Upper limb rhythm coordination training device and method

Abstract

The invention provides an upper limb rhythm coordination training device and method, and relates to the technical field of exercise training.The method comprises the steps that input user information of a user to be trained is received, user evaluation is conducted, and a user rating is obtained; according to the user information and the user rating, training parameters for training the user are configured, and the training parameters comprise music characteristic parameters; the training parameters are adopted to control lamp shooting training, and training input parameters of a user are received; and a training result of the training input parameters is analyzed and displayed. The technical problems that in existing upper limb rhythm coordination training, training individuation is insufficient, combination with an actual application scene is not tight, and a feedback monitoring mechanism is incomplete are solved, and the technical effects that individualized evaluation and training are achieved through multi-module cooperative work, the combination degree with the actual scene is enhanced, and the feedback monitoring mechanism is improved are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of sports training, and in particular to an upper limb rhythmic coordination training device and method. Background Art

[0002] In contemporary society, the incidence of brain trauma and spinal cord injury is increasing year by year due to traffic accidents, falls from heights, sports and entertainment, and collisions. Various chronic diseases, bad living habits, aging, and increased life pressure are important causes of stroke. In order to enable patients to recover as soon as possible and adapt to society, upper limb functional rehabilitation has become an important part of the rehabilitation training program. The existing traditional upper limb training is mainly aimed at restoring the patient's upper limb motor function. Through the training of joint range of motion, muscle strength, balance and coordination ability, etc., it has the effects of improving pain, improving upper limb motor function, improving the coordination of upper limb activities, and improving spasticity. However, the training form is relatively simple and mechanical. Long-term training patients are prone to fatigue and irritability, and the limitations are strong. In addition, a few clinical practices currently combine music rhythm with rehabilitation equipment, ignoring the impact of auditory stimulation on the motor area of ​​the brain.

[0003] Therefore, the existing upper limb rhythmic coordination training has technical problems such as insufficient training personalization, loose integration with actual application scenarios, and imperfect feedback monitoring mechanism. Summary of the invention

[0004] The present application provides an upper limb rhythmic coordination training device and method, which are used to solve the technical problems existing in the existing upper limb rhythmic coordination training, such as insufficient training personalization, loose integration with actual application scenarios, and imperfect feedback monitoring mechanism.

[0005] In view of the above problems, the present application provides an upper limb rhythmic coordination training device and method.

[0006] In a first aspect, the present application provides an upper limb rhythm coordination training device, the device comprising:

[0007] A user rating acquisition module, the user rating acquisition module is used to receive input user information of users to be trained, perform user evaluation, and obtain user ratings; a training parameter configuration module, the training parameter configuration module is used to configure training parameters for training the users according to the user information and user ratings, wherein the training parameters include music feature parameters; a clapping training control module, the clapping training control module is used to adopt the training parameters to control clapping training and receive user training input parameters; a training result display module, the training result display module is used to analyze the training results of the training input parameters and display them.

[0008] In a second aspect, the present application provides an upper limb rhythm coordination training method, the method comprising:

[0009] Receive input user information of a user to be trained, conduct user evaluation, and obtain a user rating; configure training parameters for training the user based on the user information and user rating, wherein the training parameters include music feature parameters; use the training parameters to control clapping training, and receive the user's training input parameters; analyze the training results of the training input parameters and display them.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] The upper limb rhythm coordination training device and method provided in the embodiment of the present application receives the input user information of the user to be trained, performs user evaluation, and obtains the user rating; configures the training parameters for training the user according to the user information and user rating; uses the training parameters to control the clapping training and receives the user's training input parameters; analyzes the training results of the training input parameters and displays them. The technical effect of realizing personalized evaluation and training through the collaborative work of multiple modules, enhancing the integration with the actual scene and improving the feedback monitoring mechanism is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A structural schematic diagram of an upper limb rhythmic coordination training device is provided for this application;

[0013] Figure 2 A flow chart of an upper limb rhythmic coordination training method is provided for this application.

[0014] Explanation of the reference numerals: user rating acquisition module 10 , training parameter configuration module 20 , light-shooting training control module 30 , training result display module 40 . DETAILED DESCRIPTION

[0015] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0016] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0017] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0018] Embodiment 1: Figure 1 As shown, the present application provides an upper limb rhythm coordination training device, the device comprising:

[0019] The user rating acquisition module 10 is used to receive input user information of a user to be trained, perform user evaluation, and obtain user ratings.

