A motor ability analysis method based on enhanced processing of rehabilitation data

Abstract

The present invention relates to a method for analyzing athletic ability based on enhanced processing of rehabilitation data, and relates to the field of data processing. The method comprises: performing a search based on a multimodal rehabilitation data sequence to obtain a primary similar user set; determining athletic demand information in combination with life scenarios, screening the primary similar user set to obtain a secondary similar user set; obtaining an athletic ability analysis report set based on the secondary similar user set analysis, and training a athletic ability analyzer; utilizing the athletic ability analyzer to perform analysis based on the multimodal rehabilitation data sequence and output a target athletic ability analysis report. The present invention can solve the problem that traditional analysis methods cannot fully reflect the patient's true athletic ability and have insufficient analysis accuracy and practicality; by utilizing artificial intelligence and big data technology, and combining the patient's life scenarios and exercise needs to perform athletic ability analysis, the scientificity, accuracy, and practicality of the patient's athletic ability analysis can be significantly improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method for analyzing motor ability through enhanced processing of rehabilitation data. Background Art

[0002] In the field of modern rehabilitation medicine, the assessment of patients' motor ability is a key link in formulating rehabilitation plans. Especially for patients with cerebrovascular diseases (such as stroke and cerebral infarction), the recovery of motor ability is directly related to the patient's quality of life.

[0003] Currently, most motor ability assessment methods rely on regular doctor examinations and standardized scoring systems. This method cannot fully reflect the dynamic changes of patients during the rehabilitation process, nor can it timely capture fluctuations in motor ability. In addition, each patient's rehabilitation process and life needs are different. Traditional methods are difficult to dynamically adjust the assessment criteria according to the patient's actual situation, and fail to effectively combine the patient's actual life needs (such as whether they can go to the toilet or walk independently), resulting in the lack of practical significance of the assessment and plan implementation. Summary of the Invention

[0004] The present invention aims to solve the technical problems that traditional motor ability analysis methods cannot fully reflect the patient's true motor ability and have insufficient analysis accuracy and practicality. It provides a motor ability analysis method based on rehabilitation data enhancement processing to solve the problem.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] In a first aspect, the present invention provides a method for analyzing athletic ability by enhanced processing of rehabilitation data, comprising: collecting a multimodal rehabilitation data sequence of a target user, taking the multimodal rehabilitation data sequence as a benchmark, performing a retrieval based on big data, and obtaining a similar user set, wherein the retrieval includes a data similarity comparison and a data fluctuation similarity comparison; determining athletic demand information in combination with the target user's life scenario, screening the similar user set based on the athletic demand information, and obtaining a secondary similar user set; based on the secondary similar user set, analyzing and obtaining an athletic ability analysis report set, and training a athletic ability analyzer; utilizing the athletic ability analyzer to perform analysis according to the multimodal rehabilitation data sequence, outputting a target athletic ability analysis report, and formulating an athletic rehabilitation plan for the next rehabilitation stage according to the target athletic ability analysis report.

[0007] Preferably, the method for analyzing athletic ability with enhanced processing of rehabilitation data also includes: taking the end of the rehabilitation stage as the data collection node, continuously recording the multimodal rehabilitation data of the target user, and constructing a multimodal rehabilitation data sequence; obtaining the number of data collection nodes, and configuring a first similarity threshold, wherein the first similarity threshold shows a decreasing trend with the increase in the number of collection nodes; taking the multimodal rehabilitation data sequence as a benchmark, performing a search based on big data in accordance with the order of data collection nodes, setting users whose data of all data collection nodes meet the first similarity threshold as initial similar users, and obtaining an initial similar user set; performing rehabilitation volatility analysis on the multimodal rehabilitation data sequence, and screening the initial similar user set according to the multivariate rehabilitation volatility coefficient to obtain the primary similar user set.

[0008] Preferably, the method for analyzing motor ability by enhanced processing of rehabilitation data further includes: the multimodal rehabilitation data at least includes physiological data, functional data, imaging data and rehabilitation treatment records.

