Balance training instrument combining brain function training and limb training

Abstract

The invention relates to the technical field of training instruments, and discloses a brain function training and limb training combined balance training instrument which comprises a supporting base, a balancing instrument is fixedly connected to one side of the supporting base, a signal collecting module is installed in the balancing instrument, a supporting frame is fixedly connected to the other side of the supporting base, and a signal collecting module is installed in the supporting frame. A supporting table is fixedly connected to the inner wall of the supporting frame, tDCS equipment is arranged on the upper surface of the supporting table, a charging module and an electrical stimulation module are installed in the tDCS equipment, and a display screen is arranged on one side of the supporting frame. By adopting a collaborative optimization mechanism of tDCS stimulation and balanced cognitive training, the training scheme can adjust stimulation parameters in real time according to the motion control and cognitive load of the user. The technical effect of accurately matching individual demands is achieved. Compared with a scheme of fixing stimulation parameters in a single training mode in the prior art, the problem of insufficient individuation is solved, and the adaptability and effectiveness of training are improved.

Description

Technical Field

[0001] The invention relates to the technical field of training instruments, in particular to a balance training instrument which combines brain function training with limb training. Background Art

[0002] In the field of modern rehabilitation training and motor function enhancement, balance training and cognitive training are widely used to improve the function of the nervous system and enhance the motor coordination ability and cognitive level of patients or specific populations. However, existing training methods still have certain limitations in training mode, data collection, stimulation feedback, etc., making it difficult to fully optimize the training effect.

[0003] Most of the current balance training systems rely solely on mechanical training, visual feedback or center of gravity control for training, such as providing training stimulation through dynamic balancers, gait training platforms or virtual reality systems. This type of method only regulates the user's external motor performance, while ignoring the neural regulation ability of the cerebral cortex. Simply put, the user's movement pattern can be trained, but the plasticity of the nervous system has not been effectively intervened. This leads to a lag in the training effect and differences in individual adaptability, affecting the effectiveness of long-term training.

[0004] Traditional balance training programs usually use preset fixed training parameters, and users, whether beginners or advanced, all train according to the same set of patterns. Although some systems will make manual adjustments based on user performance, this adjustment method is often delayed and relies on manual judgment, lacking accurate data analysis and adaptive adjustment mechanisms. Users with strong individual adaptability may feel that the training is not challenging enough, while for users with poor adaptability, the training may be too difficult and may even cause fatigue or decreased adaptability, affecting the continuity of training. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a balance training device that combines brain function training and limb training, which solves the problems of insufficient personalization, delayed training feedback, single data collection and lack of neural regulation in the prior balance training.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a balance training device that combines brain function training and limb training, comprising a support base, one side of the support base is fixedly connected to a balance instrument, a signal acquisition module is installed inside the balance instrument, the other side of the support base is fixedly connected to a support frame, the inner wall of the support frame is fixedly connected to a support platform, a tDCS device is arranged on the upper surface of the support platform, a charging module and an electrical stimulation module are installed inside the tDCS device, and a display screen is arranged on one side of the support frame.

[0007] A balance training system that combines brain function training and limb training, including; A data acquisition module, which is connected to a balance meter and a tDCS device via Bluetooth, and is used to collect the user's motion data, balance state data, and cognitive training-related data; A data analysis module, which is connected to the data acquisition module, and is used to process the collected data and generate training feedback; A program management module, which is connected to the data analysis module, and is used to store, manage, and execute training programs; A balance and cognitive dual-function training module, which is connected to the program management module, and is also connected to the balance meter and the display screen via Bluetooth, and is used to receive training programs for limb training and cognitive training; A tDCS stimulation module, which is connected to the program management module, and is also connected to the tDCS device via Bluetooth, and is used to receive training programs to perform transcranial direct current stimulation on the user.

[0008] Preferably, the program management module includes; A balance and cognitive training management unit, which is used to coordinate and execute different balance and cognitive training programs; A tDCS electrical stimulation management unit, which is used to control the current intensity, time, and stimulation mode of tDCS stimulation; A Bluetooth management unit, which is used to wirelessly transmit data to external devices; A user management unit, which is used to store user information and personalized training parameters; A record management unit, which is used to store training history data and training effect evaluation; A user interaction unit, which is connected to the display screen, and supports user operations and visual display of training feedback.