[0020] Specifically, the main function of the user rating acquisition module 10 is to receive relevant information of the user to be trained, and the user information includes the basic physical condition information of the patient. For example, whether there is brain trauma or spinal cord injury caused by traffic accidents, falls from heights, sports and entertainment, and collisions, and whether there are various chronic diseases. These information are crucial to understanding the patient's basic physical condition. At the same time, it also involves information related to upper limb motor function, such as data on joint range of motion, muscle strength, etc. This is because the training is mainly aimed at patients with upper limb motor coordination disorders, and these data can reflect the existing functional state of the upper limbs. In addition, whether the patient has cognitive impairment and psychological disorders is also important information content. After obtaining these user information, the user rating acquisition module 10 conducts a comprehensive evaluation of the user. This evaluation process conducts initial evaluation content such as Brunnstrom staging, muscle strength, joint range of motion, modified Ashworth, MMSE, Berg balance scale, VAS score, etc. on the patient. Through these evaluation methods, the user rating is finally obtained, and the rating result will provide an important basis for subsequent operations such as training parameter configuration.

[0021] The training parameter configuration module 20 is used to configure the training parameters for training the user according to the user information and the user rating, wherein the training parameters include music feature parameters.

[0022] Specifically, the training parameter configuration module 20 configures the training parameters according to the user information and user ratings provided by the user rating acquisition module 10, wherein the music feature parameters are an important component. First, the user information and ratings cover many aspects, including whether the patient has a history of brain trauma, spinal cord injury, etc., whether he suffers from chronic diseases, upper limb motor function-related data such as joint mobility, muscle strength, etc., as well as cognitive impairment and psychological disorders. The user rating is the result obtained after evaluating the patient by Brunnstrom staging, muscle strength, joint mobility, modified Ashworth, MMSE, Berg balance scale, VAS score, etc. Based on this information, the configuration of the music feature parameters is as follows: If the patient's muscle tension is level 0 (this is one of the user rating results obtained based on the muscle tension classification), fast-paced music will be selected, and the high-intensity interval training (HIIT) mode will be used at the same time. This configuration takes into account that patients with muscle tension level 0 need more dynamic and intense training stimulation.

[0023] When the patient's muscle tension is at level 1-2, music with alternating fast and slow rhythms will be selected. This music selection helps to adapt to the muscle tension state of such patients and provide a more suitable training rhythm.

[0024] For patients with muscle tension level 3-4, you can choose music with a slower rhythm, or music with a rhythm that changes from slow to fast. Music with this rhythm can relax the patient's muscle tension in the early stage and improve the patient's active movement performance in the middle and late stages.

[0025] For patients with upper limb edema and pain (this is also a relevant situation obtained from user information), the music rhythm and speed will be appropriately reduced to avoid aggravating the patient's discomfort due to the music rhythm being too fast or the intensity being too high. In addition to the music feature parameters, the training parameter configuration module 20 will also configure other training parameters according to the user's actual physical condition. For example, the therapist will select a suitable lighting quadrant for the patient based on the patient's muscle strength, pain condition, muscle tension, etc. (considering factors such as the quadrant color and the number and layout of lights in each quadrant), determine the training time and the interval time between lights on the panel, etc. These training parameters together constitute a complete configuration for training users to meet the personalized training needs of different users.

[0026] The clapping lights training control module 30 is used to adopt the training parameters to control the clapping lights training and receive the training input parameters of the user.

[0027] Specifically, music playback control is performed according to the music feature parameters determined by the training parameter configuration module 20. For example, if fast-tempo music is configured, the module will accurately play fast-tempo music tracks; if it is music with alternating fast and slow rhythms, it will also be played according to the set rhythm change rules, and the parameters such as the lighting quadrants (including the color and the number and layout of lights in each quadrant) selected by the therapist according to the patient's condition and the interval time between lights on the panel are set to ensure that the training environment meets the training requirements customized for the patient. Control is performed according to the training mode determined by the patient's muscle tension classification. For example, for the high-intensity interval training (HIIT) mode selected for muscle tension level 0, the module will strictly execute the training process according to the rules of this mode, including controlling the intensity and interval time of the training.

[0028] During the training process, the user will clap the lights according to the lights that are on. The clap light training control module 30 will receive information such as the time and order of the user pressing the lights as part of the user training input parameters. When the user presses the lights, the module will determine whether the user operation is correct based on the rhythm of the current music. If the light that is on is pressed, the correct rhythm in the music will be emitted; otherwise, an error prompt tone "Deng, Duang, Pia, etc." will be emitted, and the information on whether the user operation is correct or not will be recorded as another part of the training input parameters. These training input parameters will be passed to the training result display module 40 for subsequent analysis and processing.

[0029] The training result display module 40 is used to analyze the training results of the training input parameters and display them.