[0009] Preferably, the method for analyzing athletic ability by enhancing rehabilitation data processing further includes: respectively calculating the data mean and data standard deviation of the multimodal rehabilitation data sequence, setting the ratio of the data mean to the data standard deviation as the rehabilitation fluctuation coefficient, and obtaining the multivariate rehabilitation fluctuation coefficient; respectively calculating and obtaining the historical multivariate rehabilitation fluctuation coefficient set of the initial similar user set; performing similarity screening on the historical multivariate rehabilitation fluctuation coefficient set according to the multivariate rehabilitation fluctuation coefficient, selecting the initial similar users whose multivariate rehabilitation fluctuation coefficients all meet the first similarity threshold as primary similar users, and obtaining the primary similar user set.

[0010] Preferably, the method for analyzing exercise ability by enhanced processing of rehabilitation data further includes: determining exercise demand information in combination with life scenario analysis of the target user, wherein the exercise demand information includes a number of autonomously completed exercise types; based on the several autonomously completed exercise types, screening the first-level similar user set, setting the first-level similar users whose exercise type intersection ratio is greater than a predetermined threshold as secondary similar users, and obtaining a secondary similar user set, wherein the exercise type intersection ratio is the ratio of the number of exercise type intersections to the total number of exercise types of the first-level similar users, and the predetermined threshold is 80%.

[0011] Preferably, the method for analyzing athletic ability by enhancing rehabilitation data processing further includes: based on big data, obtaining multiple sample multimodal rehabilitation data sequences of multiple secondary similar users in the secondary similar user set, and obtaining multiple sample athletic ability analysis reports; constructing K athletic ability analysis plug-ins based on a feedforward neural network, wherein K is an integer greater than or equal to 10; using the multiple sample multimodal rehabilitation data sequences and the multiple sample athletic ability analysis reports as training data, and dividing them into K parts, selecting them with replacement K times to construct a first training set, and iteratively selecting them K times to obtain K training sets; using the K training sets to respectively perform supervised training and verification on the K athletic ability analysis plug-ins until predetermined constraints are met, thereby obtaining K converged athletic ability analysis plug-ins, which are combined to obtain the athletic ability analyzer.

[0012] Preferably, the method for analyzing sports ability by enhancing rehabilitation data further includes: counting the proportion of sports types completed independently by each secondary similar user within a preset time range after the end of treatment, and constructing a sample sports ability analysis report based on the types of sports completed independently; and sequentially analyzing to obtain multiple sample sports ability analysis reports for multiple secondary similar users.

[0013] Preferably, the method for analyzing athletic ability with enhanced processing of rehabilitation data also includes: performing mean calculation on the multiple sample multimodal rehabilitation data sequences to obtain a sample multimodal rehabilitation data mean sequence; performing deviation analysis on the multimodal rehabilitation data sequence and the sample multimodal rehabilitation data mean sequence to obtain a data deviation ratio; calculating the ratio of the data deviation ratio to the historical maximum data deviation ratio, multiplying it by K and rounding it up to obtain the number of plug-in selections P; randomly selecting P convergent athletic ability analysis plug-ins from the K convergent athletic ability analysis plug-ins of the athletic ability analyzer, analyzing the multimodal rehabilitation data sequence, outputting P predicted athletic ability analysis reports, and obtaining the target athletic ability analysis report after mean calculation.

[0014] The beneficial effects of the present invention are as follows: by collecting the multimodal rehabilitation data sequence of the target user, taking the multimodal rehabilitation data sequence as a benchmark, a search is performed based on big data to obtain a similar user set, wherein the search includes data similarity comparison and data fluctuation similarity comparison; then, the motion demand information is determined in combination with the target user's life scene, and the motion demand information is used as a benchmark to screen the similar user set to obtain a secondary similar user set; further based on the secondary similar user set, a motion ability analysis report set is analyzed and obtained, and a motion ability analyzer is trained to obtain; then, the motion ability analyzer is used to analyze according to the multimodal rehabilitation data sequence and output a target motion ability analysis report; finally, an exercise rehabilitation plan for the next rehabilitation stage is formulated based on the target motion ability analysis report. In other words, by utilizing artificial intelligence and big data technology, and combining the patient's life scene and exercise needs to perform motion ability analysis, the scientificity, accuracy and practicality of the patient's motion ability analysis can be significantly improved, thereby formulating a more accurate and effective exercise rehabilitation plan for the patient. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic flow chart of a method for analyzing motor performance through enhanced rehabilitation data processing provided by the present invention;

[0016] Figure 2 This is a flow chart of obtaining a similar user set in a motor ability analysis method for enhanced rehabilitation data processing provided by the present invention. DETAILED DESCRIPTION

[0017] 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 those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0018] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the specified features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0019] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or illustration". Any embodiment of the present invention described as "for example" is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed herein.