[0009] Preferably, the balance and cognitive dual-function training module includes; A balance and cognitive evaluation unit, which is used to evaluate the user's basic balance ability and cognitive function; A balance training unit, which is used to perform balance training programs; A cognitive training unit, which is used to perform cognitive training programs.

[0010] Preferably, the tDCS stimulation module includes; A charging module, which is used to provide a stable power supply for the electrical stimulation module; An electrical stimulation module, which can provide different modes of current stimulation to affect the neural activities of specific brain regions.

[0011] Preferably, the balance training unit includes; A single-leg balance training unit, which conducts training by detecting the stability of the user when standing on one leg; A dynamic balance training unit for training the user's balance ability in a dynamic environment; A maze training unit that guides the user to complete specific balance tasks by simulating maze paths.

[0012] Preferably, the cognitive training unit includes; A refresh training unit that trains the user's short-term memory ability through picture or character tasks; A visual template training unit that enhances the user's visual cognitive ability through pattern recognition tasks; A phonological loop training unit that enhances executive function and language processing ability through speech tasks; A cognitive flexibility training unit that improves the user's cognitive adaptation ability through task switching training.

[0013] Preferably, the data analysis module adopts an adaptive threshold calculation method, dynamically adjusts the training difficulty according to the user's training data, and determines the training feedback threshold based on the following calculation formula; ; Where: is the mean of the user's historical training data; is the standard deviation of this data; and are adjustment coefficients for optimizing the training intensity matching.

[0014] Preferably, the tDCS electrical stimulation management unit can dynamically adjust the stimulation parameters according to the user's training status, including the stimulation current intensity, electrode polarity, and stimulation duration, where the stimulation current magnitude is calculated according to the following formula: ; Where: is the applied voltage; is the electrode contact impedance.

[0015] Preferably, the user interaction unit includes a remote data interaction module, which is used to upload the user's training data to the cloud server and supports remote expert intervention and personalized training plan adjustment.

[0016] The present invention provides a balance trainer that combines brain function training and limb training. It has the following beneficial effects: 1. By adopting the collaborative optimization mechanism of tDCS stimulation and balance cognitive training, the training scheme of the present invention can adjust the stimulation parameters in real time according to the user's movement control and cognitive load, achieving the technical effect of accurately matching individual needs. Compared with the scheme of fixed stimulation parameters in a single training mode in the prior art, the problem of insufficient personalization is solved, and the adaptability and effectiveness of training are improved.

[0017] 2. Through the multi-dimensional sensing fusion technology of the data acquisition module, the present invention realizes the high-precision synchronous acquisition of the user's movement data, balance state and cognitive training data, ensuring the integrity and real-time nature of the data. Compared with the traditional scheme relying on a single sensor, the problems of data loss and asynchronous timing are overcome, making the training feedback more accurate and reliable.

[0018] 3. By adopting the personalized training strategy optimization mechanism of the scheme management module, the present invention can dynamically adjust the difficulty and mode of training tasks based on the user's historical training data, current training status and tDCS feedback, making the training scheme more targeted. Compared with the way of presetting a fixed training scheme in the prior art, the problem of low training adaptability of users is solved, and the sustainability and optimization ability of training are enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a perspective view of the present invention; Figure 2 is a system framework diagram of the present invention; Figure 3 is a schematic diagram of the scheme management module of the present invention; Figure 4 is a schematic diagram of the balance cognitive dual-function training module of the present invention; Figure 5 is a schematic diagram of the tDCS stimulation module of the present invention; Figure 6 is a schematic diagram of the balance training unit of the present invention; Figure 7 is a schematic diagram of the cognitive training unit of the present invention.