[0030] Specifically, the training result display module 40 is mainly responsible for processing and displaying the relevant data in the training process. First, the training input parameters are received from the clapping light training control module 30. These parameters include information such as the time, order, and whether the operation is correct when the user presses the light. According to the received parameters, the patient's clapping light speed and rhythm adaptability are analyzed. For example, by comparing the time interval between the user's clapping light and the beat interval of the music rhythm, the degree of adaptation between the two is calculated. At the same time, the clapping light accuracy is analyzed, that is, the proportion of the number of times the user correctly presses the light to the total number of operations is counted. After training, the results of the analysis are displayed on the screen, mainly showing the two key indicators of the patient's clapping light speed and rhythm adaptability and the clapping light accuracy. These indicators can intuitively reflect the patient's performance in the training process and the degree of mastery of the training content. The displayed results provide clear feedback. According to these results, the training effect can be understood, so as to adjust the subsequent training plan and parameter configuration. Patients can also understand their own training progress by viewing the results.

[0031] In a possible implementation, the user rating acquisition module 10 further includes:

[0032] A pre-trained user rater; receiving input user information of a user to be trained, wherein the user information includes one or more of Brunnstrom stage, muscle strength, range of motion, modified Ashworth, MMSE, Berg balance scale, and VAS score; inputting the user information into the user rater, and outputting a user rating.

[0033] Specifically, the purpose of pre-training the user rater is to be able to more accurately output reasonable user ratings based on the input user information. Since upper limb motor coordination training needs to be personalized for the specific conditions of different patients, and the upper limb function and status of users are affected by many factors, a pre-trained rater is needed to comprehensively consider these factors and improve the accuracy and reliability of the rating. Relevant information is obtained by retrieving the upper limb training data records of users in historical time. These historical data contain a large number of upper limb training conditions of different patients, covering various upper limb function states and related influencing factors. Sample user information sets are collected from these historical data. This information includes Brunnstrom staging, muscle strength, joint range of motion, modified Ashworth, MMSE, Berg balance scale, VAS score and other indicators related to upper limb function and patient status. This information of each sample user is collected to form a rich sample user information set. Each sample user in the collected sample user information set is rated and marked, and the upper limb function and status of each sample user is comprehensively evaluated in combination with various information indicators of the sample user, and a corresponding rating mark is given. By rating all sample users, a sample user rating set is obtained. Each element in this set corresponds to a rating result of a sample user, and corresponds one-to-one with the sample user information in the sample user information set. The sample user information set and the sample user rating set are used as supervised training data. These data will serve as the basis for training the user rater, so that the rater can learn the relationship between different user information and corresponding ratings. The user rater is trained using these supervised training data until the rater converges. Convergence means that the performance of the rater reaches a stable state, and its output rating results can maintain high accuracy and stability when facing new input user information. Through such a pre-training process, the user rater can better provide accurate user ratings for the upper limb rhythm coordination training device, thereby providing a reliable basis for subsequent training parameter configuration and other operations.

[0034] In the upper limb rhythmic coordination training device, the user information of the user to be trained must first be received as input. This user information is a multi-dimensional description of the user's upper limb function and physical state, covering multiple important aspects. These include Brunnstrom staging, which can reflect the stage of recovery of the patient's upper limb motor function; muscle strength information, which is used to understand the size of muscle strength; joint range of motion, which reflects the flexibility of the joints; modified Ashworth scale, which assesses the degree of muscle spasm; MMSE, which is used to detect the patient's cognitive function; Berg balance scale, which measures the patient's balance ability; VAS score, which intuitively reflects the patient's pain level. One or more of these information may be received as input by the device, depending on the patient's actual situation and evaluation needs.

[0035] After receiving the user information, it is input into a pre-trained user rater, which is trained and optimized with a large amount of data and can perform complex analysis and processing based on the input user information. It will comprehensively consider the various information indicators received and their interrelationships. For example, if the received user information shows that the patient's Brunnstrom stage is in the early stage, the muscle strength is weak, the joint mobility is small, and there is a certain degree of muscle spasm (the modified Ashworth scale score is high), then the user rater will not rate based on only one indicator, but will integrate this information and output a comprehensive user rating. This user rating will serve as an important basis for subsequent training parameter configuration and other operations, providing a basis for formulating personalized training plans.

[0036] In a possible implementation, the user rating acquisition module 10 further includes:

[0037] Retrieve the user's upper limb training data records within the historical time, collect a sample user information set, and perform rating identification to obtain a sample user rating set; use the sample user information set and the sample user rating set as supervised training data to train the user rater until convergence.