[0020] Examples, such as Figure 1 As shown, an embodiment of the present invention provides a method for analyzing athletic performance through enhanced processing of rehabilitation data, which specifically includes the following steps:

[0021] S10: Collecting a multimodal rehabilitation data sequence of a target user, and using the multimodal rehabilitation data sequence as a benchmark, performing a search based on big data to obtain a similar user set, wherein the search includes data similarity comparison and data fluctuation similarity comparison.

[0022] Further, if Figure 2 As shown, step S10 of the present invention further includes:

[0023] S11: Taking the end of the rehabilitation stage as the data collection node, continuously record the multimodal rehabilitation data of the target user and construct a multimodal rehabilitation data sequence.

[0024] Furthermore, step S11 of the present invention further includes:

[0025] S111: The multimodal rehabilitation data at least includes physiological data, functional data, imaging data and rehabilitation treatment records.

[0026] Specifically, during the patient's rehabilitation process, each rehabilitation stage usually has different treatment goals and evaluation criteria. The end of the rehabilitation stage refers to the evaluation node at the end of each rehabilitation cycle (which can be set according to the actual scenario, such as setting a rehabilitation cycle every 5 days). It is usually the patient's final state of treatment and recovery at this stage. This node is crucial for recording the patient's rehabilitation effect at a specific stage because it can reflect the patient's overall rehabilitation status and provide guidance for the next step of treatment. At the end of each rehabilitation stage, the data collection node can be used to comprehensively evaluate the patient's motor ability, physical function and overall rehabilitation progress at that stage.

[0027] The end of the rehabilitation phase serves as the data collection node, continuously recording the target user's multimodal rehabilitation data. This means that during the rehabilitation process, comprehensive data collection and recording of the patient's rehabilitation process from multiple dimensions and perspectives is performed. This data can reflect the patient's rehabilitation progress and status in real time. Multimodal rehabilitation data includes at least physiological data, functional data, imaging data, and rehabilitation treatment records. Physiological data reflects basic indicators of the patient's physical health status, such as heart rate, blood oxygen saturation, blood pressure, body temperature, and weight. Functional data primarily addresses the patient's motor function recovery during the rehabilitation process, such as range of motion, muscle strength, balance, walking distance, and speed. Imaging data provides a morphological basis for motor ability assessment, with common imaging data including CT scans and MRI images. Rehabilitation treatment records include the content and progress of all treatments received by the patient, including but not limited to physical therapy, exercise therapy, and medication. By integrating these multiple data sources (physiological data, functional data, imaging data, and rehabilitation treatment records), a multimodal rehabilitation data series is constructed. This data series, by tracking the changes in various data throughout the patient's rehabilitation phase over a long period of time, can provide a detailed record of the patient's rehabilitation progress. Through comprehensive analysis of multimodal data, the accuracy and practicality of rehabilitation assessment can be significantly improved.

[0028] S12: Obtain the number of data acquisition nodes and configure a first similarity threshold, wherein the first similarity threshold decreases as the number of acquisition nodes increases; S13: Based on the multimodal rehabilitation data sequence and in accordance with the order of data acquisition nodes, perform a search based on big data, set users whose data from all data acquisition nodes meet the first similarity threshold as initial similar users, and obtain an initial similar user set.

[0029] Specifically, first, determine the number of data collection nodes. Data collection nodes refer to events that record data at different time points during rehabilitation treatment based on the patient's specific situation. The number of these nodes directly affects the accuracy and coverage of similarity calculations. Next, a first similarity threshold is configured according to the number of data acquisition nodes. The first similarity threshold is a standard used to measure the consistency of the similarity between two sets of multimodal data sequences. The first similarity threshold decreases with the increase in the number of acquisition nodes. That is, as the number of acquisition nodes increases, the number of comparable points and information in the data sequence increases. The initial similarity threshold should be gradually lowered to allow more data sets to be compared, thereby expanding the range of similar user sets. When there are fewer data acquisition nodes, a higher first similarity threshold is set to ensure higher data similarity and screen out a more accurate similar user set. As the number of acquisition nodes increases, the threshold gradually decreases, allowing a wider range of user data sets to be selected into the similar user set. For example, the initial similarity threshold is set to 0.85. For each additional data acquisition node, the similarity threshold decreases by 0.015. That is, if the number of data acquisition nodes is 10, the first similarity threshold is 0.85-0.015*10, which is equal to 0.7.