[0020] Among them, 1. Support base; 2. Balance meter; 3. Support frame; 4. tDCS device; 5. Display screen; 6. Support table. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0022] Please refer to the attached Figure 1 , an embodiment of the present invention provides a balance training instrument that combines brain function training and limb training, including a support base 1. One side of the support base 1 is fixedly connected with a balance instrument 2. A signal acquisition module is installed inside the balance instrument 2. The other side of the support base 1 is fixedly connected with a support frame 3. The inner wall of the support frame 3 is fixedly connected with a support table 6. A tDCS device 4 is arranged on the upper surface of the support table 6. A charging module and an electrical stimulation module are installed inside the tDCS device 4. One side of the support frame 3 is provided with a display screen 5; Specifically, the user stands on the balance instrument 2 and places the arms on the support frame 3 for support, and thus can perform balance training. By wearing the tDCS device 4 on the head and cooperating with the display screen 5, the effect of cognitive training can be achieved.

[0023] Please refer to the attached Figure 2 - attached Figure 7 , a balance training system that combines brain function training and limb training, including; A data acquisition module, which is connected to the balance instrument 2 and the tDCS device 4 via Bluetooth, and is used to collect the user's motion data, balance state data, and cognitive training-related data; Specifically, in this embodiment, the data acquisition module is connected to the balance instrument 2 and the tDCS device 4 via Bluetooth, and is responsible for collecting the user's motion data, balance state data, and cognitive training-related data in real time. The core function of this module is to accurately and stably obtain various physiological and behavioral data generated by the user during limb training and cognitive training, and transmit these data to the data analysis module for processing and feedback through wireless communication.

[0024] In one implementation, the data acquisition module includes a set of sensors for collecting motion data in real time. These sensors can be accelerometers, gyroscopes, and force sensors, which can capture the user's body motion information in different balance training states such as standing and walking. For example, the balance instrument 2 can monitor indicators such as the user's standing stability, sway amplitude, and tilt angle through accelerometers and gyroscopes. By collecting these data, the system can accurately evaluate the user's balance ability and provide a basis for subsequent training adjustment.

[0025] In the data acquisition module, the signal acquisition module is also responsible for converting the balance state data and motion data into digital signals and wirelessly transmitting them to the data analysis module via Bluetooth. Since these data acquisitions are closely related to the user's training process, the module needs to ensure high-precision and low-latency transmission of the data. For this reason, low-power Bluetooth technology is adopted to ensure that the data can be transmitted in real time and stably.

[0026] To ensure the efficiency and accuracy of data collection, the signal collection process in the data collection module needs to go through precise calculations and filtering. Based on the raw data provided by the data collection module, the data analysis module makes dynamic adjustments through formulas. For example, assuming the collected balance data is , the data analysis module will use the following formula to calculate the change in the balance state.

[0027] ; Where: represents the average balance state of the user during training; is the average data value for each collection; is the number of times of collected data.

[0028] To more accurately reflect the user's training intensity, the data analysis module will also calculate the standard deviation based on the user's training historical data ; ; Where: Standard deviation, representing the degree of dispersion of data distribution; The th data value; Mean, that is, the average value of all data; is the total number of data points in the dataset; The sum of the squares of the differences between each data point and the mean represents the degree of deviation of the data from the mean.

[0029] These data will serve as the basis for training feedback, and the data collection module provides the basis for training feedback by collecting data in real time. By combining these data, the system can make adjustments according to the user's training progress, making the training intensity more in line with the user's personalized needs.

[0030] The data collection module provides accurate user data for the balance training system and ensures the dynamic adjustment of the training plan. Through seamless connection with the balance meter 2, tDCS device 4 and other modules, the data collection module can provide real-time support for data analysis and training feedback. At the same time, the design and implementation of the data collection module meet the requirements of high-precision and high-frequency data collection, enabling this system to provide personalized and accurate balance training plans to help users improve their balance ability and cognitive function.

[0031] The data analysis module, which is connected to the data acquisition module, is used to process the collected data and generate training feedback; Specifically, in this embodiment, the data analysis module serves as the core processing unit of the system, mainly responsible for real-time processing, analysis, and modeling of the raw data provided by the data acquisition module, and providing accurate basis for the adjustment of the training plan. Its data sources include but are not limited to the motion data provided by the balance instrument 2, the stimulation parameters of the tDCS device 4, and the physiological data generated by the user during the training process. Through the fusion calculation of these multi-dimensional data, the data analysis module can dynamically evaluate the user's training status and transmit the analysis results to the plan management module to achieve real-time optimization of personalized training.

