Orthopedic disease rehabilitation action auxiliary generation method and system based on traditional chinese medicine exercise and readable medium

Abstract

The application discloses a rehabilitation action assisting generation method and system for orthopedic diseases based on traditional Chinese medicine exercise and a readable medium, and belongs to the technical field of medical data processing. The method comprises the following steps: acquiring a diseased position and formulating a rehabilitation strategy, establishing a human kinematics coding reference model, coding rehabilitation actions and traditional Chinese medicine exercise actions, and generating corresponding action coding sets. The matching degrees of the rehabilitation action coding sets and the exercise action coding sets are statistically analyzed, and the traditional Chinese medicine exercise with a high matching degree is selected as a rehabilitation assisting exercise. The method realizes accurate matching of rehabilitation actions and traditional Chinese medicine exercise actions, promotes the rehabilitation of a diseased position, and improves treatment efficiency. Meanwhile, the method supports detailed optimization and personalized expansion of exercise actions, and can generate a teaching video to assist user learning and improve rehabilitation assisting effect.

Description

Technical Field

[0001] This application belongs to the field of medical data processing technology, and relates to a method, system and readable medium for generating rehabilitation movements for orthopedic diseases based on traditional Chinese medicine exercises. Background Technology

[0002] There are still many areas for improvement in using traditional Chinese medicine exercises for orthopedic rehabilitation. For example, different exercises correspond to different movements, requiring the coordinated use of different joints and muscles. Clearly, if the exercises are not appropriately matched to different orthopedic conditions, they will not only fail to aid rehabilitation but may also worsen the condition and cause further damage.

[0003] How to effectively utilize traditional Chinese medicine exercises to serve the rehabilitation treatment of orthopedic diseases remains an urgent problem to be solved. Summary of the Invention

[0004] To address the problem that traditional Chinese medicine exercises are difficult to apply precisely and efficiently in the rehabilitation treatment of orthopedic diseases in practical applications, this application aims to propose a method for generating rehabilitation movements for orthopedic diseases based on traditional Chinese medicine exercises. This method analyzes the areas of the patient's body requiring rehabilitation treatment, generates the necessary rehabilitation movements, and matches these movements with corresponding traditional Chinese medicine exercises, optimizing the detailed parameters of the exercises to achieve personalized and precise treatment assistance and improve the rehabilitation effect of orthopedic diseases. To implement the above-mentioned method for generating rehabilitation movements for orthopedic diseases, this application aims to provide a system for generating rehabilitation movements for orthopedic diseases based on traditional Chinese medicine exercises. A third objective is to protect a computer-readable storage medium, the specific details of which are as follows:

[0005] A method for generating rehabilitation movements for orthopedic diseases based on traditional Chinese medicine exercises, comprising:

[0006] Obtain and formulate the necessary rehabilitation strategy based on the location of the affected area;

[0007] Based on the rehabilitation strategy, at least one traditional Chinese medicine exercise is matched as an auxiliary rehabilitation exercise and output;

[0008] The rehabilitation strategy includes rehabilitation actions corresponding to each rehabilitation stage, as well as the sequence, range, speed and number of times of each rehabilitation action.

[0009] Based on the rehabilitation strategy, at least one traditional Chinese medicine exercise is matched, including:

[0010] Based on the human kinematics model, the joints and muscle tissues related to movement are encoded and stored as a coding reference model;

[0011] Each traditional Chinese medicine exercise is broken down into steps and movements. Each movement is numbered, and the associated joints and muscles are analyzed. Based on the coding reference model, the codes corresponding to the associated joints and muscles are combined in a set order to generate a sequence of exercise codes representing each movement. This sequence is then associated with and stored with the movement numbers. Finally, the sequence of movements in each traditional Chinese medicine exercise is combined to generate a set of exercise movement codes for each exercise.

[0012] Each rehabilitation action is assigned a number, and the associated joints and muscles are analyzed and obtained. Based on the coding reference model, the codes corresponding to the associated joints and muscles are combined in a set order to generate a rehabilitation code queue to represent each rehabilitation action, which is then stored in association with the rehabilitation action number. The rehabilitation code queues are combined according to the action sequence of the rehabilitation actions to generate a rehabilitation action code set corresponding to each rehabilitation action.

[0013] Statistical analysis was performed to determine the degree of matching between the rehabilitation movement code set and the movement code sets of various exercises. Traditional Chinese medicine exercises corresponding to the movement code sets with high matching degrees were selected as rehabilitation auxiliary exercises.

[0014] The set order is the sequence of movement of each associated joint and muscle tissue when performing related actions, or the order according to the importance of each joint and muscle tissue in the movement.

[0015] Furthermore, statistical analysis was conducted to determine the degree of matching between the rehabilitation movement coding set and the movement coding sets of each exercise, including:

[0016] Obtain the rehabilitation code queues contained in the rehabilitation movement code set, and the exercise code queues contained in the exercise movement code set;

[0017] When analyzing the degree of matching between the rehabilitation movement coding set and a set of exercise movement coding sets:

[0018] Count the number N1 of the exercise code queues that are the same as the rehabilitation code queue in the above-mentioned exercise movement code set;

[0019] Count the number of rehabilitation coding queues that have the same queue in the exercise movement coding set, and calculate the ratio Q1 between the number of the above rehabilitation coding queues and the total number of rehabilitation coding queues.

[0020] The rehabilitation action coding set contains multiple rehabilitation coding queues, which are decomposed to generate multiple second data groups formed by a single coding element. The exercise coding set contains multiple exercise coding queues, which are decomposed to generate multiple first data groups formed by a single coding element.

[0021] Compare each second data group corresponding to the rehabilitation movement coding set with the multiple first data groups corresponding to the set exercise movement coding set, and count the number N2 of the first data groups that are the same as each second data group in each exercise movement coding set.

[0022] Count the number of second data groups in which all elements are the same as those in one of the first data groups, and calculate the ratio Q2 of the number of the second data groups to the total number of second data groups.

[0023] Each of the quantities N1, Q1, N2, and Q2 is assigned a weight value, and a matching coefficient is generated based on the weighted fusion algorithm to represent the degree of matching between the rehabilitation action code set and the currently set exercise action code set.

[0024] The above technical solution performs similarity matching based on two dimensions: whether the movements are completely identical and whether the joints and muscle tissues associated with the movements are completely identical. Different calculation weights are set for different dimensions, so users can increase the first calculation weight value according to their actual needs, such as emphasizing complete consistency of movements, thereby making the matching results more accurate.

[0025] Furthermore, the statistical analysis of the matching degree between the rehabilitation movement coding set and the movement coding sets of each exercise also includes:

[0026] Each rehabilitation coding queue is split into multiple basic coding segments;

[0027] Using multiple basic coding segments as matching references, search for coding segments that are the same as the basic coding segments from the coding queue corresponding to the set of exercise action coding sets;

[0028] Count the number N3 of code segments in the set of exercise movement codes that are identical to the basic code segment;

[0029] The number of basic coding segments with the same coding segment in the set of exercise action coding is counted, and the ratio Q3 of the number of basic coding segments to the total number of basic coding segments is calculated.