[0030] Next, a big data search is performed based on the multimodal rehabilitation data sequence and the order of data collection nodes. For each data collection node, the similarity between users is calculated. This similarity can be calculated using a variety of methods, such as cosine similarity, Euclidean distance, or by extracting features through deep learning algorithms (such as autoencoders or convolutional neural networks) for comparison. A big data search is performed to calculate the similarity of each user at each data collection node, and to determine whether each user's multimodal data sequence meets a set first similarity threshold. Only when the user's similarity across all data collection nodes exceeds the threshold is the user included in the initial similar user set. Users who meet the similarity requirements for all data collection nodes are then designated as initial similar users, resulting in an initial similar user set. The initial similar user set contains multiple candidate users whose performance on the multimodal data is highly similar to that of the target user. This big data search ensures a comprehensive comparison of the multimodal data sequence, obtains an accurate similar user set, and further improves the reliability and practicality of the analysis results.

[0031] S14: performing rehabilitation volatility analysis on the multimodal rehabilitation data sequence, and screening the initial similar user set according to the multivariate rehabilitation volatility coefficient to obtain the primary similar user set.

[0032] Furthermore, step S14 of the present invention further includes:

[0033] S141: Calculate the data mean and data standard deviation of the multimodal rehabilitation data sequence respectively, set the ratio of the data mean to the data standard deviation as the rehabilitation fluctuation coefficient, and obtain the multivariate rehabilitation fluctuation coefficient; S142: Calculate and obtain the historical multivariate rehabilitation fluctuation coefficient set of the initial similar user set respectively; S143: According to the multivariate rehabilitation fluctuation coefficient, perform similarity screening on the historical multivariate rehabilitation fluctuation coefficient set, select the initial similar users whose multivariate rehabilitation fluctuation coefficients all meet the first similarity threshold as the first similar users, and obtain the first similar user set.

[0034] Specifically, for the target user's multimodal rehabilitation data series, the mean and standard deviation of each data series (e.g., physiological data, functional data, and imaging data) are calculated. The ratio of the mean to the standard deviation of each data series is then defined as the rehabilitation fluctuation coefficient. The rehabilitation fluctuation coefficient is an important indicator of rehabilitation data volatility. A higher fluctuation coefficient indicates greater volatility in the data series. For example, a high fluctuation coefficient indicates that the series exhibited significant volatility during acquisition, implying significant fluctuations or instability in the patient's rehabilitation process. A low fluctuation coefficient indicates that the data changes more steadily, suggesting a more stable rehabilitation process. The multivariate rehabilitation fluctuation coefficient is then calculated sequentially. The multivariate rehabilitation fluctuation coefficient includes multiple rehabilitation fluctuation coefficients for multiple rehabilitation data series. Specifically, the target user's multimodal rehabilitation data series contains multiple different data types, such as physiological data, functional data, and imaging data, each of which has a corresponding rehabilitation fluctuation coefficient.

[0035] Next, for each user in the initial similar user set, their historical multivariate rehabilitation fluctuation coefficient is calculated to obtain a historical multivariate rehabilitation fluctuation coefficient set. This historical multivariate rehabilitation fluctuation coefficient set is then screened for similarity based on the multivariate rehabilitation fluctuation coefficient. For example, the Euclidean distance or cosine similarity between the target user and the initial similar users is calculated to quantify the difference. Initial similar users whose multivariate rehabilitation fluctuation coefficients are all greater than the first similarity threshold are further selected as primary similar users to obtain a primary similar user set. By analyzing rehabilitation volatility to screen the initial similar user set, the screening range can be further narrowed, selecting users whose volatility is more similar to that of the target user.

[0036] S20: Determine exercise demand information based on the target user's life scenario, and screen the primary similar user set based on the exercise demand information to obtain a secondary similar user set.

[0037] Furthermore, step S20 of the present invention further includes:

[0038] S21: Determine the exercise demand information in combination with the life scenario analysis of the target user, wherein the exercise demand information includes a number of self-completed exercise types; S22: Based on the said number of self-completed exercise types, screen the said first-level similar user set, and set the first-level similar users whose exercise type intersection ratio is greater than a predetermined threshold as secondary similar users, to obtain a secondary similar user set, wherein the exercise type intersection ratio is the ratio of the number of exercise type intersections to the total number of exercise types of the first-level similar users, and the predetermined threshold is 80%.