[0032] During the operation of the system, the data analysis module not only quantitatively calculates the user's balance state and motion performance, but also combines the electrical stimulation parameters of the tDCS device 4 to evaluate the cognitive training effect. The data analysis module calculates core indicators such as the user's balance deviation, posture stability, and training load through specific algorithms, so as to achieve accurate training feedback.

[0033] In this embodiment, the core calculation logic of the data analysis module is constructed based on multiple mathematical models, including time series data analysis, pattern recognition, and adaptive adjustment algorithms. Specifically, the system realizes the comprehensive analysis of the user's training data by establishing a balance state model, a motion pattern recognition model, and a cognitive training feedback model.

[0034] In a possible implementation manner, the data analysis module first processes the user's balance data. Assume that the balance data of the user during the training process is , where is the time variable, represents the balance deviation of the user at time . The data analysis module evaluates the overall stability of the user by calculating the mean value and the standard deviation of the balance deviation of the user within a certain time interval; ; ; Where: is the training duration; reflects the overall balance deviation degree of the user; represents the fluctuation range of the balance data.

[0035] Generally, if is lower than the set threshold, it indicates that the user's balance ability is stable, and the system can appropriately increase the training difficulty; if Exceeding the threshold indicates that the user's balance control ability is weak, and the system will reduce the training intensity to avoid training overload.

[0036] As an option, the data analysis module also classifies the user's movement patterns and determines the user's movement trend through pattern recognition algorithms. For example, the dynamic time warping algorithm is used to calculate the matching degree between the user's current movement pattern and the standard pattern.

[0037] ; Where: is the user's current training data sequence; is the standard training trajectory; represents the dynamic matching distance between the two; The length of the data sequence, i.e., and The number of elements in. Assuming two sequences and have the same length, then represents the number of elements in the sequence; and are the th and th elements in the sequences and represents the absolute difference between the corresponding elements and in the sequence. This difference reflects the deviation between the user's movement trajectory and the standard trajectory at time ; Sum the deviations at all time points and obtain the final distance by minimization.

[0038] If is below the set threshold, it is considered that the user's movement pattern meets the expectation, otherwise the system will adjust the training plan to help the user better master the target movement pattern.

[0039] Specifically, in another possible implementation of the data analysis module, the system adjusts the stimulation parameters of the tDCS device 4 in real time to optimize the cognitive training effect. Assuming the stimulation current intensity of the tDCS device 4 is , and the stimulation time is , then the effective action of the cognitive training can be defined as; ; Wherein: is the stimulation efficacy coefficient, obtained by fitting experimental data.

[0040] The system adjusts and according to the user's cognitive training feedback to ensure the optimization of the cognitive training effect. For example, if the reaction time of the user in the cognitive task is lower than the set threshold, it indicates that the training is effective, and the system can appropriately reduce the stimulation intensity; if exceeds the threshold, the system will increase the stimulation intensity to improve the training effect.

[0041] During the data analysis process, since the signals provided by the data acquisition module may have certain noise interference, the data analysis module uses a variety of signal processing methods to improve the accuracy of the data. For example, during the processing of balance data, the system uses the Kalman filtering method to smooth the balance offset data, and its state update equation is:[[]] ; ; Wherein: is the optimal estimate at the current moment; The optimal estimate at the previous moment, representing the estimated value of the system state at time ; is the observed value; is the Kalman gain; is the prior error covariance matrix; is the measurement matrix; is the measurement noise covariance.

[0042] In some embodiments, in order to reduce the influence of data fluctuations in a short period of time on the training feedback, the system uses the weighted moving average method to smooth the data: ; Wherein: is the smoothed value at the th moment; are the original data at the past moments; is the weight factor; is the window length.

[0043] Generally, a larger value can provide a smoother curve, but may reduce the system's response speed to sudden changes in the training state.

[0044] The data analysis module, as an important part of the system of the present invention, is responsible for processing, analyzing training data, and providing key training feedback. Through mathematical modeling, pattern recognition, and signal processing technologies, the data analysis module can ensure the accuracy and adaptability of the training plan, and through cooperation with the plan management module and the tDCS stimulation module, achieve dynamic optimization of user training. The technical implementation methods of this module cover a variety of mathematical calculation methods and data processing technologies, ensuring the stability and reliability of the system.

