be calculated directly from the keypoint coordinates of each frame to estimate the patient's cadence, including: Leg length ratio difference R legs , is defined as the difference between the length ratios of the left and right legs, where the leg length is determined using the key points detected using the OpenPose framework and is measured as the distance between the hip and ankle key points of each leg, expressed as formula (1): Wherein, P9 represents the coordinates of the key point of the right hip; P 11 represents the coordinates of the key point of the right ankle; P 12 Represents the coordinates of the left hip key point; P 14 Represents the coordinates of the left ankle key point. Vertical angle of the body It is defined as the angle between the midpoints of the two ankles and a straight line passing through the neck and parallel to the y-axis, expressed as formula (2): Among them, x1 represents the horizontal coordinate of the neck key point; x 11,14 represents the horizontal coordinate of the midpoint between the right ankle key point and the left ankle key point; P1 represents the coordinate of the neck key point; P 11,14 Represents the coordinates of the midpoints of the left and right ankles; Horizontal angle of ankle It is defined as the angle between the line between the two ankles and the x-axis, expressed as formula (3): Among them, the horizontal coordinate of the right ankle key point is represented by x 11 ;y 11 Indicates the vertical coordinate of the right ankle key point; x 14 Indicates the horizontal coordinate of the left ankle key point; y 14 Indicates the vertical coordinate of the left ankle keypoint.

4. The method according to claim 1, characterized in that: The digital filter is a Savitzky-Golay filter.

5. The method according to claim 4, characterized in that: When using the Savitzky-Golay filter, let x k is the gait feature of the kth frame, y k is the smoothed data, and the smoothed curve is expressed as formula (4): in, is the smoothing value of the frame data, H is the normalization factor, l is the half width of the smoothing window, x k+i is the data point of the k+i frame in the window, h i It is the smoothing coefficient obtained by fitting a polynomial using the least squares method and is used to best represent the trend of the data within the window.

6. The method according to claim 1, characterized in that The dynamic time warping method using frequency-amplitude enhancement integrates local frequency and amplitude into the distance metric to quantify the differences in gait motion, including: Given two time series S1 and S2, with lengths of m and n respectively, represented by S1 = (u1, u2, ..., u m ) and S2=(v1,v2,…,v n ); u in S1 i and S2's v j A fixed-size window ω is selected from the neighborhood of each point in and a subsequence is defined: i =(u i-ω / 2 ,…,u i ,…,u i+ω / 2 ) and (v j-ω / 2 ,…,v j ,…,v j+ω / 2 ); Computing local frequency and amplitude involves finding peaks in the windowed subsequence to calculate the average period and frequency; wherein the amplitude is determined by calculating the difference between the average and minimum of these peaks, and calculating the local frequency and amplitude based on the local frequency and amplitude involves finding the peak in the windowed subsequence to calculate the average period and frequency; According to the local frequency and amplitude, the difference between gait sequences is measured by the distance function, and the distance function is defined as formula (5): Among them, V j The local frequency and amplitude are f(v j ) and A(v j ); f(u i ) and A(u i ) is U i local frequency and amplitude; and λ1 and λ2 are weighting parameters that adjust the effect of frequency and amplitude on distance.

7. The method according to claim 6, characterized in that: In order to align two time series, an n×m matrix is ​​constructed, which is the cumulative distance matrix calculated by dynamic programming, and the optimal aligned warping path is obtained by backtracking according to the following recursive function, expressed as formula (6): D(i,j)=d(u i ,v j )+min(D(i-1,j),D(i,j-1),D(i-1,j-1)), (6)。 8. The method according to claim 7, characterized in that: The regularized path needs to meet boundary conditions, continuity conditions and monotonicity conditions. Therefore, the starting point and end point of the normalized path can only be the two endpoints of the matrix diagonal, and the step length can only be two adjacent matrix elements. The regularized path is expressed as formula (7): W=(w1,w2,...,w k ,...,w K ),max(m,n)≤K≤m+n-1 (7) Among them, w k is the position (i, j) of the kth observation in the regularized path; then, by minimizing the cost of the regularized path, the DTW distance with the optimal aligned regularized path is obtained, expressed as formula (8): in, is the set of all possible regular paths, and K is the length of the regular path. The distance is divided by K to eliminate the influence of regular paths of different lengths.

9. The method according to claim 8, characterized in that: The best matching path W found in DTW * For each pair of regular points (u i ,v j ), M is defined as formula (9): Among them, u i q and v j q Respectively represent u i and v j For the qth feature of , a positive value of the gait comparison metric M indicates an improvement in gait motion.

10. The method according to any one of claims 1 to 9, characterized in that: Also execute: Select multiple gait datasets with different characteristics and sources, which contain gait information of different groups of people; For each gait dataset, divide it into a training set and a test set, where the training set is used for the training process of the algorithm or model, and the test set is used to evaluate the performance of the algorithm or model on unseen data; Conduct multiple repeated experiments and cross-validation to obtain the average performance of the algorithm model on different data sets, so as to more accurately evaluate its generalization ability; Analyze and compare the evaluation results of the algorithm model on different data sets.

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