[0038] Specifically, first, the user upper limb training data records in the historical time are retrieved as required. From these records, the sample user information set is carefully collected. For each sample user, relevant information such as Brunnstrom stage, muscle strength, joint mobility, modified Ashworth, MMSE, Berg balance scale, VAS score, etc. is extracted. This information constitutes the input feature vector of the decision tree algorithm. Then, the collected sample user information set is rated and marked. Taking into account various indicators, the upper limb function and status of each sample user are evaluated and given corresponding ratings. By rating all sample users, a sample user rating set is obtained. The rating results in this set will be used as the target output of the decision tree algorithm. When the decision tree algorithm starts to be built, the first thing to do is to select an optimal split attribute. In this scenario, considering each feature in the sample user information set, such as Brunnstrom stage, muscle strength, etc., the algorithm will calculate the information gain or other relevant indicators of each attribute to determine which attribute can best distinguish different rating categories. For example, if it is found that the attribute Brunnstrom stage has a high information gain for distinguishing sample users with different ratings, it will be selected as the split attribute first. Based on the selected split attribute, the sample user information set is divided according to the different values ​​of the attribute. For example, if Brunnstrom stage has different stage values, the sample users are divided into different subsets according to these stage values. Then, for each subset, the process of selecting split attributes and dividing is repeated until a certain stopping condition is met. The stopping condition may include that the ratings of the sample users in the subset are exactly the same, or the size of the subset is less than a certain set threshold. Through such continuous splitting and division, a decision tree structure is gradually constructed. The sample user information set and the sample user rating set are used as supervised training data to train the constructed decision tree. For each input sample user information, the decision tree predicts a rating result according to its internal tree structure and splitting rules. Then, the predicted result is compared with the actual sample user rating. If the predicted result is inconsistent with the actual result, the decision tree needs to be adjusted. The adjustment method includes reselecting the split attribute, modifying the tree structure, etc. During the training process, it is necessary to constantly judge whether the decision tree has converged. The convergence criterion can be that in several consecutive training iterations, the decision tree's prediction accuracy for sample user ratings no longer improves significantly. When the decision tree converges, it means that it has learned the effective relationship between sample user information and ratings, and can make relatively accurate rating predictions for new input user information. At this point, the trained decision tree can be used as a user rater to rate new users to be trained.

[0039] In a possible implementation, the training parameter configuration module 20 further includes:

[0040] According to the user information, the training quadrant for the user to perform upper limb clapping training is configured; according to the user rating, the music characteristic parameters for the user to perform upper limb clapping training are configured, wherein the music characteristic parameters include music type, music rhythm speed, music time and clapping frequency.

[0041] Specifically, when configuring the training quadrants for upper limb clapping training for users, it is necessary to fully consider the key elements in the user information. First, the user's muscle strength is an important consideration. If the user's muscle strength is weak, then the training quadrant with relatively low strength requirements should be selected. For example, the layout of the lights in the quadrant is relatively concentrated, and the user can reach the area without overstretching the arm, so as to avoid the inability to complete the clapping operation due to insufficient strength. Secondly, the range of motion of the joints also plays a key role. For users with limited joint mobility, quadrants that do not require large joint movements should be selected. For example, avoid quadrants that require large rotation or extension of the arms, and choose areas that are more suitable for their joint range of motion to ensure that users can smoothly perform clapping training. The degree of muscle spasm reflected by the modified Ashworth scale should also not be ignored. For users with high muscle spasm, training quadrants that can relieve muscle tension should be selected, and quadrants with loose layouts and relatively simple operations should be given priority to avoid tense muscles affecting the accuracy and continuity of clapping. In addition, the user's pain level (reflected by the VAS score) will also affect the choice of training quadrants. If the user's pain is more obvious, then it is necessary to choose a quadrant that is less stimulating to the pain. For example, choose quadrants where the light flashes at a moderate frequency, and will not cause visual fatigue and discomfort due to frequent flashing, thereby aggravating the pain. At the same time, factors such as the color of the quadrant can also be considered to avoid overly glaring colors that cause more discomfort to users in pain. By comprehensively considering information such as the user's muscle strength, joint mobility, degree of muscle spasm, and degree of pain, the training quadrants for upper limb light-clapping training can be accurately configured for the user to improve the effectiveness of the training and the user's comfort.