[0045] The plan management module, which is connected to the data analysis module, is used to store, manage, and execute the training plan; Specifically, in this embodiment, the plan management module, as the core control unit of the training system, is mainly responsible for formulating, adjusting, and storing personalized training plans according to the user training data provided by the data analysis module. Its working mode is based on a dynamic feedback mechanism, which can adjust the difficulty, type, and time parameters of the training tasks in real time during the training process to ensure the accuracy and adaptability of the training plan. The plan management module is closely linked with the data analysis module and obtains real-time training data through the data acquisition module to continuously optimize the training effect during the operation of the system.

[0046] During the formulation process of the training plan, the plan management module first receives the key training parameters calculated by the data analysis module, including balance deviation, movement stability, tDCS stimulation feedback, etc., and combines the user's historical training data to generate a personalized training plan using a series of optimization algorithms. Generally, the plan management module will judge whether the training intensity needs to be adjusted based on the set target threshold, and select a suitable training mode in the plan library to ensure that the user's training progress meets the set requirements. In this embodiment.

[0047] In a possible implementation manner, the plan management module calculates the effectiveness of the current plan based on the training plan evaluation function ; ; where: represents the user's balance stability score; represents the user's training intensity adaptability score; represents the user's cognitive training effect score; is a weight parameter used to balance the influence degree of each training index.

[0048] Generally, if exceeds the set threshold, the scheme management module will increase the training difficulty, for example, increasing the speed of dynamic balance training or increasing the complexity of cognitive training tasks; if is lower than the set threshold, the system will reduce the training intensity to avoid user over-fatigue.

[0049] Specifically, during the adjustment of the balance training scheme, the scheme management module calculates the user's training progress ; ; where: is the balance score of the user in the th training; is the number of training times.

[0050] The scheme management module decides whether to adjust the training scheme according to the change trend. For example, if continues to rise, indicating that the user's balance ability has improved, the system can increase the training difficulty, such as adding more unstable balance tasks; if shows a downward trend, it indicates that the user may be facing a training bottleneck, and the system will appropriately reduce the training intensity or adjust the training mode to optimize the training effect.

[0051] In a possible implementation, the scheme management module also optimizes the cognitive training scheme in combination with the stimulation parameters of the tDCS device 4. Assume that the stimulation parameters of tDCS are (current intensity), (stimulation time), and the scheme management module calculates the adaptive stimulation parameters through the following function.

[0052] ; ; where; and are the adjusted stimulation parameters; and are the adjustment coefficients; is the change amount of the user's reaction time in cognitive training.

[0053] When When it decreases, it indicates that the user's cognitive training effect is good, and the system appropriately reduces the stimulation intensity; when it increases, the system increases the stimulation intensity to ensure the continuous optimization of cognitive training.

[0054] The program management module needs to store the training program for subsequent call and optimization. In terms of data storage, this embodiment adopts a database management method to store the training program, training records, and program adjustment history of each user. The data format stored in the database is as follows: User ID: The number that uniquely identifies the user; Training type: The category of the current training task; Training parameters: The main parameters of the current training program; Training results: The training effects evaluated by the system, including balance scores and cognitive training scores; Adjustment records: The historical records of each training program adjustment.

[0055] As an option, the program management module also provides a personalized training program recommendation function. The system can predict the best training program based on the user's training data through machine learning methods. For example, the system can use the support vector machine classification algorithm to classify the applicability of different training programs according to the user's training performance: ; Among them: is the user's training data; is the classification weight; is the bias term; The weight vector transpose. It will be converted from a column vector to a row vector so that it can perform a dot product operation with the input feature vector .

[0056] Generally, the system will adjust the and values according to the training data of different users to provide the optimal training recommendation program.

[0057] The program management module undertakes the core training optimization task in the system of the present invention. Based on the user training data provided by the data analysis module, it dynamically adjusts the training program through mathematical modeling, optimization algorithms, and database storage technologies to ensure the personalization and adaptability of the training process. The implementation methods of the module include training evaluation based on mathematical functions, program storage based on databases, and program recommendation functions based on machine learning, which can effectively improve the intelligence level of the training system.