[0030] Extract the key codes from each rehabilitation coding queue as array elements, and generate multiple core coding groups corresponding to each rehabilitation coding queue;

[0031] Using multiple core coding groups as matching references, search for a coding queue containing all the key codes contained in the core coding groups from the coding queue corresponding to the set coding set of exercises.

[0032] The number N4 of the exercise code queues that contain all the key codes in the set of exercise action codes is obtained statistically.

[0033] The number of core coding groups that share the same coding group in the set exercise action coding set is counted, and the ratio Q4 of the number of such core coding groups to the total number of core coding groups is calculated.

[0034] Each of the quantities N1, N2, N3, N4, quantity ratio Q1, quantity ratio Q2, quantity ratio Q3, and quantity ratio Q4 is assigned a calculation weight value, and a matching coefficient is generated according to the weighted fusion algorithm to represent the degree of matching between the rehabilitation action code set and the currently set exercise action code set.

[0035] Furthermore, the method for generating rehabilitation movements for orthopedic diseases also includes:

[0036] Establish and store the appropriate range of motion, speed of motion, and number of repetitions for each stage of rehabilitation.

[0037] Based on the established rehabilitation auxiliary exercises, identify the exercises that match each rehabilitation movement and mark them as target movements;

[0038] Determine the appropriate range of motion and speed for each target movement based on the current stage of rehabilitation, and generate movement optimization parameters;

[0039] The target movements are adjusted according to the aforementioned movement optimization parameters to generate optimized rehabilitation auxiliary exercises;

[0040] The optimization parameters include the amplitude of movement and the speed of movement.

[0041] The above technical solutions can make the exercises more suitable for the current user's needs and improve the rehabilitation effect.

[0042] Furthermore, the method for generating rehabilitation movements for orthopedic diseases also includes:

[0043] Obtain the movements and their numbers included in the rehabilitation assistive exercises;

[0044] Based on the TCM concepts corresponding to the rehabilitation auxiliary exercises, each exercise movement is configured with at least one extended movement and numbered, and then stored in association with the corresponding exercise movement number;

[0045] Analyze and, based on the joints and muscle tissues associated with the above extended movements, combine the codes corresponding to the associated joints and muscle tissues in a set order according to the coding reference model to generate an extended coding queue to represent the extended movements, and store it in association with the exercise movement number and the extended movement number.

[0046] Obtain the rehabilitation code queue, second data group, basic code segment and core code group corresponding to each rehabilitation action, and calculate the matching coefficient between each rehabilitation action and each of the extended actions according to the aforementioned weighted fusion algorithm;

[0047] If the matching coefficient exceeds the set threshold, the extended action is selected and added before or after its associated exercise action to generate an optimized rehabilitation auxiliary exercise.

[0048] The above technical solutions can precisely improve and expand existing rehabilitation exercises according to rehabilitation needs, making the generated rehabilitation auxiliary exercises more suitable for the needs and improving the effectiveness of rehabilitation assistance without violating the principles of traditional Chinese medicine exercises.

[0049] Furthermore, at least one traditional Chinese medicine exercise is matched as a rehabilitation aid and output, including:

[0050] Based on the generated optimized rehabilitation assistive exercises, according to the exercises and extended movements they contain, combined with standard exercise videos, the optimized rehabilitation assistive exercise videos are generated and output using video generation tools;

[0051] Specifically, the movements of each exercise are judged, and if the range and speed of the movements have been optimized, then a voice prompt message is played when the exercise movements are played.

[0052] The above technical solution allows for the precise generation of optimized rehabilitation auxiliary exercise instruction videos using AI video generation tools, making it easier for users to conduct rehabilitation training based on these videos.

[0053] Furthermore, the method for generating rehabilitation movements for orthopedic diseases also includes:

[0054] The motion optimization parameters are obtained, and corresponding rehabilitation equipment is selected or rehabilitation equipment parameters are set based on the motion optimization parameters and the joints and muscle tissues associated with the target motion, in order to limit the user's motion range and / or motion speed.

[0055] The above technical solutions can be used to configure appropriate rehabilitation equipment for users based on the optimized rehabilitation auxiliary exercises, thereby improving the rehabilitation effect and avoiding accidental damage to the affected area.

[0056] To implement the aforementioned method for generating rehabilitation movements for orthopedic diseases, this application also discloses a system for generating rehabilitation movements for orthopedic diseases based on traditional Chinese medicine exercises, comprising:

[0057] The data acquisition module is used to acquire information about the location of the affected area and formulate the required rehabilitation strategies.

[0058] The exercise matching module is used to match at least one traditional Chinese medicine exercise as an auxiliary rehabilitation exercise according to the rehabilitation strategy and output it.

[0059] The exercise matching module includes:

[0060] The encoding unit is configured to encode and store motion-related joints and muscle tissues based on the human kinematics model as an encoding reference model;

[0061] The exercise movement analysis unit is configured to break down each traditional Chinese medicine exercise into steps and movements, number each exercise movement and analyze its associated joints and muscle tissues, generate an exercise code queue to represent each exercise movement according to the coding reference model, and combine the above exercise code queues to generate an exercise movement code set corresponding to the traditional Chinese medicine exercise.

[0062] The rehabilitation movement parsing unit is used to number each rehabilitation movement and analyze and obtain the associated joints and muscle tissues. According to the coding reference model, the codes corresponding to the associated joints and muscle tissues are combined in a set order to generate a rehabilitation code queue to represent each rehabilitation movement and store it in association with the rehabilitation movement number. According to the movement order of the rehabilitation movement, the rehabilitation code queues are combined to generate the rehabilitation movement code set corresponding to the rehabilitation movement.

[0063] The matching analysis unit is used to statistically analyze the degree of matching between the rehabilitation movement code set and the movement code sets of various exercises, and select the traditional Chinese medicine exercises corresponding to the movement code sets with a high degree of matching as rehabilitation auxiliary exercises.

[0064] The matching analysis unit includes:

[0065] The queue temporary storage subunit is configured to acquire and store each rehabilitation code queue contained in the rehabilitation action code set, and each exercise code queue contained in each exercise action code set.

[0066] The pre-set exercise subunit is configured to temporarily store a set of pre-set exercise action codes for matching.

[0067] The first statistical subunit is configured to count the number N1 of the exercise code queues that are the same as the rehabilitation code queues in the above-mentioned set exercise action code set, count the number of rehabilitation code queues that have the same queues in the exercise action code set, and calculate the ratio Q1 of the above-mentioned number of rehabilitation code queues to the total number of rehabilitation code queues.