[0042] When configuring the type of music based on user ratings, the recovery of the user's upper limb function should be fully considered. If the user rating shows that the upper limb function is in the early stage of recovery, the type of music selected should be slow and soothing music, such as slow and soothing music. This is because in the early stage, the user's upper limb coordination and motor ability are relatively weak. The slower rhythm can give the user enough time to perceive and respond to the music, so as to better control the upper limb clapping action and avoid the situation of panic, uncoordinated movement and premature fatigue due to too fast rhythm. On the contrary, if the user rating shows that the upper limb function has recovered to a good level, such as the upper limb strength and coordination have been significantly improved, then fast-paced technical music is more appropriate. This type of music can provide users with a more challenging training environment, inspire users to further improve the motor ability and coordination of the upper limbs, and prompt users to complete the clapping action more agilely under the guidance of fast-paced music to adapt to higher-intensity training requirements. The configuration of the music rhythm speed is crucial to the training effect. It is closely dependent on the degree of upper limb function recovery reflected by the user rating. For users whose upper limb function recovery is relatively slow, the rhythm speed of the music should be appropriately reduced. This is because such users may have certain limitations in joint mobility and muscle strength, and cannot keep up with the faster rhythm. After reducing the rhythm speed, users can more calmly perform the clapping action according to the rhythm of the music, improving the accuracy and continuity of the action. For example, for users with limited joint mobility, a slower rhythm can give them enough time to complete the arm extension and clapping operation; for users with weak muscle strength, it can also avoid the lack of strength due to too fast rhythm, which affects the accuracy of clapping. For users whose upper limb function recovers faster, the music rhythm speed can be appropriately increased. The purpose of this is to further stimulate users and prompt them to speed up the movement speed of their upper limbs, thereby improving the coordination and flexibility of their upper limbs. As the upper limb function continues to recover, users can better adapt to the faster rhythm, and while increasing the movement speed, they can also better cooperate with the music to achieve a more efficient training effect. The configuration of music time needs to comprehensively consider the user's endurance and concentration, both of which are related to the user rating. If the user rating shows that the user has poor endurance or difficulty concentrating, the music time should be appropriately shortened. Because during the training process, if the music time is too long, users with insufficient endurance will be overly tired, thus affecting the training effect; users with poor concentration will be distracted by long training and miss some clap light prompts, reducing the accuracy of the training. For example, for users with cognitive impairment, they often have difficulty concentrating for a long time, so the music time configured for such users should be relatively short to ensure the effectiveness and accuracy of the training.The frequency of clapping is closely related to the speed of the music rhythm, and it also needs to be adjusted according to the speed and coordination of the user's upper limb movement. If the user's upper limb movement speed is slow, the frequency of clapping should be reduced accordingly. This is because a lower frequency of clapping allows the user to better match the clapping action with the rhythm of the music, avoiding the user's inability to keep up with the rhythm and making mistakes due to the high frequency of clapping. For example, in the early stage of the user's upper limb function recovery, the upper limb movement speed is usually slow. At this time, the frequency of clapping should be set lower so that the user can better adapt to the training rhythm. On the contrary, if the user's upper limb movement speed is fast, the frequency of clapping should be appropriately increased, which can further improve the user's upper limb coordination and flexibility, so that the user can complete the clapping action more accurately while moving quickly. However, the frequency of clapping should not be too high, otherwise the user may feel frustrated because it is difficult to keep up, which will affect the enthusiasm of training. Therefore, when adjusting the frequency of clapping, it is necessary to comprehensively consider the actual situation of the user to ensure that he can adapt to the training rhythm and maintain the enthusiasm and effectiveness of the training.

[0043] In a possible implementation, the training parameter configuration module 20 further includes:

[0044] Collect a set of sample user ratings, and according to different sample user ratings, set a set of sample music types, a set of sample music rhythm speeds, a set of sample music time and a set of sample clap frequencies as a set of sample music feature parameters; construct a mapping relationship between the sample user rating set and the sample music feature parameter set to obtain a music feature parameter classifier; input the user ratings into the music feature parameter classifier for mapping and classification to obtain music feature parameters.

[0045] Specifically, first, a sample user rating set is collected from a large amount of historical data. This process is based on the evaluation and identification of the upper limb training conditions of many users. For example, by comprehensively analyzing the upper limb function recovery stage (such as Brunnstrom stage), muscle strength, joint range of motion, modified Ashworth, MMSE, Berg balance scale, VAS score and other information of different users, the rating results of each sample user are obtained, and then the sample user rating set is formed. According to different sample user ratings, different sample music feature parameter sets are set respectively. For the sample music type set, if a sample user rating shows that the upper limb function is in the early stage of recovery, it is set to soothing music with slow rhythm; if the rating shows that the upper limb function is well recovered, it is set to technical music with fast rhythm, etc. For the sample music rhythm speed set, according to the sample user rating, for the sample user with slow upper limb function recovery, a slower rhythm speed value is set; for the sample user with fast recovery, a faster rhythm speed value is set. For the sample music time set, when the sample user rating indicates poor endurance or lack of concentration, a shorter music time value is set; otherwise, a longer value is set. For the sample clapping frequency set, combined with the sample user ratings and upper limb movement speed, a lower clapping frequency value is set for sample users with slow upper limb movement speed; and a higher value is set for sample users with fast movement speed.