[0058] A balance and cognitive dual-functional training module, which is connected to the program management module and is also connected to a balance meter 2 and a display screen 5 via Bluetooth, and is used to receive training programs for limb training and cognitive training; Specifically, in this embodiment, the balance and cognitive dual-functional training module is used to synchronously improve the user's dynamic balance ability and cognitive reaction ability, and through evaluation, training and dynamic feedback regulation, a personalized training program is realized. This module relies on the training status data provided by the data analysis module and combines the personalized training program set by the program management module to optimize the training content in real time. The module mainly consists of a balance and cognitive evaluation unit, a balance training unit, and a cognitive training unit, and each unit works together to ensure the pertinence and effectiveness of the training.

[0059] Generally, traditional training methods mainly focus on single-functional training, while in this embodiment, through a multi-dimensional training method, the combination of balance control and cognitive function training is adopted to better meet the needs of neurorehabilitation and motor control. As an option, devices such as an inertial measurement unit, a force platform, and an electroencephalogram acquisition device can be used during the training process to real-time monitor the user's movement state and cognitive reaction situation, and optimize the training strategy based on the measured data.

[0060] In this embodiment, the balance and cognitive evaluation unit is used to evaluate the user's basic balance ability and cognitive function, and the evaluation results can be used for the formulation and adjustment of personalized training programs.

[0061] Specifically, this unit uses a posture evaluation algorithm to calculate the user's stability score: ; Where: is the balance stability score; is the center of gravity offset of the user in the th evaluation; is the weight parameter; is the total number of evaluation times, indicating the total number of evaluations the user has carried out.

[0062] Generally, if is lower than the set threshold, it indicates that the user's balance ability is weak, and the training program needs to reduce the difficulty, such as reducing dynamic disturbances or providing visual support.

[0063] In terms of cognitive evaluation, the system uses reaction time and correct rate to calculate the user's cognitive task completion situation: ; Where: is the cognitive function score; is the reaction time for the th task; is the correct rate; is the standard task time; is the total number of tasks. It indicates how many times the task tests have been carried out in total.

[0064] Generally, if continues to be lower than the set threshold, it indicates that the user's cognitive flexibility is weak, and the system can reduce the complexity of the cognitive task or extend the reaction time.

[0065] In this embodiment, the balance training unit is used to perform personalized balance training, including a single-leg balance training unit, a dynamic balance training unit, and a maze training unit, and trains according to different motion control requirements.

[0066] Single-leg balance training unit: It trains by detecting the stability of the user standing on one leg, and is mainly used to improve static balance ability. The system measures the center-of-gravity offset of the user through a force platform or an IMU device and calculates the stability index.

[0067] In a possible implementation, the training task is adjusted based on the center-of-gravity swing trajectory of the user: ; where: is the center-of-gravity stability index; is the center-of-gravity coordinate of the user at the th frame; is the initial balance position; The total number of measurements (or frames). It indicates the number of times the center-of-gravity position is recorded during the entire training process.

[0068] If exceeds the threshold, the system increases the training time or reduces the training difficulty.

[0069] Dynamic balance training unit: It is used to train the balance ability of the user in a dynamic environment, simulates gait adjustment, obstacle avoidance training, etc., and improves motion coordination.

[0070] As an option, this training can use a variable-speed platform, and the user needs to maintain a stable posture in different speed environments. Generally, the system controls the training by the user's gait adjustment speed as follows: ; Wherein: is the gait adjustment distance; is the adjustment time.

[0071] When is lower than the preset threshold, the system increases the training difficulty, such as adding random perturbations.

[0072] Maze training unit: By simulating the maze path, guiding the user to complete specific balance tasks, mainly training the direction control and gait adjustment capabilities.

[0073] This training task calculates the user's optimal travel route based on the path optimization algorithm, and uses the cost function to optimize the gait trajectory: ; Wherein: is the optimal path; is the gait deviation distance; is the task completion time; is the adjustment weight; The total number of steps of the path.

[0074] In this embodiment, the cognitive training unit is used to provide different types of cognitive training, including a refresh training unit, a visual template training unit, a phonological loop training unit, and a cognitive flexibility training unit, to improve the user's cognitive adaptation ability.

