System for language training of aphasia patients

Abstract

The invention provides a system for language training of aphasia patients, and relates to the technical field of rehabilitation training, and the system comprises a user side device which is used for receiving the voice input of a patient, displaying the training content and providing real-time feedback; the cloud server is used for storing personal information, training data and a training plan of the patient and providing functions of voice recognition, intelligent analysis and training plan generation; the remote monitoring platform is used for remotely monitoring the training condition of the patient by a therapist, adjusting a training plan and carrying out necessary remote guidance; wherein the cloud server automatically generates a personalized training plan according to an evaluation result of the patient, the personalized training plan is displayed to the patient through the user side equipment, and the patient performs self-service language rehabilitation training according to the training plan and receives real-time feedback from the cloud server at the same time. By applying the system provided by the invention, external pressure brought to the patient by an existing training mode can be reduced, and effective development of language training is facilitated.

Description

Technical Field

[0001] The present application relates to the technical field of rehabilitation training, and in particular to a system for language training of aphasia patients. Background Art

[0002] Aphasia refers to lesions of the brain tissue related to language function, such as stroke, brain trauma, brain tumor, brain inflammation, etc., which cause damage to the patient's ability to understand and express the human communication symbol system, especially the understanding and expression of components such as phonetics, vocabulary, grammar, language structure, and language content and meaning, as well as the decline and functional damage of the language cognitive process that is the basis of language.

[0003] For aphasia, rehabilitation treatment can generally be carried out through language training, surgical treatment and other methods. In existing practice, in the language training rehabilitation treatment method, therapists are usually required to implement relevant treatment plans face to face with target patients. In the process of realizing the technical solution of this application, the inventor found that some aphasia patients, such as some middle-aged and elderly stroke patients, often have resistance to language training when facing strangers such as therapists due to individual psychological factors during the rehabilitation treatment of aphasia symptoms, which leads to the inability to carry out relevant treatment well and affects the patient's recovery. In view of this situation in reality, in order to facilitate the implementation of language training rehabilitation treatment for such aphasia patients, it is obviously necessary to have a technical solution that can enable users to carry out effective rehabilitation training alone, so as to facilitate the effective implementation of relevant language rehabilitation training. Summary of the invention

[0004] In order to overcome the problems existing in the related art to at least a certain extent, the embodiment of the present application provides a system for language training of aphasia patients, which can carry out rehabilitation training based on a remote self-service manner, so that patients can independently carry out language rehabilitation training without external pressure, thereby effectively carrying out language training.

[0005] The present application embodiment provides a system for language training of aphasia patients, the system comprising: A user-end device for receiving voice input from the patient, displaying training content, and providing real-time feedback; Cloud server, used to store patients’ personal information, training data, training plans, and provide voice recognition, intelligent analysis, and training plan generation functions; Remote monitoring platform, used by therapists to remotely monitor patients’ training status, adjust training plans, and provide necessary remote guidance; The user-end device and the remote monitoring platform are connected to the cloud server through a network. The cloud server automatically generates a personalized training plan based on the patient's evaluation results and displays it to the patient through the user-end device. The patient performs self-service language rehabilitation training according to the training plan and receives real-time feedback from the cloud server.

[0006] In some possible implementations, the process of generating the personalized training plan includes: Analyze the patient's language ability based on the patient's initial assessment results using an artificial intelligence algorithm; Based on the analysis results, combined with the preset training templates and difficulty gradients, a training plan containing multiple training modules is automatically generated; Allows therapists to manually adjust and optimize training plans through the remote monitoring platform.

[0007] In some possible implementations, the process of implementing the real-time feedback includes: Facilitating the user terminal device to capture the patient's voice input; Through the speech recognition technology of the cloud server, the voice input is converted into text or instructions; Based on intelligent analysis algorithms, the patient's speech characteristics are evaluated, including pronunciation accuracy, speaking speed, and intonation; The evaluation results are fed back to the user-end device in real time, so that the user-end device can display them to the patient in a preset form.

[0008] In some possible implementations, the preset format is a display output format of a graph or a table.

[0009] In some possible implementations, the process of presenting the patient in a preset form includes: Based on real speech synthesis technology, the assessment results are presented to patients in an auditory manner.

[0010] Some possible implementations also include an incentive support mechanism, which includes: Display encouraging words, achievement badges, or progress bars on the user’s device; Set up a reward mechanism to unlock new training modules or receive virtual rewards after completing specific training tasks.