[0068] The data group generation subunit is configured to decompose multiple rehabilitation code queues contained in the rehabilitation action code set to generate multiple second data groups formed by a single code element, and to decompose multiple exercise code queues contained in the above-mentioned exercise action code set to generate multiple first data groups formed by a single code element.

[0069] The second statistical subunit is configured to compare each second data group corresponding to the rehabilitation action coding set with multiple first data groups corresponding to the set exercise action coding set, count the number N2 of first data groups that are the same as each second data group in each exercise action coding set, count the number of second data groups whose elements are the same as all the elements in one of the first data groups, and calculate the ratio Q2 of the number of the above-mentioned second data groups to the total number of second data groups.

[0070] The basic coding segment generation subunit is configured to split each rehabilitation coding queue into multiple basic coding segments;

[0071] The third statistical subunit is configured to use multiple basic coding segments as matching references to search for coding segments that are the same as the basic coding segments in the exercise coding queue corresponding to the set exercise action coding set, count the number N3 of coding segments that are the same as the basic coding segments in the set exercise action coding set, count the number of basic coding segments that have the same coding segments in the set exercise action coding set, and calculate the ratio Q3 of the number of basic coding segments to the total number of basic coding segments.

[0072] The core coding group generation sub-unit is configured to extract key codes from each rehabilitation coding queue as array elements, and generate multiple core coding groups corresponding to each rehabilitation coding queue.

[0073] The fourth statistical subunit is configured to use multiple core coding groups as matching references to search for a queue of exercise codes that contains all the key codes contained in the core coding group from the exercise code queue corresponding to the set exercise action coding set, count the number N4 of exercise code queues that contain all the key codes in the set exercise action coding set, count the number of core coding groups that have the same coding group in the set exercise action coding set, and calculate the ratio Q4 of the number of core coding groups to the total number of core coding groups.

[0074] The matching coefficient calculation subunit assigns calculation weight values ​​to the quantities N1, N2, N3, N4, quantity ratio Q1, quantity ratio Q2, quantity ratio Q3, and quantity ratio Q4 respectively, and calculates and generates a matching coefficient to characterize the degree of matching between the rehabilitation action code set and the currently set exercise action code set according to the weighted fusion algorithm.

[0075] The rehabilitation strategy includes rehabilitation actions corresponding to each rehabilitation stage, as well as the sequence, range, speed and number of times of each rehabilitation action.

[0076] The set order is the sequence of movement of each associated joint and muscle tissue when performing related actions, or the order according to the importance of each joint and muscle tissue in the movement.

[0077] Furthermore, the matching analysis unit also includes:

[0078] The motion parameter storage subunit is configured to set and store the appropriate motion amplitude, speed and number of repetitions for each rehabilitation stage.

[0079] The target movement recognition subunit is configured to identify the movement that matches each rehabilitation movement based on the established rehabilitation assistive exercises, and mark it as the target movement.

[0080] The parameter generation subunit is configured to determine the appropriate range of motion and speed of motion for each target movement based on the current rehabilitation stage, and generate motion optimization parameters.

[0081] The movement adjustment subunit is configured to adjust each target movement according to the movement optimization parameters to generate a first rehabilitation auxiliary exercise.

[0082] The action extension configuration subunit is configured to obtain the exercises and their numbers included in the rehabilitation assistive exercises, and to configure at least one extended action and number it for each exercise based on the concept of traditional Chinese medicine.

[0083] An extended coding generation subunit is configured to analyze and combine the codes corresponding to the associated joints and muscle tissues according to a set order based on the coding reference model, based on the aforementioned extended action, to generate an extended coding queue to characterize the extended action.

[0084] The matching degree calculation subunit is configured to obtain the rehabilitation code queue, second data group, basic code segment and core code group corresponding to each rehabilitation action, and calculate the matching coefficient between each rehabilitation action and each of the extended actions according to the aforementioned weighted fusion algorithm;

[0085] The extended action merging subunit is configured to add extended actions with matching coefficients exceeding a set threshold before or after the relevant exercises in the first rehabilitation assistive exercise, thereby generating an optimized rehabilitation assistive exercise.

[0086] A computer-readable storage medium having a program module loaded thereon, which, when executed by a processor, is used to implement the previously described method for generating rehabilitation movements for orthopedic diseases based on traditional Chinese medicine exercises.

[0087] The above technical solutions will help promote the use of the orthopedic disease rehabilitation movement generation method described in this application.

[0088] This application includes at least one of the following beneficial effects:

[0089] (1) By converting rehabilitation movements and traditional Chinese medicine exercises into specific codes, the corresponding exercises can be accurately matched according to the needs of rehabilitation movements, and the traditional Chinese medicine exercises can be used to promote the rehabilitation of the affected area and improve the rehabilitation efficiency.

[0090] (2) After matching the above rehabilitation movements with corresponding traditional Chinese medicine exercises, optimize the detailed parameters of the exercises according to actual needs, expand the exercises, and achieve personalized and precise treatment assistance.

[0091] (3) By outputting the optimized rehabilitation assistive exercises as teaching videos, users can watch them and improve the rehabilitation assistive effect. Attached Figure Description

[0092] Figure 1 A diagram illustrating the steps and methods of traditional Chinese medicine exercises;

[0093] Figure 2 A diagram illustrating the methods for expanding the exercises;

[0094] Figure 3 This is a schematic diagram of the functional module structure of the system in this application.

[0095] Figure reference numerals: 1. Encoding unit; 2. Exercise movement analysis unit; 3. Rehabilitation movement analysis unit; 4. Matching analysis unit; 41. Queue temporary storage subunit; 42. Exercise preset subunit; 43. First statistical subunit; 44. Data group generation subunit; 45. Second statistical subunit; 46. Basic coding segment generation subunit; 47. Third statistical subunit; 48. Core coding group generation subunit; 49. Fourth statistical subunit; 410. Matching coefficient calculation subunit. Detailed Implementation

[0096] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0097] In the description of this specification, the references to "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples" refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0098] Before proceeding with a detailed explanation of the solution, it should be noted that the orthopedic diseases described in the embodiments of this application include, but are not limited to, frozen shoulder, cervical spondylosis, lumbar muscle strain, knee osteoarthritis, etc. For the sake of clarity in explaining the solution of this application, frozen shoulder is used as the orthopedic disease to be rehabilitated in the following embodiments.

[0099] The traditional Chinese medicine exercises described in this application include at least: Tai Chi, Baduanjin, Shaolin internal exercises, Yijinjing, and Wuqinxi.

[0100] The detailed method for generating rehabilitation movements for orthopedic diseases based on traditional Chinese medicine exercises mainly includes the following steps:

[0101] S100: Obtain and formulate the required rehabilitation strategy based on the location of the affected area;

[0102] S200, based on the rehabilitation strategy, match at least one traditional Chinese medicine exercise as an auxiliary rehabilitation exercise and output it.

[0103] In step S100 above, obtaining the location of the affected area includes palpation, medical history taking, MRI examination, and determination by analyzing the user's motion video.