[0046] The mapping relationship is established by the support vector machine (SVM) algorithm to obtain the music feature parameter classifier. First, the sample user rating set and the sample music feature parameter set are preprocessed, and each rating in the sample user rating set is numerically encoded, for example, different rating levels are mapped to different values. For the sample music feature parameter set, the music type is classified and encoded (such as encoding slow rhythm soothing music as 0, and fast rhythm skill music as 1), and the music rhythm speed, music time and clapping frequency are normalized to make their values ​​within a suitable range. The preprocessed data is used as the input of the SVM algorithm. The goal of the SVM algorithm is to find an optimal hyperplane that can distinguish the music feature parameters corresponding to different ratings to the greatest extent. By calculating the distance from the sample point to the hyperplane and using kernel functions (such as linear kernels, Gaussian kernels, etc.) to process nonlinearly separable data, the position and parameters of the hyperplane are continuously adjusted to achieve an accurate description of the relationship between different ratings and music feature parameters. When the SVM algorithm converges, that is, when the optimal hyperplane is found, a music feature parameter classifier is obtained. This classifier can output the corresponding music feature parameters based on the input user rating (which has been numerically encoded) by judging which side of the hyperplane it is on and calculating the distance to the hyperplane. For example, if the point corresponding to the input user rating value is on one side of the hyperplane and is closest to a certain music feature parameter pattern on that side, then the music feature parameters corresponding to the pattern, such as the music type, music rhythm speed, music time, and clapping frequency, are output.

[0047] After the music feature parameter classifier is determined, the user rating of the new user is input into the classifier for mapping and classification, so as to obtain the music feature parameters suitable for the user. First of all, the user rating is the result obtained after evaluating the user's upper limb function, physical condition and other aspects. It reflects the user's upper limb training needs and ability level at a specific stage. This rating is used as input data and passed to the constructed music feature parameter classifier. After receiving the user rating, the classifier will process it according to the mapping relationship established inside it. According to the corresponding pattern between different ratings and music feature parameters learned in the training stage, the input user rating is analyzed to determine which side of the hyperplane the point corresponding to the input user rating is on, and the closest pattern is determined by calculating the distance to each music feature parameter pattern. After being processed by the classifier, the corresponding music feature parameters are finally output. These music feature parameters include music type, music rhythm speed, music time, and clapping frequency. The music type is determined based on the user rating, and is suitable for the user's current training stage, whether it is slow or fast-paced music; the music rhythm speed will be adjusted according to the user's upper limb function recovery degree. Users with slower recovery may get a slower rhythm speed, while users with better recovery may get a faster rhythm speed; the music time will take into account the user's endurance and concentration. Users with poor endurance or lack of concentration may get a shorter music time; the clapping frequency will also match the user's upper limb movement speed. Users with slow upper limb movement speed will have a lower clapping frequency, while users with fast movement speed will have a higher clapping frequency. In this way, music feature parameters suitable for each user's specific situation can be tailored to better support upper limb clapping training, improve training effects and user experience.

[0048] In a possible implementation, the clapping training control module 30 further includes:

[0049] The training parameters are used to control music playback and lighting, and the user's clapping operation is monitored; if the clapping operation and the lighting are consistent, the music continues to play; if the clapping operation and the lighting are inconsistent, the music is stopped and an error prompt tone is played, and after the error prompt tone ends, the music playback and the brightness are continued; the number of times the light is turned on and the number of times the clapping operation and the lighting are consistent are recorded as training input parameters.

[0050] Specifically, when conducting upper limb rhythm coordination training, once the training parameters are determined, these parameters are used to control the training process. First, according to the music feature parameters in the training parameters, the music playback is controlled. If the music type is fast-paced technical music, then the corresponding style of music will be selected from the music library for playback, and the music will be played at a specific music rhythm speed. For example, for users with better upper limb function recovery, faster rhythm music is played to increase the challenge of training. At the same time, the lighting operation is performed according to the lighting settings in the training parameters, which involves determining the order, frequency, and area (training quadrant) of lighting. For example, for users at different training stages, the lighting frequency will be different. Users in the early stage of upper limb function recovery will have a lower lighting frequency so that they have enough time to react and complete the clapping action. During the music playback and lighting process, the user's clapping operation is continuously monitored, and through sensors or other monitoring devices, it can be sensed in real time whether the user presses the light that is on. If the user presses the light, the time and location of this operation and other information are recorded. In addition, the user's clapping operation is compared with the current music rhythm and lighting situation to determine whether the user's operation is correct and timely. For example, when the music rhythm is fast and the lights are on, the user is expected to press the corresponding light quickly. If the user presses the correct light within the specified time, it will be considered a correct light-pressing operation. On the contrary, if the user does not press the light within the appropriate time or presses the wrong light, it will be recorded as an incorrect operation. Such a monitoring process can provide important data basis for subsequent analysis of training results.

[0051] During the entire upper limb rhythmic coordination training process, the user's light-clapping operation is continuously judged and responded to. If the user's light-clapping operation matches the light-on situation, it means that the user accurately pressed the corresponding light when the light was on, and the user's operation is considered correct. In this case, the music will continue to play to maintain the continuity and fluency of the training. The continuous playing of music can provide users with stable rhythm guidance, allowing users to better keep up with the rhythm of training and continuously improve the coordination and reaction speed of the upper limbs. However, if the user's light-clapping operation does not match the light-on situation, it means that the user has made an incorrect operation. At this time, the music will be stopped immediately to attract the user's attention. At the same time, an error prompt tone will be played. The purpose of this error prompt tone is to clearly convey to the user that there is a problem with their operation. The error prompt tone can be a specific sound signal, such as a short alarm sound or a specific prompt tone melody. During the playing of the error prompt tone, the user can realize his or her mistake and reflect on it. When the error prompt tone ends, the music playback and light-up operations will continue. This design is to provide users with timely feedback, allowing them to quickly adjust their status after an error occurs, re-engage in training, and continuously improve their clapping accuracy and upper limb rhythmic coordination ability.