[0075] Refresh training unit: Train the user's short-term memory ability through picture or character tasks, and adopt a dynamic task update mechanism to improve the cognitive load control ability.

[0076] Visual template training unit: Improve the user's visual cognitive ability through pattern recognition tasks, and the system adjusts the task difficulty according to the image complexity, such as adding interference items to improve the recognition ability.

[0077] Phonological loop training unit: Enhance the executive function and language processing ability through voice tasks. The user needs to complete voice recognition tasks in different background noise environments to improve language understanding and memory ability.

[0078] Cognitive flexibility training unit: Improve the user's cognitive adaptation ability through task switching training, and adopt a multi-task interference mechanism to evaluate the user's task switching ability.

[0079] In a possible implementation manner, the system calculates the training task adaptability using the cognitive load index: ; Wherein: The cognitive load index, which is used to measure the cognitive load when performing tasks; The reaction time of the The accuracy value range of the nth task is generally between [0, 1], indicating whether the task is completed correctly;

[0080] The balanced cognitive dual-functional training module provided in this embodiment realizes a personalized training plan through evaluation, training, and dynamic feedback regulation. The module consists of multiple training units, including static and dynamic balance training and different types of cognitive training tasks, to ensure that users can obtain comprehensive training effects. The system ensures the adaptability and challenge of training tasks through real-time data analysis and regulation, and combines multi-modal perception technology to improve the accuracy and personalization level of training.

[0081] The tDCS stimulation module, which is connected to the program management module and is also connected to the tDCS device 4 via Bluetooth, is used to receive the training program to perform transcranial direct current stimulation on the user.

[0082] Specifically, in this embodiment, the tDCS stimulation module is used to provide adaptive neuromodulation during the balanced cognitive dual-functional training process to optimize the neuroplasticity of cognitive training and motor control. This module works in coordination with the program management module and dynamically adjusts the stimulation parameters according to the training status information provided by the data analysis module to ensure the matching of the stimulation intensity and training tasks. The tDCS stimulation module can optimize the current intensity, stimulation time, and electrode position according to the user's training performance to improve the training effect.

[0083] In this embodiment, the tDCS stimulation module consists of a charging module and an electrical stimulation module, and each unit works together to ensure the stability and adaptive regulation ability of the stimulation.

[0084] Charging module In this embodiment, the charging module is used to provide a stable power supply for the electrical stimulation module to ensure that the tDCS device 4 can maintain accurate current output during training.

[0085] Generally, tDCS stimulation requires precise control of the current intensity to avoid affecting the neuromodulation effect due to current fluctuations. Therefore, the charging module adopts a high-precision DC power management system to ensure the stability of the current output.

[0086] Electrical Stimulation Module In this embodiment, the electrical stimulation module is used to provide current stimulation in different modes to affect the neural activities in specific brain regions.

[0087] Specifically, the electrical stimulation module can provide different modes of neuromodulation schemes such as direct current stimulation, alternating current stimulation, and random noise stimulation based on the requirements of the training task to adapt to different types of neural training needs.

[0088] In one possible implementation, the selection of the electrical stimulation mode is based on the neural excitability index Calculation: ; Where: represents the optimal electrical stimulation mode; is the cortical excitability index; is the training task intensity; is the adjustment weight.

[0089] Generally, if is relatively low, the system selects the direct current stimulation (tDCS) mode to enhance neural plasticity; if is in a high-load state, random noise stimulation (tRNS) is adopted to improve neural adaptability.

[0090] As an option, the electrical stimulation module can also adjust the electrode configuration to optimize the current distribution. Specifically, the system predicts the current distribution in the cortex based on finite element current simulation calculations and optimizes the electrode placement positions accordingly: ; Where; is the potential difference between electrodes; is the current flowing into the electrode; is the cortical resistance; The number of layers through which the current flows, indicating the number of different levels through which the current passes.

[0091] Generally, when is too large, the system can adjust the electrode spacing or change the polarity to optimize the current distribution.

[0092] The tDCS stimulation module provided in this embodiment realizes stable neuromodulation output through a charging module and an electrical stimulation module. The system dynamically optimizes the stimulation parameters of tDCS based on the user's balance ability, cognitive adaptability, and neural state to ensure the matching of the stimulation intensity with the training task.