[0104] In step S100, the rehabilitation strategy includes rehabilitation movements corresponding to each rehabilitation stage, as well as the sequence, amplitude, speed, and number of movements for each movement. In this embodiment, the rehabilitation strategy is set by the physician based on the patient.

[0105] Based on the rehabilitation strategy, at least one traditional Chinese medicine exercise is matched, such as Figure 1 As shown, it includes:

[0106] S210, based on the human kinematic model, encode each joint and muscle tissue related to movement and store it as a coding reference model. Preferably, the above coding consists of numbers, characters, or a combination of both, for example, using the code "JZ02" to represent the hunchback joint and "JZ04" to represent the supraspinatus muscle.

[0107] S2201 breaks down the steps and movements of various traditional Chinese medicine exercises, assigns numbers to each movement, and analyzes the associated joints and muscles. For example, Tai Chi Chuan is divided into 24 movements from the starting posture to the ending posture. The starting posture is further divided into three movements: standing with feet apart, raising both arms forward, and bending the knees and pressing the palms down. In the steps of this application, the above-mentioned movements are numbered. For example, standing with feet apart is numbered "TJ0101". According to the human kinematic model analysis, the movement of standing with feet apart is a complex movement that is dominated by hip abduction, cushioned by the knee joint, stabilized by the ankle joint, powered by the lateral gluteal muscles, coordinated by the anteroposterior thigh muscles, and maintained by the core muscle group. The joints and muscles involved include: hip joint, knee joint, ankle joint, lateral gluteal muscles, and anteroposterior thigh muscles.

[0108] S2202, according to the coding reference model, the codes corresponding to the associated joints and muscle tissues are combined in a set order to generate a skill code queue to represent each step of the skill movement, and then stored in association with the skill movement number. In this embodiment of the application, the above-mentioned set order is preferably the order in which the associated joints and muscle tissues move when performing the relevant movements, or sorted according to the importance of each joint and muscle tissue in the movement. For example, if the codes corresponding to the hip joint, knee joint, ankle joint, lateral gluteal muscles, and anteroposterior thigh muscles are a1, b1, c1, d1, and e1 respectively, then the skill code queue corresponding to the Tai Chi Chuan starting posture with feet apart (skill movement number: TJ0101) is a1d1b1c1e1.

[0109] S2203, combine the code queues of each exercise according to the movement sequence of traditional Chinese medicine exercises to generate the corresponding exercise movement code set. Based on the above analysis, convert the corresponding exercises in Tai Chi Chuan into exercise code queues, and then combine them according to the sequence of movements to form the exercise movement code set.

[0110] S2301, assign numbers to each of the described rehabilitation actions and analyze and obtain the associated joints and muscle tissues.

[0111] S2302, according to the coding reference model, the codes corresponding to the associated joints and muscle tissues are combined in a set order to generate a rehabilitation code queue to represent each rehabilitation action and store it in association with the rehabilitation action number.

[0112] S2303, combine the rehabilitation code queues according to the action sequence of the rehabilitation actions to generate a rehabilitation action code set corresponding to the rehabilitation actions.

[0113] For the generation methods of rehabilitation coding queues and rehabilitation movement coding sets, please refer to the above-mentioned generation methods of exercise coding queues and exercise movement coding sets, which will not be repeated here.

[0114] S240, Statistically analyze the matching degree between the rehabilitation movement code set and the movement code sets of each exercise, and select the traditional Chinese medicine exercises corresponding to the movement code sets with a high matching degree as rehabilitation auxiliary exercises.

[0115] In this application's implementation, the statistical analysis of the matching degree between the rehabilitation movement coding set and the various exercise movement coding sets includes:

[0116] Preparation steps: Obtain the rehabilitation code queues contained in the rehabilitation movement code set, and the exercise code queues contained in the exercise movement code set.

[0117] When analyzing the degree of matching between the rehabilitation movement coding set and a set of exercise movement coding sets:

[0118] S2401, count the number N1 of the exercise code queues that are the same as the rehabilitation code queue in the above-mentioned set of exercise movement codes. For example, if the rehabilitation movement code set contains the queue "a1d1b1c1e1", and the exercise setting exercise movement code set contains 4 of the above queues, then N1=4. In actual application, the exercise movements corresponding to a certain traditional Chinese medicine exercise may be repeated.

[0119] S2402, count the number of rehabilitation code queues that have the same queue in the exercise movement code set, and calculate the ratio Q1 of the number of the above rehabilitation code queues to the total number of rehabilitation code queues.

[0120] The purpose of the above steps S2401-2402 is to determine whether the exercise movement code set contains movements that are completely consistent with the rehabilitation movements. For example, if the required rehabilitation movement is knee bending, and there are 12 knee bending movements in Tai Chi, then N1=12; if the rehabilitation movement is 10 movements such as knee bending and elbow bending, that is, the total number of rehabilitation code queues is 10, and the exercise movement code set contains 6 movement types, then the above quantity ratio Q1=0.6.

[0121] S2403, decompose the multiple rehabilitation code queues contained in the rehabilitation action code set to generate multiple second data groups formed by a single code element, and decompose the multiple exercise code queues contained in the above-mentioned set exercise action code set to generate multiple first data groups formed by a single code element.

[0122] S2404, compare each second data group corresponding to the rehabilitation movement code set with the multiple first data groups corresponding to the set exercise movement code set, and count the number N2 of first data groups that are the same as each second data group in each exercise movement code set.

[0123] S2405, count the number of second data groups in which all elements in the second data group are the same as those in one of the first data groups, and calculate the ratio Q2 of the number of the second data groups to the total number of the second data groups.

[0124] The purpose of steps S2403-2405 above is to determine whether the exercise movement code set contains movements similar to each rehabilitation movement. That is, it is only necessary to determine whether the same joint or muscle tissue is moving. For example, the two movements "straightening and bending" and "bending and straightening" are considered equivalent when the current step is performed.

[0125] S2406, assign calculation weight values ​​to the quantities N1, Q1, N2, and Q2 respectively, and calculate a matching coefficient to characterize the degree of matching between the rehabilitation action coding set and the currently set exercise action coding set according to the weighted fusion algorithm. For example, when matching traditional Chinese medicine exercises, the execution order of a certain action is not important, that is, as long as the relevant joints and muscle tissues move during exercise, the quantity N2 and the quantity ratio Q2 can be assigned higher calculation weight values. In order to reduce the influence of the dimensions on the calculation results, the above values ​​can be standardized first, for example, the quantity N1 / N2 can be linearly mapped to a fixed numerical range [0,1].

[0126] The above technical solution performs similarity matching based on two dimensions: whether the movements are completely identical and whether the joints and muscle tissues associated with the movements are completely identical. Different calculation weights are set for different dimensions, so users can increase the first calculation weight value according to their actual needs, such as emphasizing complete consistency of movements, thereby making the matching results more accurate.