[0052] During the entire upper limb rhythmic coordination training process, important data is continuously recorded as training input parameters. Among them, the number of times the light is on is a key recording indicator. Whenever the system controls the light to be on, whether it is according to the preset music rhythm, training progress or other rules, the number of times the light is on will be accumulated and recorded. For example, in a training session, there will be multiple light-on situations during the music playback. Accurately record each light-on situation, whether it is a quick and continuous light-on situation or a light-on situation with a long interval, and nothing will be missed. At the same time, pay close attention to the user's light-clapping operation and record the number of times the light-clapping operation matches the light-on situation. When the light is on, if the user accurately presses the corresponding light, this operation is recorded as a match, and the number of matches is accumulated. Through continuous monitoring and recording, the user's performance in the training process is accurately grasped. These recorded light-on times and the number of times the light-clapping operation matches the light-on situation will serve as important training input parameters, providing a solid data foundation for subsequent training result analysis and evaluation. These parameters can reflect the user's accuracy, reaction speed and development and changes in upper limb rhythmic coordination ability in training, so as to better meet the user's personalized training needs.

[0053] In a possible implementation, the training result display module 40 further includes:

[0054] According to the training input parameters, the ratio of the number of times the clapping operation matches the lighting operation to the number of times the lighting operation occurs is calculated as the training accuracy rate; the training accuracy rate is used as the training result for output display.

[0055] Specifically, after completing the upper limb rhythmic coordination training, in-depth analysis is performed based on the collected training input parameters to accurately evaluate the user's training effect. First, the training accuracy is calculated using the number of times the clapping operation matches the light on and the total number of times the light is on in the training input parameters. This calculation process is very critical and directly reflects the user's performance level in the training. The number of times the clapping operation matches the light on is divided by the number of times the light is on, and the ratio obtained is the training accuracy. For example, if in a training session, the total number of times the light is on is 100 times, and the number of times the user's clapping operation matches the light on is 80 times, then the training accuracy is 80%.

[0056] This training accuracy is output as an important training result. The output can be displayed in a variety of ways, such as intuitively presenting it in digital form on the display screen, or showing the trend of the training accuracy over time in the form of a chart, so as to clearly understand the progress of the training. By outputting and displaying the training accuracy, you can clearly understand the progress and shortcomings in the training, so as to adjust the training strategy more specifically and improve the training effect. At the same time, the training status can be evaluated according to the training accuracy, and a more personalized training plan can be formulated to better meet the needs of rehabilitation or skill improvement.

[0057] Embodiment 2: Based on the same inventive concept as the upper limb rhythm coordination training device in the above embodiment, Figure 2 As shown, the present application provides an upper limb rhythm coordination training method, the method comprising:

[0058] Step S100: receiving input user information of a user to be trained, performing user evaluation, and obtaining a user rating.

[0059] Step S200: configuring training parameters for training the user according to the user information and the user rating, wherein the training parameters include music feature parameters.

[0060] Step S300: adopting the training parameters to control the light-patting training and receiving the training input parameters from the user.

[0061] Step S400: analyzing the training results of the training input parameters and displaying them.

[0062] Furthermore, the step S100 further includes:

[0063] Step S110: Pre-training the user rater.

[0064] Step S120: receiving input user information of the user to be trained, wherein the user information includes one or more of Brunnstrom stage, muscle strength, joint range of motion, modified Ashworth, MMSE, Berg balance scale, and VAS score.

[0065] Step S130: input the user information into the user rater and output the obtained user rating.

[0066] Furthermore, the step S110 further includes:

[0067] Step S111: retrieve the user's upper limb training data records within the historical period, collect a sample user information set, perform rating identification, and obtain a sample user rating set.

[0068] Step S112: using the sample user information set and the sample user rating set as supervised training data, and training the user rater until convergence.

[0069] Furthermore, the step S200 further includes:

[0070] Step S210: configuring the training quadrant for the user to perform upper limb clapping training according to the user information.

[0071] Step S220: According to the user rating, configure the music characteristic parameters for the user to perform upper limb clapping training, wherein the music characteristic parameters include music type, music rhythm speed, music time and clapping frequency.

[0072] Furthermore, the step S220 further includes:

[0073] Step S221: Collect a set of sample user ratings, and according to different sample user ratings, set a set of sample music types, a set of sample music rhythm speeds, a set of sample music time, and a set of sample clap frequencies as a set of sample music feature parameters.