[0039] Specifically, first, the exercise demand information is determined in combination with the target user's life scenario analysis, where the exercise demand information includes several types of autonomous exercise. By observing and recording the patient's daily life activities, the patient's actual exercise needs are understood, and these needs are converted into quantifiable exercise types, such as walking indoors, going to the toilet independently, climbing stairs, dressing independently, cooking independently, standing up and walking, etc. These autonomous exercise types provide basic data for subsequent exercise ability analysis, which can help to more accurately evaluate the patient's rehabilitation status and formulate personalized rehabilitation treatment plans.

[0040] Next, based on the several self-completed exercise types, the set of first-level similar users is screened. First, the intersection of exercise types needs to be calculated, that is, how many exercise types are common between the exercise needs of the target user and the exercise needs of the first-level similar users (that is, exercise types that can both be completed independently). Then, the ratio of the number of exercise type intersections to the total number of exercise types of the first-level similar users is set as the exercise type intersection ratio, and the intersection ratios of multiple exercise types for multiple first-level similar users are obtained. Then, a predetermined threshold (80%) is obtained, and first-level similar users whose exercise type intersection ratio is greater than the predetermined threshold are set as secondary similar users. That is, if the intersection ratio is greater than 80%, the user is considered to have similar exercise needs to the target user, and a set of secondary similar users is obtained. Among them, the exercise needs of the secondary similar users are consistent with those of the target user to a high degree, which can provide more accurate data support for subsequent exercise capacity analysis, rehabilitation program formulation, etc., and provide a more reliable reference basis for personalized rehabilitation programs.

[0041] S30: Based on the secondary similar user set, analyze and obtain a set of sports ability analysis reports, and train a sports ability analyzer.

[0042] Furthermore, step S30 of the present invention further includes:

[0043] S31: Based on big data, obtain multiple sample multimodal rehabilitation data sequences of multiple secondary similar users in the secondary similar user set, and obtain multiple sample sports ability analysis reports.

[0044] Furthermore, step S31 of the present invention further includes:

[0045] S311: Counting the proportion of exercise types completed independently by each secondary similar user within a preset time range after the end of treatment, and constructing a sample exercise ability analysis report based on the types of exercise completed independently; S312: Analyzing in sequence to obtain multiple sample exercise ability analysis reports for multiple secondary similar users.

[0046] Specifically, based on big data, for each secondary similar user, multimodal data is extracted from their rehabilitation process, resulting in multiple sample multimodal rehabilitation data sequences for multiple secondary similar users in the secondary similar user set. Next, for each secondary similar user, within a preset timeframe after the completion of rehabilitation treatment (e.g., within three months after treatment), the proportion of exercise types they were able to complete independently was counted. For example, after treatment, the types of exercise a user could independently complete included indoor walking, climbing stairs, and using the toilet independently. The completion percentage of these exercise types within the preset timeframe was recorded. A sample exercise capacity analysis report was then constructed based on the types of exercise completed independently. The sample exercise capacity analysis report is shown in Table 1:

[0047] Table 1: Sample Athletic Performance Analysis Report

[0048]

[0049] Then, multiple secondary similar users are analyzed in sequence to obtain multiple sample sports ability analysis reports of the multiple secondary similar users.

[0050] S32: Construct K sports ability analysis plug-ins based on a feedforward neural network, where K is an integer greater than or equal to 10; S33: Use the multiple sample multimodal rehabilitation data sequences and multiple sample sports ability analysis reports as training data, and divide them into K equal parts, select them with replacement K times, construct a first training set, and iteratively select them K times to obtain K training sets; S34: Use the K training sets to perform supervised training and verification on the K sports ability analysis plug-ins respectively until the predetermined constraints are met, and obtain K converged sports ability analysis plug-ins, which are combined to obtain the sports ability analyzer.

[0051] Specifically, a feedforward neural network is one of the most basic artificial neural network models, which is widely used in machine learning and deep learning tasks. First, K motor ability analysis plug-ins are constructed based on the feedforward neural network, where K is an integer greater than or equal to 10. The value of K can be set according to actual needs. For example, K is set to 20. The choice of K affects the capacity and computational complexity of the model. The larger the K value, the more features and patterns the model can learn, which may improve the accuracy of motor ability analysis. However, at the same time, a large K value will also increase computing resources and time consumption. Each plug-in represents an independent feedforward neural network, which is used to analyze rehabilitation data and predict the patient's motor ability. It includes an input layer, multiple hidden layers and an output layer. The input data of the input layer is a multimodal rehabilitation data sequence, and the output data is a motor ability analysis report.