[0127] In an optimized embodiment of this application, the statistical analysis of the matching degree between the rehabilitation movement coding set and the movement coding sets of each exercise further includes:

[0128] S2411, split each rehabilitation coding queue into multiple basic coding segments. For example, split the rehabilitation coding queue "a1d1b1c1e1" into two basic coding segments "a1d1" and "b1c1e1".

[0129] S2412, using multiple basic coding segments as matching references, search for a coding segment that is the same as the basic coding segment from the coding queue corresponding to the set of exercise action coding sets.

[0130] S2413, Count the number N3 of the code segments that are the same as the basic code segment in the set of exercise action codes.

[0131] S2414, count the number of basic coding segments with the same coding segment in the set of exercise action coding set, and calculate the ratio Q3 of the number of basic coding segments to the total number of basic coding segments.

[0132] The above steps S2411-S2413 are essentially string matching and statistical analysis, which can be implemented using a traversal search algorithm.

[0133] S2421, extract the key codes from each rehabilitation coding queue as array elements, and generate multiple core coding groups corresponding to each rehabilitation coding queue. For example, extract the core coding group [a1,d1] from the rehabilitation coding queue "a1d1b1c1e1".

[0134] S2422, using multiple core coding groups as matching references, search for a coding queue containing all the key codes contained in the core coding groups from the coding queue corresponding to the set coding set of the exercise movements.

[0135] S2423, Count the number N4 of the exercise code queues that contain all the key codes in the set exercise action code set.

[0136] S2424, count the number of core coding groups that have the same coding group in the set exercise action coding set, and calculate the ratio Q4 of the number of the core coding groups to the total number of core coding groups.

[0137] S2425, assign calculation weight values ​​to the quantities N1, N2, N3, N4, quantity ratio Q1, quantity ratio Q2, quantity ratio Q3, and quantity ratio Q4 respectively, and calculate and generate a matching coefficient to characterize the degree of matching between the rehabilitation action code set and the currently set exercise action code set according to the weighted fusion algorithm.

[0138] To better adapt the exercises to the needs of current users and improve rehabilitation outcomes, the optimized method for generating rehabilitation exercises for orthopedic diseases further includes:

[0139] S2431, Establish and store the appropriate range of motion, speed of motion, and number of repetitions for rehabilitation movements at each stage of rehabilitation.

[0140] S2432, Based on the established rehabilitation auxiliary exercises, identify the exercises that match each rehabilitation movement and mark them as target movements.

[0141] S2433, determine the appropriate range of motion and speed for each target movement based on the current rehabilitation stage, and generate movement optimization parameters. The optimization parameters include range of motion and speed.

[0142] S2434, Adjust each target movement according to the movement optimization parameters to generate an optimized rehabilitation auxiliary exercise.

[0143] In practical applications, the range and speed of the above movements are set by the physician according to the patient's recovery status. For example, the speed of the movements may be slowed down by 0.8 times based on the standard exercise movements.

[0144] In practice, a set of traditional Chinese medicine exercises may not fully cover all the necessary rehabilitation movements. Therefore, in the embodiments of this application, such as... Figure 2 As shown, the method for generating rehabilitation movements for orthopedic diseases also includes:

[0145] S2441, Obtain the movements and their numbers included in the rehabilitation assistive exercises;

[0146] S2442, Based on the TCM concept corresponding to the rehabilitation auxiliary exercise, configure at least one extended movement for each exercise movement and number it, and store it in association with the corresponding exercise movement number;

[0147] S2443, Analyze and based on the joints and muscle tissues associated with the above extended movements, combine the codes corresponding to the associated joints and muscle tissues in a set order according to the coding reference model, generate an extended coding queue to represent the extended movements, and store it in association with the exercise movement number and the extended movement number.

[0148] S2444, obtain the rehabilitation code queue, second data group, basic code segment and core code group corresponding to each rehabilitation action, and calculate the matching coefficient between each rehabilitation action and each of the extended actions according to the aforementioned weighted fusion algorithm;

[0149] S2445, if the matching coefficient exceeds the set threshold, the extended action is selected and added before or after its associated exercise action to generate an optimized rehabilitation auxiliary exercise; if the matching coefficient does not exceed the set threshold, no extended action is added.

[0150] The aforementioned technical solutions can precisely refine and expand existing rehabilitation exercises according to rehabilitation needs, making the generated rehabilitation aids more suitable for the needs and improving the effectiveness of rehabilitation assistance without violating the principles of Traditional Chinese Medicine (TCM). For example, TCM believes that the foundation of human health lies in the balance of Yin and Yang, and Tai Chi movements are full of the dialectical relationship between Yin and Yang: when shifting the center of gravity, the supporting leg is "solid" (Yang), and the other leg is "empty" (Yin), with a clear distinction between solid and empty and constantly transforming. Based on the above TCM principles, when expanding Tai Chi movements, it is necessary to pay attention to the alternating support of both legs and avoid movements where both feet leave the ground simultaneously (jumping). Since the TCM principles that various traditional Chinese medicine exercises align with differ, it is necessary to consult the corresponding TCM principles of the rehabilitation aids before formulating the appropriate movements when expanding the movements, which will not be elaborated here.

[0151] To facilitate patients' understanding of the movement details of rehabilitation exercises, this application's implementation includes matching at least one traditional Chinese medicine exercise as a rehabilitation aid and outputting it, including:

[0152] Based on the generated optimized rehabilitation assistive exercises, and according to the included exercises and extended movements, combined with standard exercise videos, an optimized rehabilitation assistive exercise video is generated and output using a video generation tool.

[0153] The aforementioned standard exercise videos can be obtained from professional teaching video libraries produced by relevant traditional Chinese medicine universities, and the video generation tools can be implemented using relevant open-source AI models. During video generation, the movements of each exercise are evaluated. If the range of motion and speed of the movements have been optimized, a voice prompt, such as "Please gently raise your arm," will be played during the playback of the exercise.

[0154] To further improve rehabilitation outcomes and avoid accidental injury to the affected area, the orthopedic disease rehabilitation movement generation method described in this application further includes:

[0155] The aforementioned motion optimization parameters are obtained. Based on these parameters and the joints and muscles associated with the target motion, corresponding rehabilitation equipment is selected or its parameters are set to limit the user's range of motion and / or speed. For example, a shoulder abduction brace is configured to fix the arm at a specific abduction angle (usually 15°-45°), strictly limiting the adduction, internal rotation, and extension of the shoulder joint.

[0156] To implement the above-mentioned method for generating rehabilitation movements for orthopedic diseases, this application also discloses a system for generating rehabilitation movements for orthopedic diseases based on traditional Chinese medicine exercises, which mainly includes a data acquisition module and an exercise matching module.

[0157] The data acquisition module is used to obtain the location information of the affected area and formulate the required rehabilitation strategy. The location information of the affected area and the required rehabilitation strategy are both input by the physician according to the actual situation.