[0093] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A balance training device combining brain function training and limb training, characterized in that: The invention comprises a support base (1), one side of the support base (1) is fixedly connected to a balance instrument (2), a signal acquisition module is installed inside the balance instrument (2), the other side of the support base (1) is fixedly connected to a support frame (3), the inner wall of the support frame (3) is fixedly connected to a support platform (6), a tDCS device (4) is arranged on the upper surface of the support platform (6), a charging module and an electrical stimulation module are installed inside the tDCS device (4), and a display screen (5) is arranged on one side of the support frame (3).

2. A balance training system combining brain function training and limb training, according to the balance training device combining brain function training and limb training as claimed in claim 1, characterized in that: include; A data collection module, which is connected to the balance instrument (2) and the tDCS device (4) via Bluetooth and is used to collect the user's movement data, balance state data and cognitive training related data; A data analysis module, which is connected to the data acquisition module and is used to process the collected data and generate training feedback; A program management module, which is connected to the data analysis module and is used to store, manage and execute training programs; A balance and cognition dual-function training module, which is connected to the program management module and is also connected to the balance instrument (2) and the display screen (5) via Bluetooth, and is used to receive training programs for limb training and cognitive training; The tDCS stimulation module is connected to the program management module and is also connected to the tDCS device (4) via Bluetooth, and is used to receive the training program to perform transcranial direct current stimulation on the user.

3. A balance training system combining brain function training and limb training according to claim 2, characterized in that: The scheme management module includes: A balance and cognitive training management unit, used to coordinate and execute different balance and cognitive training programs; tDCS electrical stimulation management unit, used to control the current intensity, time and stimulation mode of tDCS stimulation; Bluetooth management unit for wirelessly transmitting data to external devices; User management unit, used to store user information and personalized training parameters; Record management unit, used to store training history data and training effect evaluation; The user interaction unit is connected to the display screen (5) and supports visual display of user operations and training feedback.

4. A balance training system combining brain function training and limb training according to claim 2, characterized in that: The balance-cognition dual-function training module includes: Balance cognition assessment unit, used to assess the user's basic balance ability and cognitive function; A balance training unit for conducting a balance training program; A cognitive training unit is used to conduct cognitive training programs.

5. A balance training system combining brain function training and limb training according to claim 2, characterized in that: The tDCS stimulation module includes: A charging module, used to provide a stable power supply to the electrical stimulation module; The electrical stimulation module can provide different modes of electrical stimulation to affect the neural activity of specific brain areas.

6. A balance training system combining brain function training and limb training according to claim 2, characterized in that: The balance training unit comprises: The single-leg balance training unit trains users by detecting their stability when standing on one leg; Dynamic balance training unit, used to train the user's balance ability in a dynamic environment; The maze training unit guides users to complete specific balance tasks by simulating maze paths.

7. A balance training system combining brain function training and limb training according to claim 2, characterized in that: The cognitive training unit comprises: Refresh training unit, training users' short-term memory ability through picture or character tasks; Visual template training unit, which improves users' visual cognition ability through pattern recognition tasks; a phonological loop training unit that enhances executive function and language processing through speech tasks; The cognitive flexibility training unit improves users' cognitive adaptability through task switching training.

8. A balance training system combining brain function training and limb training according to claim 2, characterized in that: The data analysis module adopts an adaptive threshold calculation method to dynamically adjust the training difficulty according to the user's training data, and determines the training feedback threshold based on the following calculation formula; ； in: is the mean of the user’s historical training data; is the standard deviation of the data; and is the adjustment coefficient, which is used to optimize the training intensity matching.

9. A balance training system combining brain function training and limb training according to claim 2, characterized in that: The tDCS electrical stimulation management unit can dynamically adjust the stimulation parameters according to the user's training status, including stimulation current intensity, electrode polarity and stimulation duration. Calculated according to the following formula: ； in: is the applied voltage; is the electrode contact impedance.

10. A balance training system combining brain function training and limb training according to claim 2, characterized in that: The user interaction unit includes a remote data interaction module, which is used to upload the user's training data to the cloud server and support remote expert intervention and personalized training program adjustment.

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