[0052] Next, the multiple multimodal rehabilitation data sequences and multiple athletic performance analysis reports are used as training data and divided equally into K parts, resulting in K datasets. The K datasets are then sampled K times with replacement to construct the first training set. The same method is then used to iteratively sample K times, resulting in K training sets. This resampling with replacement method effectively improves the model's generalization and reduces overfitting. Each iteration ensures that every data sample participates in training, while also allowing the model's accuracy and robustness to be verified using a validation set. Ultimately, the results of the K training sets can be combined to form a more robust and robust athletic performance analyzer.

[0053] Then, taking the sample multimodal rehabilitation data sequence as input and the sample athletic ability analysis report as supervision, the K training sets are used to perform supervised training and verification on the K athletic ability analysis plug-ins respectively. During the training process, the multimodal rehabilitation data sequence of each training sample is input into the corresponding plug-in model, and the input data is forward propagated through the feedforward neural network to calculate the output result, which represents the prediction of athletic ability; then, the predicted result is compared with the actual athletic ability analysis report, and the loss value is calculated. The loss function is usually the mean square error, which is used to measure the gap between the model output and the true value; then, the gradient of the loss function with respect to the model parameters is calculated through the backpropagation algorithm, and the weights are updated using the gradient descent method. The goal of each update is to minimize the loss function, thereby optimizing the model's parameters. Multiple iterations are performed until the loss function stabilizes or reaches a preset stopping condition. After each training session, the model is evaluated using a validation set. The validation set is data not used in training and is used to test the model's generalization ability. The training sets of different motor ability analysis plug-ins are exchanged and used as validation sets for verification. When the training process is complete, the loss and evaluation metrics of the validation set are used to determine whether each plug-in has converged. For example, the verification loss drops to a predetermined threshold. K converged motor ability analysis plug-ins are obtained, and the motor ability analyzer is obtained by combining these K converged motor ability analysis plug-ins. Through this training and verification process, the motor ability analysis capabilities of each plug-in are gradually optimized using multimodal rehabilitation data sequences and motor ability analysis reports from multiple samples. Ultimately, they are combined into a system that can provide patients with personalized and scientific motor ability analysis, significantly improving the intelligence and efficiency of motor ability analysis.

[0054] S40: Utilizing the exercise capacity analyzer, analyzing the multimodal rehabilitation data sequence, outputting a target exercise capacity analysis report, and formulating an exercise rehabilitation program for the next rehabilitation stage based on the target exercise capacity analysis report.

[0055] Furthermore, step S40 of the present invention further includes:

[0056] S41: Calculate the mean of the multiple sample multimodal rehabilitation data sequences to obtain a sample multimodal rehabilitation data mean sequence; S42: Perform deviation analysis on the multimodal rehabilitation data sequence and the sample multimodal rehabilitation data mean sequence to obtain a data deviation ratio; S43: Calculate the ratio of the data deviation ratio to the historical maximum data deviation ratio, multiply it by K and round it up to obtain the number of plug-in selections P; S44: Randomly select P convergent motion ability analysis plug-ins from the K convergent motion ability analysis plug-ins of the motion ability analyzer, analyze the multimodal rehabilitation data sequence, output P predicted motion ability analysis reports, and obtain the target motion ability analysis report after mean calculation.

[0057] Specifically, first, the multimodal rehabilitation data sequences of multiple samples are processed, and the mean of each data sequence is calculated. Assuming that each sample's multimodal rehabilitation data sequence includes different types of data (such as physiological data, functional data, and imaging data), the mean is calculated for each data type, and the means of all data types are combined in sequence order to obtain a sample multimodal rehabilitation data mean sequence. Next, a deviation analysis is performed on the multimodal rehabilitation data sequence and the sample multimodal rehabilitation data mean sequence. The goal of the deviation analysis is to quantify the deviation between each data point and the mean and calculate the data deviation ratio. For example, for each sample data point, the difference between its difference and the sample multimodal rehabilitation data mean sequence is calculated, and the deviations of all data points are normalized. After the mean is calculated, the data deviation ratio is obtained. The data deviation ratio reflects the overall deviation between the multimodal data sequence and the mean sequence. A smaller value indicates that the data is closer to the mean, and a larger value indicates a more significant deviation.