[0158] The exercise matching module is used to match at least one traditional Chinese medicine exercise as an auxiliary rehabilitation exercise according to the rehabilitation strategy and output it.

[0159] Combination Figure 3 As shown, the exercise matching module includes: encoding unit 1, exercise movement analysis unit 2, rehabilitation movement analysis unit 3, and matching analysis unit 4.

[0160] Encoding unit 1 is configured to encode and store movement-related joints and muscles based on a human kinematics model as an encoding reference model. Exercise movement analysis unit 2 is configured to break down each traditional Chinese medicine exercise into steps and movements, number each step, analyze its associated joints and muscles, generate an exercise code queue representing each step based on the encoding reference model, and combine these queues to generate an exercise movement code set corresponding to each traditional Chinese medicine exercise. Rehabilitation movement analysis unit 3 is used to number each rehabilitation movement and analyze its associated joints and muscles, generate a rehabilitation code queue representing each rehabilitation movement based on the encoding reference model, and combine these queues to generate a rehabilitation movement code set corresponding to each rehabilitation movement. Matching analysis unit 4 is used to statistically analyze the matching degree between the rehabilitation movement code set and each exercise movement code set, selecting the traditional Chinese medicine exercise corresponding to the exercise movement code set with a high matching degree as the rehabilitation auxiliary exercise.

[0161] The matching analysis unit 4 includes: a queue temporary storage subunit 41, a method preset subunit 42, a first statistical subunit 43, a data group generation subunit 44, a second statistical subunit 45, a basic coding segment generation subunit 46, a third statistical subunit 47, a core coding group generation subunit 48, a fourth statistical subunit 49, and a matching coefficient calculation subunit 410.

[0162] The queue temporary storage subunit 41 is configured to acquire and store each rehabilitation code queue contained in the rehabilitation action code set, and each exercise code queue contained in each exercise action code set. The exercise preset subunit 42 is configured to temporarily store a set of preset exercise action codes for matching. The first statistics subunit 43 is configured to count the number N1 of exercise code queues that are the same as rehabilitation code queues in the preset exercise action code set, count the number of rehabilitation code queues that have the same queues in the exercise action code set, and calculate the ratio Q1 of the number of rehabilitation code queues to the total number of rehabilitation code queues. The data group generation subunit 44 is configured to decompose the multiple rehabilitation code queues contained in the rehabilitation action code set to generate multiple second data groups formed by a single code element, and to decompose the multiple exercise code queues contained in the preset exercise action code set to generate multiple first data groups formed by a single code element. The second statistical subunit 45 is configured to compare each second data group corresponding to the rehabilitation action coding set with multiple first data groups corresponding to the set exercise action coding set, count the number N2 of first data groups that are the same as each second data group in each exercise action coding set, count the number of second data groups whose elements are the same as those in one of the first data groups, and calculate the ratio Q2 of the number of the above-mentioned second data groups to the total number of second data groups.

[0163] The basic coding segment generation subunit 46 is configured to split each rehabilitation coding queue into multiple basic coding segments. The third statistical subunit 47 is configured to use the multiple basic coding segments as matching references to search for coding segments identical to the basic coding segments in the exercise coding queue corresponding to the set exercise movement coding set, count the number N3 of coding segments identical to the basic coding segments in the set exercise movement coding set, count the number of basic coding segments with identical coding segments in the set exercise movement coding set, and calculate the ratio Q3 between the number of basic coding segments and the total number of basic coding segments. The core coding group generation subunit 48 is configured to extract key codes from each rehabilitation coding queue as array elements, generating multiple core coding groups corresponding to each rehabilitation coding queue. The fourth statistical subunit 49 is configured to use multiple core coding groups as matching references to search for a queue of exercise codes containing all the key codes contained in the core coding groups from the exercise code queues corresponding to the set exercise action coding set, count the number N4 of exercise code queues containing all the key codes in the set exercise action coding set, count the number of core coding groups with the same coding group in the set exercise action coding set, and calculate the ratio Q4 of the number of core coding groups to the total number of core coding groups. The matching coefficient calculation subunit 410 assigns calculation weight values ​​to the quantities N1, N2, N3, N4, and the ratios Q1, Q2, Q3, and Q4, respectively, and calculates a matching coefficient to represent the degree of matching between the rehabilitation exercise coding set and the current set exercise action coding set according to a weighted fusion algorithm.

[0164] The matching analysis unit 4 further includes: an action parameter storage subunit, a target action recognition subunit, an optimization parameter generation subunit, an action adjustment subunit, an action extension configuration subunit, an extension code generation subunit, a matching degree calculation subunit, and an extension action merging subunit.

[0165] The movement parameter storage subunit is configured to set and store the appropriate movement amplitude, speed, and repetition parameters for each rehabilitation stage. The target movement identification subunit is configured to identify the appropriate movement from the established rehabilitation auxiliary exercises and mark it as the target movement. The optimization parameter generation subunit is configured to determine the appropriate movement amplitude and speed for each target movement based on the current rehabilitation stage and generate movement optimization parameters. The movement adjustment subunit is configured to adjust each target movement according to the movement optimization parameters to generate a first rehabilitation auxiliary exercise. The movement extension configuration subunit is configured to obtain the exercises and their numbers included in the rehabilitation auxiliary exercises, and configure at least one extension movement for each exercise based on traditional Chinese medicine principles and assign it a number. The extension code generation subunit is configured to analyze and, based on the joints and muscles associated with the extension movements, combine the codes corresponding to the associated joints and muscles in a set order according to the coding reference model to generate an extension code queue representing the extension movements. The matching degree calculation subunit is configured to acquire the rehabilitation code queue, second data group, basic code segment, and core code group corresponding to each rehabilitation movement, and calculate the matching coefficient between each rehabilitation movement and each of the aforementioned extended movements according to the weighted fusion algorithm. The extended movement merging subunit is configured to add extended movements with matching coefficients exceeding a set threshold before or after the relevant movements in the first rehabilitation auxiliary exercise, thereby generating an optimized rehabilitation auxiliary exercise.

[0166] To facilitate the widespread use of the orthopedic rehabilitation movement generation method described in this application, this application also proposes a computer-readable storage medium loaded with a program module. When the program module is executed by a processor, it is used to implement the orthopedic rehabilitation movement generation method based on traditional Chinese medicine exercises as described above. The computer-readable storage medium includes, but is not limited to, disk storage, CD-ROM, optical storage, etc.