[0074] Step S222: construct a mapping relationship between the sample user rating set and the sample music feature parameter set to obtain a music feature parameter classifier.

[0075] Step S223: input the user rating into the music feature parameter classifier to map the classification and obtain the music feature parameters.

[0076] Furthermore, the step S300 further includes:

[0077] Step S310: using the training parameters to control music playing and lighting, and monitoring the user's light-clapping operation.

[0078] Step S320: If the clapping operation and the lighting are consistent, the music continues to play; if the clapping operation and the lighting are inconsistent, the music stops playing and an error prompt tone is played; after the error prompt tone ends, the music continues to play and the brightness is adjusted.

[0079] Step S330: Record the number of times the light is turned on, and the number of times the light tapping operation matches the light turning on, as training input parameters.

[0080] Furthermore, the step S400 further includes:

[0081] Step S410: Calculate the ratio of the number of times the light-up operation matches the number of times the light-up operation occurs based on the training input parameters as the training accuracy.

[0082] Step S420: The training accuracy is used as the training result for output display.

[0083] Through the above detailed description of the upper limb rhythmic coordination training device in this specification, those skilled in the art can clearly understand the upper limb rhythmic coordination training device and method in this embodiment. As for the method disclosed in the embodiment, since it corresponds to the device disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0084] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An upper limb rhythmic coordination training device, characterized in that: The device comprises: A user rating acquisition module, the user rating acquisition module is used to receive input user information of a user to be trained, perform user evaluation, and obtain a user rating; A training parameter configuration module, the training parameter configuration module is used to configure training parameters for training the user according to the user information and the user rating, wherein the training parameters include music feature parameters; A clapping light training control module, the clapping light training control module is used to control the clapping light training by using the training parameters, and receive the training input parameters of the user; The training result display module is used to analyze the training results of the training input parameters and display them.

2. The upper limb rhythm coordination training device according to claim 1, characterized in that: Receive input user information of the user to be trained, perform user evaluation, and obtain user ratings, including: Pre-trained user rater; Receiving input user information of a user to be trained, wherein the user information includes one or more of Brunnstrom stage, muscle strength, joint range of motion, modified Ashworth, MMSE, Berg balance scale, and VAS score; The user information is input into the user rater, and the user rating is obtained as an output.

3. The upper limb rhythm coordination training device according to claim 2, characterized in that: Pre-trained user raters, including: Retrieve the user's upper limb training data records within the historical period, collect the sample user information set, and perform rating identification to obtain the sample user rating set; The sample user information set and the sample user rating set are used as supervised training data to train the user rater until convergence.

4. The upper limb rhythm coordination training device according to claim 2, characterized in that: Configuring training parameters for training the user according to the user information and the user rating includes: According to the user information, configure the training quadrant for the user to perform upper limb clapping training; According to the user rating, music characteristic parameters for the user to perform upper limb clapping training are configured, wherein the music characteristic parameters include music type, music rhythm speed, music time and clapping frequency.

5. The upper limb rhythm coordination training device according to claim 4, characterized in that: According to the user rating, the music feature parameters for the user to perform upper limb clapping training are configured, including: Collecting a sample user rating set, and setting a sample music type set, a sample music rhythm speed set, a sample music time set, and a sample clap frequency set according to different sample user ratings as a sample music feature parameter set; Constructing a mapping relationship between the sample user rating set and the sample music feature parameter set to obtain a music feature parameter classifier; The user rating is input into the music feature parameter classifier to map the classification and obtain the music feature parameters.

6. The upper limb rhythm coordination training device according to claim 1, characterized in that: The training parameters are used to control the light-clapping training, including: Using the training parameters, controlling the music playing and lighting, and monitoring the user's light-clapping operation; If the clapping operation and the lighting are consistent, the music continues to play; if the clapping operation and the lighting are inconsistent, the music stops playing and an error prompt tone is played, and the music continues to play and the brightness is adjusted after the error prompt tone ends; The number of times the light is on and the number of times the tapping operation matches the light being on are recorded as training input parameters.

7. The upper limb rhythm coordination training device according to claim 6, characterized in that: Analyze the training results of the training input parameters and display them, including: According to the training input parameters, the ratio of the number of times the light-up operation matches the number of times the light-up operation occurs to the number of times the light-up operation occurs is calculated as the training accuracy rate; The training accuracy is used as the training result and outputted for display.

8. An upper limb rhythmic coordination training method, characterized in that: The method comprises: Receive input user information of the user to be trained, perform user evaluation, and obtain user ratings; According to the user information and the user rating, configuring training parameters for training the user, wherein the training parameters include music feature parameters; Using the training parameters, controlling the light-patting training, and receiving the training input parameters from the user; The training results of the training input parameters are analyzed and displayed.

Images