[0058] Next, the ratio of the data deviation ratio to the historical maximum data deviation ratio (which can be calculated based on historical data) is calculated. This ratio reflects the degree of deviation between the current data and the historical data. The ratio of the data deviation ratio to the historical maximum data deviation ratio is then multiplied by K and rounded to the nearest integer to obtain the number of plug-ins to be selected, P. P represents the number of plug-ins to be randomly selected from the K converged motor performance analysis plug-ins. A larger P indicates a greater degree of data deviation, and more plug-ins are required for multi-angle analysis. Next, based on the calculated P, P plug-ins are randomly selected from the K converged motor performance analysis plug-ins. These plug-ins are then used to analyze the target user's multimodal rehabilitation data sequence. Each plug-in independently performs motor performance analysis based on the input multimodal data sequence and outputs a predicted result, namely a predicted motor performance analysis report. This results in P predicted motor performance analysis reports, which are then averaged to obtain a target motor performance analysis report.

[0059] Through deviation analysis based on multimodal data sequences and athletic ability analysis reports, the number of plug-in selections is dynamically calculated, and the final target athletic ability analysis report is generated based on the independent analysis results of multiple plug-ins. This method uses changes in data deviations to adjust the depth and accuracy of the analysis to ensure the scientificity and practicality of athletic ability assessment. At the same time, it can reduce unnecessary waste of computing resources while ensuring analysis accuracy and improve analysis efficiency.

[0060] Finally, an exercise rehabilitation plan for the next rehabilitation stage is formulated based on the target exercise capacity analysis report. That is, based on the patient's exercise capacity assessment results in the current rehabilitation stage, their deficiencies, potential areas, and types of exercise that need to be strengthened are analyzed, so as to formulate a personalized and precise rehabilitation plan to ensure that each patient can receive the best training support at the appropriate rehabilitation stage and gradually improve their exercise capacity.

[0061] The embodiment of the present invention provides a method for analyzing motor ability by enhancing rehabilitation data, which has at least the following technical effects:

[0062] By collecting the multimodal rehabilitation data sequence of the target user, using the multimodal rehabilitation data sequence as a benchmark, a search is performed based on big data to obtain a similar user set, wherein the primary search includes data similarity comparison and data fluctuation similarity comparison; then, the exercise demand information is determined in combination with the target user's life scenario, and the exercise demand information is used as a benchmark to screen the primary similar user set to obtain a secondary similar user set; further based on the secondary similar user set, an exercise capacity analysis report set is analyzed and obtained, and an exercise capacity analyzer is trained; then, the exercise capacity analyzer is used to analyze according to the multimodal rehabilitation data sequence and output a target exercise capacity analysis report; finally, an exercise rehabilitation plan for the next rehabilitation stage is formulated based on the target exercise capacity analysis report. In other words, by utilizing artificial intelligence and big data technology to analyze exercise capacity in combination with the patient's life scenario and exercise demand, the scientificity, accuracy and practicality of the patient's exercise capacity analysis can be significantly improved, thereby formulating a more accurate and effective exercise rehabilitation plan for the patient.

[0063] Although preferred embodiments of the present invention have been described, additional changes and modifications to these embodiments may occur to those skilled in the art once the basic inventive concepts become known.