[0167] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for generating rehabilitation movements for orthopedic diseases based on traditional Chinese medicine exercises, characterized in that, include: Obtain and formulate the necessary rehabilitation strategy based on the location of the affected area; Based on the rehabilitation strategy, at least one traditional Chinese medicine exercise is matched as an auxiliary rehabilitation exercise and output; The rehabilitation strategy includes rehabilitation actions corresponding to each rehabilitation stage, as well as the sequence, range, speed and number of times of each rehabilitation action. Based on the rehabilitation strategy, at least one traditional Chinese medicine exercise is matched, including: Based on the human kinematics model, the joints and muscle tissues related to movement are encoded and stored as a coding reference model; Each traditional Chinese medicine exercise is broken down into steps and movements. Each movement is numbered, and the associated joints and muscles are analyzed. Based on the coding reference model, the codes corresponding to the associated joints and muscles are combined in a set order to generate a sequence of exercise codes representing each movement. This sequence is then associated with and stored with the movement numbers. Finally, the sequence of movements in each traditional Chinese medicine exercise is combined to generate a set of exercise movement codes for each exercise. Each rehabilitation action is assigned a number, and the associated joints and muscles are analyzed and obtained. Based on the coding reference model, the codes corresponding to the associated joints and muscles are combined in a set order to generate a rehabilitation code queue to represent each rehabilitation action, which is then stored in association with the rehabilitation action number. The rehabilitation code queues are combined according to the action sequence of the rehabilitation actions to generate a rehabilitation action code set corresponding to each rehabilitation action. Statistical analysis was performed to determine the degree of matching between the rehabilitation movement code set and the movement code sets of various exercises. Traditional Chinese medicine exercises corresponding to the movement code sets with high matching degrees were selected as rehabilitation auxiliary exercises. The set order is the sequence of movement of each associated joint and muscle tissue when performing related actions, or the order according to the importance of each joint and muscle tissue in the movement.

2. The method for generating rehabilitation movements for orthopedic diseases based on traditional Chinese medicine exercises according to claim 1, characterized in that, Statistical analysis was performed to determine the degree of matching between the rehabilitation movement coding set and the movement coding sets of various exercises, including: Obtain the rehabilitation code queues contained in the rehabilitation movement code set, and the exercise code queues contained in the exercise movement code set; When analyzing the degree of matching between the rehabilitation movement coding set and a set of exercise movement coding sets: Count the number N1 of the exercise code queues that are the same as the rehabilitation code queue in the above-mentioned exercise movement code set; Count the number of rehabilitation coding queues that have the same queue in the exercise movement coding set, and calculate the ratio Q1 between the number of the above rehabilitation coding queues and the total number of rehabilitation coding queues. The rehabilitation action coding set contains multiple rehabilitation coding queues, which are decomposed to generate multiple second data groups formed by a single coding element. The exercise coding set contains multiple exercise coding queues, which are decomposed to generate multiple first data groups formed by a single coding element. Compare each second data group corresponding to the rehabilitation movement coding set with the multiple first data groups corresponding to the set exercise movement coding set, and count the number N2 of the first data groups that are the same as each second data group in each exercise movement coding set. Count the number of second data groups in which all elements are the same as those in one of the first data groups, and calculate the ratio Q2 of the number of the second data groups to the total number of second data groups. Each of the quantities N1, Q1, N2, and Q2 is assigned a weight value, and a matching coefficient is generated based on the weighted fusion algorithm to represent the degree of matching between the rehabilitation action code set and the currently set exercise action code set.

3. The method for generating rehabilitation movements for orthopedic diseases based on traditional Chinese medicine exercises according to claim 2, characterized in that, The statistical analysis of the matching degree between the rehabilitation movement coding set and the movement coding sets of each exercise also includes: Each rehabilitation coding queue is split into multiple basic coding segments; Using multiple basic coding segments as matching references, search for coding segments that are the same as the basic coding segments from the coding queue corresponding to the set of exercise action coding sets; Count the number N3 of code segments in the set of exercise movement codes that are identical to the basic code segment; The number of basic coding segments with the same coding segment in the set of exercise action coding is counted, and the ratio Q3 of the number of basic coding segments to the total number of basic coding segments is calculated. Extract the key codes from each rehabilitation coding queue as array elements, and generate multiple core coding groups corresponding to each rehabilitation coding queue; Using multiple core coding groups as matching references, search for a coding queue containing all the key codes contained in the core coding groups from the coding queue corresponding to the set coding set of exercises. The number N4 of the exercise code queues that contain all the key codes in the set of exercise action codes is obtained statistically. The number of core coding groups that share the same coding group in the set exercise action coding set is counted, and the ratio Q4 of the number of the core coding groups to the total number of core coding groups is calculated. Each of the quantities N1, N2, N3, N4, quantity ratio Q1, quantity ratio Q2, quantity ratio Q3, and quantity ratio Q4 is assigned a calculation weight value, and a matching coefficient is generated according to the weighted fusion algorithm to represent the degree of matching between the rehabilitation action code set and the currently set exercise action code set.

4. The method for generating rehabilitation movements for orthopedic diseases based on traditional Chinese medicine exercises according to claim 2 or 3, characterized in that, The method for generating rehabilitation movements for orthopedic diseases also includes: Set and store the appropriate range of motion, speed of motion, and number of repetitions for each stage of rehabilitation. Based on the established rehabilitation auxiliary exercises, identify the exercises that match each rehabilitation movement and mark them as target movements; Determine the appropriate range of motion and speed for each target movement based on the current stage of rehabilitation, and generate movement optimization parameters; The target movements are adjusted according to the aforementioned movement optimization parameters to generate optimized rehabilitation auxiliary exercises; The optimization parameters include the amplitude of movement and the speed of movement.

5. The method for generating rehabilitation movements for orthopedic diseases based on traditional Chinese medicine exercises according to claim 4, characterized in that, The method for generating rehabilitation movements for orthopedic diseases also includes: Obtain the movements and their numbers included in the rehabilitation assistive exercises; Based on the TCM concepts corresponding to the rehabilitation auxiliary exercises, each exercise movement is configured with at least one extended movement and numbered, and then stored in association with the corresponding exercise movement number; Analyze and, based on the joints and muscle tissues associated with the above extended movements, combine the codes corresponding to the associated joints and muscle tissues in a set order according to the coding reference model to generate an extended coding queue to represent the extended movements, and store it in association with the exercise movement number and the extended movement number. Obtain the rehabilitation code queue, second data group, basic code segment and core code group corresponding to each rehabilitation action, and calculate the matching coefficient between each rehabilitation action and each of the extended actions according to the aforementioned weighted fusion algorithm; If the matching coefficient exceeds the set threshold, the extended action is selected and added before or after its associated exercise action to generate an optimized rehabilitation auxiliary exercise.

6. The method for generating rehabilitation movements for orthopedic diseases based on traditional Chinese medicine exercises according to claim 5, characterized in that, Match at least one traditional Chinese medicine exercise as a rehabilitation aid and output it, including: Based on the generated optimized rehabilitation assistive exercises, according to the exercises and extended movements they contain, combined with standard exercise videos, the optimized rehabilitation assistive exercise videos are generated and output using video generation tools; Specifically, the movements of each exercise are judged, and if the range and speed of the movements have been optimized, then a voice prompt message is played when the exercise movements are played.