[0011] In some possible implementations, the implementation process of the incentive support mechanism includes: Acquire facial image data of patients during training; Based on the facial image data, a micro-expression recognition algorithm is used to classify and identify the patient's emotional state; When the recognition result is a preset classification, the corresponding motivational words are displayed to the patient through the user terminal device.

[0012] In some possible implementations, the cloud server is also configured with data encryption and permission control functions to ensure the security and privacy of the patient's personal information and training data.

[0013] In some possible implementations, the user terminal device is configured with at least one microphone, one speaker, one display screen, one camera device, and one network communication module.

[0014] In some possible implementations, the user terminal device is implemented based on a smart mobile terminal.

[0015] The system for language training of aphasia patients provided in the embodiment of the present application includes: a user-end device, which is used to receive the patient's voice input, display the training content and provide real-time feedback; a cloud server, which is used to store the patient's personal information, training data, and training plan, and provide voice recognition, intelligent analysis and training plan generation functions; a remote monitoring platform, which is used for therapists to remotely monitor the patient's training situation, adjust the training plan, and provide necessary remote guidance; wherein the user-end device and the remote monitoring platform are connected to the cloud server through a network, and the cloud server automatically generates a personalized training plan based on the patient's evaluation results, and displays it to the patient through the user-end device, and the patient performs self-service language rehabilitation training according to the training plan, and receives real-time feedback from the cloud server at the same time.

[0016] In actual scenarios, the application of the system using the above-mentioned specific configuration in this application can enable patients to conduct language rehabilitation training independently in a private environment (such as at home), which can effectively reduce the external pressure brought to patients by existing training methods, thereby facilitating the effective implementation of language training; the system realizes the generation of personalized training plans based on the actual situation and needs of the patients, and can provide real-time voice feedback and evaluation during the training process, further ensuring the effectiveness of language training.

[0017] Other advantages, objectives, and features of the present application will be described in part in the following description, and in part will be apparent to those skilled in the art based on the following examination and study, or may be taught from the practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the technical solution of the present application or the prior art, and constitute a part of the specification. Among them, the accompanying drawings expressing the embodiments of the present application are used together with the embodiments of the present application to explain the technical solution of the present application, but do not constitute a limitation on the technical solution of the present application.

[0019] Figure 1 A schematic block diagram of a system for language training for aphasia patients provided in one embodiment of the present application; Figure 2 This is a schematic diagram illustrating the process of implementing micro-expression recognition in some embodiments of the system for language training of aphasia patients in the present application; Figure 3 This is a schematic diagram illustrating the process of facial image data acquisition in some embodiments of the system for language training of aphasia patients of the present application; Figure 4 This is a schematic diagram illustrating the process of using the system to perform aphasia language training in one embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other implementation methods obtained by ordinary technicians in the field without making creative work belong to the scope of protection of the present application.

[0021] As described in the background technology, aphasia refers to lesions of brain tissue related to language function, such as stroke, brain trauma, brain tumor, brain inflammation, etc., which cause damage to the patient's understanding and expression of the human communication symbol system, especially the understanding and expression of components such as phonetics, vocabulary, grammar, language structure, and language content and meaning, as well as the decline and functional damage of the language cognitive process that is the basis of language.

[0022] For aphasia, rehabilitation treatment can generally be carried out through language training, surgical treatment and other methods. In existing practice, in the language training rehabilitation treatment method, therapists are usually required to implement relevant treatment plans face to face with target patients. In the process of realizing the technical solution of this application, the inventor found that some aphasia patients, such as some middle-aged and elderly stroke patients, often have resistance to language training when facing strangers such as therapists due to individual psychological factors during the rehabilitation treatment of aphasia symptoms, which leads to the inability to carry out relevant treatment well and affects the patient's recovery. In view of this situation in reality, in order to facilitate the implementation of language training rehabilitation treatment for such aphasia patients, it is obviously necessary to have a technical solution that can enable users to carry out effective rehabilitation training alone, so as to facilitate the effective implementation of relevant language rehabilitation training.

[0023] Based on this, the present application proposes a system for language training of aphasia patients, which can carry out rehabilitation training in a remote self-service manner.

[0024] like Figure 1As shown, in one embodiment, the system for language training of aphasia patients proposed in the present application includes: The user terminal device 10 is used to receive the patient's voice input, display the training content and provide real-time feedback; It is easy to understand that the user terminal device here should be equipped with at least a microphone, a display screen, a speaker, and a network communication module to achieve communication connection with other components of the system; The cloud server 20 is used to store the patient's personal information, training data, and training plans, and to provide functions of speech recognition, intelligent analysis, and training plan generation. For example, the cloud server here can be implemented by using relevant cloud services provided by existing mainstream cloud computing vendors, such as Alibaba Cloud; A remote monitoring platform 30, used for therapists to remotely monitor the patient's training status, adjust the training plan, and provide necessary remote guidance; It is easy to understand that the remote monitoring platform here can be deployed in medical rehabilitation institutions to facilitate professional therapist users to perform the above-mentioned related operations.