[0064] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for analyzing motor ability by enhancing rehabilitation data processing, characterized in that: include: Collecting a multimodal rehabilitation data sequence of a target user, and using the multimodal rehabilitation data sequence as a benchmark, performing a search based on big data to obtain a similar user set, wherein the search includes a data similarity comparison and a data fluctuation similarity comparison; Determine exercise demand information based on the target user's life scenario, and use the exercise demand information as a benchmark to filter the primary similar user set to obtain a secondary similar user set; Based on the secondary similar user set, analyzing and obtaining a set of athletic ability analysis reports, and training a athletic ability analyzer; Utilizing the motor ability analyzer to analyze the multimodal rehabilitation data sequence, output a target motor ability analysis report, and formulate a motor rehabilitation program for the next rehabilitation stage based on the target motor ability analysis report; The multimodal rehabilitation data sequence of the target user is collected, and a search is performed based on big data using the multimodal rehabilitation data sequence as a benchmark to obtain a similar user set, including: Taking the end of the rehabilitation stage as the data collection node, the multimodal rehabilitation data of the target user is continuously recorded to construct a multimodal rehabilitation data sequence; Obtain the number of data collection nodes and configure a first similarity threshold, wherein the first similarity threshold is a standard used to measure the consistency of similarity between two sets of multimodal data sequences and decreases as the number of collection nodes increases; Based on the multimodal rehabilitation data sequence, a search is performed based on big data in the order of data collection nodes, and users whose data of all data collection nodes meet the first similarity threshold are set as initial similar users to obtain an initial similar user set; performing rehabilitation volatility analysis on the multimodal rehabilitation data sequence, and screening the initial similar user set according to a multivariate rehabilitation volatility coefficient to obtain the primary similar user set; The step of performing rehabilitation volatility analysis on the multimodal rehabilitation data sequence and screening the initial similar user set according to the multivariate rehabilitation volatility coefficient includes: Calculating the data mean and data standard deviation of the multimodal rehabilitation data sequence respectively, setting the ratio of the data mean to the data standard deviation as the rehabilitation fluctuation coefficient, and obtaining the multivariate rehabilitation fluctuation coefficient; Calculate and obtain the historical multivariate rehabilitation fluctuation coefficient set of the initial similar user set respectively; performing similarity screening on the historical multivariate rehabilitation fluctuation coefficient set according to the multivariate rehabilitation fluctuation coefficient, selecting initial similar users whose multivariate rehabilitation fluctuation coefficients all meet the first similarity threshold as primary similar users, and obtaining the primary similar user set; The exercise demand information is determined in combination with the target user's life scenario, and the primary similar user set is screened based on the exercise demand information to obtain the secondary similar user set, including: Determine exercise demand information based on target users' life scenarios, wherein the exercise demand information includes several types of self-completed exercise; Based on the plurality of self-completed exercise types, the primary similar user set is screened, and the primary similar users whose exercise type intersection ratio is greater than a predetermined threshold are set as secondary similar users to obtain a secondary similar user set, wherein the predetermined threshold is 80%; The method of analyzing and obtaining a set of athletic ability analysis reports based on the secondary similar user set and training a athletic ability analyzer includes: Based on the big data, a plurality of sample multimodal rehabilitation data sequences of a plurality of secondary similar users in the secondary similar user set are obtained, and a plurality of sample sports ability analysis reports are obtained; Constructing K sports performance analysis plug-ins based on a feedforward neural network, where K is an integer greater than or equal to 10; The plurality of sample multimodal rehabilitation data sequences and the plurality of sample motor ability analysis reports are used as training data, and are divided into K equal parts, and the data are selected K times with replacement to construct a first training set, and the data are iteratively selected K times to obtain K training sets; Using the K training sets, supervised training and verification are performed on the K athletic ability analysis plug-ins respectively until predetermined constraints are met, thereby obtaining K converged athletic ability analysis plug-ins, which are combined to obtain the athletic ability analyzer; Among them, obtain multiple sample sports ability analysis reports, including: Count the proportion of exercise types that each secondary similar user completed independently within the preset time range after the end of treatment, and build a sample exercise ability analysis report based on the types of exercise completed independently; Analyze in sequence to obtain multiple sample sports ability analysis reports of multiple secondary similar users; The motor performance analyzer is used to analyze the multimodal rehabilitation data sequence and output a target motor performance analysis report, including: performing mean calculation on the plurality of sample multimodal rehabilitation data sequences to obtain a sample multimodal rehabilitation data mean sequence; performing deviation analysis on the multimodal rehabilitation data sequence and the sample multimodal rehabilitation data mean sequence to obtain a data deviation ratio; Calculate the ratio of the data deviation ratio to the historical maximum data deviation ratio, multiply it by K and round it up to obtain the number of plug-in selections P; P convergent motor ability analysis plug-ins are randomly selected from the K convergent motor ability analysis plug-ins of the motor ability analyzer, the multimodal rehabilitation data sequence is analyzed, P predicted motor ability analysis reports are output, and the target motor ability analysis report is obtained after mean calculation.

2. The method for analyzing motor ability by enhancing rehabilitation data according to claim 1, characterized in that: The multimodal rehabilitation data includes physiological data, functional data, imaging data and rehabilitation treatment records.

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