7. The method for generating rehabilitation movements for orthopedic diseases based on traditional Chinese medicine exercises according to claim 6, characterized in that, The method for generating rehabilitation movements for orthopedic diseases also includes: The motion optimization parameters are obtained, and corresponding rehabilitation equipment is selected or rehabilitation equipment parameters are set based on the motion optimization parameters and the joints and muscle tissues associated with the target motion, in order to limit the user's motion range and / or motion speed.

8. A system for generating rehabilitation movements for orthopedic diseases based on traditional Chinese medicine exercises, characterized in that, include: The data acquisition module is used to acquire information about the location of the affected area and formulate the required rehabilitation strategies. The exercise matching module is used to match at least one traditional Chinese medicine exercise as an auxiliary rehabilitation exercise according to the rehabilitation strategy and output it. The exercise matching module includes: The encoding unit (1) is configured to encode and store motion-related joints and muscle tissues based on the human kinematics model as an encoding reference model; The exercise movement analysis unit (2) is configured to break down the steps and movements of each traditional Chinese medicine exercise, number each step of the exercise movement and analyze its associated joints and muscle tissues, generate an exercise code queue to represent each step of the exercise movement according to the coding reference model, and combine the above exercise code queues to generate the exercise movement code set corresponding to the traditional Chinese medicine exercise. The rehabilitation action parsing unit (3) is used to number each rehabilitation action and analyze and obtain the associated joints and muscle tissues. According to the coding reference model, the codes corresponding to the associated joints and muscle tissues are combined in a set order to generate a rehabilitation code queue to represent each rehabilitation action and store it in association with the rehabilitation action number. According to the action sequence of the rehabilitation action, the rehabilitation code queues are combined to generate the rehabilitation action code set corresponding to the rehabilitation action. The matching analysis unit (4) is used to statistically analyze the matching degree between the rehabilitation action code set and the various exercise action code sets, and select the traditional Chinese medicine exercise corresponding to the exercise action code set with a high matching degree as rehabilitation auxiliary exercise. The matching analysis unit (4) includes: The queue temporary storage subunit (41) is configured to acquire and store each rehabilitation code queue contained in the rehabilitation action code set and each exercise code queue contained in each exercise action code set. The pre-set subunit (42) is configured to temporarily store a set of pre-set exercise action codes for matching; The first statistical subunit (43) is configured to count the number N1 of the same exercise code queue as the rehabilitation code queue in the above-mentioned set exercise action code set, count the number of rehabilitation code queues that have the same queue in the exercise action code set, and calculate the ratio Q1 of the above-mentioned number of rehabilitation code queues to the total number of rehabilitation code queues. The data group generation subunit (44) is configured to decompose multiple rehabilitation code queues contained in the rehabilitation action code set and generate multiple second data groups formed by a single code element, and to decompose multiple exercise code queues contained in the above-mentioned exercise action code set and generate multiple first data groups formed by a single code element. The second statistical subunit (45) is configured to compare each second data group corresponding to the rehabilitation action coding set with the multiple first data groups corresponding to the set exercise action coding set, count the number N2 of the first data groups that are the same as each second data group in each exercise action coding set, count the number of second data groups whose elements are the same as those in one of the first data groups, and calculate the ratio Q2 of the number of the second data groups to the total number of second data groups. The basic coding segment generation subunit (46) is configured to split each rehabilitation coding queue into multiple basic coding segments; The third statistical subunit (47) is configured to use multiple basic coding segments as matching references to search for coding segments that are the same as the basic coding segments in the exercise coding queue corresponding to the set exercise action coding set, count the number N3 of coding segments that are the same as the basic coding segments in the set exercise action coding set, count the number of basic coding segments that have the same coding segments in the set exercise action coding set, and calculate the ratio Q3 of the number of basic coding segments to the total number of basic coding segments. The core coding group generation subunit (48) is configured to extract key codes from each rehabilitation coding queue as array elements, and generate multiple core coding groups corresponding to each rehabilitation coding queue. The fourth statistical subunit (49) is configured to use multiple core coding groups as matching references to search for a queue of exercises that contains all the key codes contained in the core coding group from the exercise coding queue corresponding to the set exercise action coding set, count the number N4 of exercise coding queues that contain all the key codes in the set exercise action coding set, count the number of core coding groups that have the same coding group in the set exercise action coding set, and calculate the ratio Q4 of the number of core coding groups to the total number of core coding groups. The matching coefficient calculation subunit (410) assigns calculation weight values ​​to the quantities N1, N2, N3, N4, quantity ratio Q1, quantity ratio Q2, quantity ratio Q3 and quantity ratio Q4 respectively, and calculates and generates a matching coefficient to characterize the degree of matching between the rehabilitation action code set and the currently set exercise action code set according to the weighted fusion algorithm. The rehabilitation strategy includes rehabilitation actions corresponding to each rehabilitation stage, as well as the sequence, range, speed and number of times of each rehabilitation action. The set order is the sequence of movement of each associated joint and muscle tissue when performing related actions, or the order according to the importance of each joint and muscle tissue in the movement.

9. The orthopedic disease rehabilitation movement generation system based on traditional Chinese medicine exercises according to claim 8, characterized in that, The matching analysis unit (4) further includes: The motion parameter storage subunit is configured to set and store the appropriate motion amplitude, speed and number of repetitions for each rehabilitation stage. The target movement recognition subunit is configured to identify the movement that matches each rehabilitation movement based on the established rehabilitation assistive exercises, and mark it as the target movement. The parameter generation subunit is configured to determine the appropriate range of motion and speed of motion for each target movement based on the current rehabilitation stage, and generate motion optimization parameters. The movement adjustment subunit is configured to adjust each target movement according to the movement optimization parameters to generate a first rehabilitation auxiliary exercise. The action extension configuration subunit is configured to obtain the exercises and their numbers included in the rehabilitation assistive exercises, and to configure at least one extended action and number it for each exercise based on the concept of traditional Chinese medicine. An extended coding generation subunit is configured to analyze and combine the codes corresponding to the associated joints and muscle tissues according to a set order based on the coding reference model, based on the aforementioned extended action, to generate an extended coding queue to characterize the extended action. The matching degree calculation subunit is configured to obtain the rehabilitation code queue, second data group, basic code segment and core code group corresponding to each rehabilitation action, and calculate the matching coefficient between each rehabilitation action and each of the extended actions according to the aforementioned weighted fusion algorithm; The extended action merging subunit is configured to add extended actions with matching coefficients exceeding a set threshold before or after the relevant exercises in the first rehabilitation assistive exercise, thereby generating an optimized rehabilitation assistive exercise.

10. A computer-readable storage medium, characterized in that, It is loaded with a program module, which, when called and executed by the processor, is used to implement the method for generating rehabilitation movements for orthopedic diseases based on traditional Chinese medicine exercises as described in any one of claims 1-7.

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