[0025] In this system, Figure 1 As shown, the user terminal device 10 and the remote monitoring platform 30 are connected to the cloud server 20 through the network. The cloud server 20 can automatically generate a personalized training plan based on the patient's evaluation results, and display it to the patient through the user terminal device 10. The patient performs self-service language rehabilitation training according to the training plan and receives real-time feedback from the cloud server 20.

[0026] In actual scenarios, the application of the system using the above-mentioned specific configuration in this application can enable patients to conduct language rehabilitation training independently in a private environment, such as conducting the training at the patient's home. This can effectively reduce the external pressure brought to patients by existing training methods, thereby contributing to the effective implementation of language training; and the system can generate personalized training plans based on the patient's actual situation and needs, and can provide real-time voice feedback and evaluation during the training process, further ensuring the effectiveness of language training.

[0027] To facilitate understanding of the technical solution of the present application, the technical solution of the present application is introduced and explained with reference to another embodiment below.

[0028] In this embodiment, the system for language training of aphasia patients proposed in this application includes: A user-end device for receiving voice input from the patient, displaying training content, and providing real-time feedback; Cloud server, used to store patients’ personal information, training data, training plans, and provide voice recognition, intelligent analysis, and training plan generation functions; Remote monitoring platform, used by therapists to remotely monitor patients’ training status, adjust training plans, and provide necessary remote guidance; In this system, the user-side device and remote monitoring platform are connected to the cloud server through the network. The cloud server can automatically generate a personalized training plan based on the patient's evaluation results and display it to the patient through the user-side device. The patient performs self-service language rehabilitation training according to the training plan and receives real-time feedback from the cloud server.

[0029] Specifically, in this embodiment, in the implementation of the training plan generation function of the cloud server, in the process of generating a personalized training plan, the following steps are included: Analyze the patient's language ability based on the patient's initial assessment results using an artificial intelligence algorithm; Regarding the initial assessment results, it should be noted that in actual implementation, based on the needs of specific scenarios, the acquisition of the initial assessment results can be completed online or offline, for example, the initial assessment results can be obtained offline through relevant evaluation scales, where the scales include the "Chinese Standard Aphasia Checklist", etc., for example, through a specifically set up online assessment system to complete the relevant assessment, such as the initial assessment function unit can be integrated and arranged in the system of the present application to obtain the initial assessment results, and then combined with other personal information of the patient using an artificial intelligence algorithm based on big data to analyze the patient's language ability; After analyzing the patient's language ability, a training plan containing multiple training modules (also called training projects) can be automatically generated based on the analysis results, combined with the preset training template and difficulty gradient; In the actual implementation of the plan, based on scenario requirements, the content of the training plan may include pronunciation training, vocabulary expansion, sentence construction, dialogue simulation and other aspects, with gradually increasing difficulty. Furthermore, in some scenarios, large language models such as ChatGPT can be introduced in dialogue simulation to increase the authenticity and effectiveness of the training. In actual scenarios, this automatically generated training plan may not fully meet the needs of specific individual patients. Therefore, after obtaining the training plan, based on actual needs, therapists are allowed to manually adjust and optimize the training plan through the remote monitoring platform to flexibly respond to actual specific situations and meet the further needs of individual patients, such as adjusting the proportion of each training module in the overall plan, increasing or decreasing training items, and replacing similar items with different difficulty levels.

[0030] On the other hand, from the patient's perspective, in some specific implementation scenarios, the user-end device can provide an intuitive and easy-to-use self-service training interface. Patients can choose training modules according to their own needs and freely adjust the training time and progress. It is easy to understand that the actual interface design should focus on user experience and reduce operational complexity to help increase patient participation. For example, multiple sets of different styles of interfaces can be built-in for users to choose from, or automatically switched according to the implementation of different training modules.

[0031] In this embodiment, during the training process, the process of implementing real-time feedback includes: Using a microphone configured on the user terminal device to capture the patient's voice input; Through the speech recognition technology of the cloud server, the voice input is converted into text or instructions; Based on the intelligent analysis algorithm, the patient's pronunciation accuracy, speaking speed, intonation and other speech characteristics are evaluated. The intelligent analysis algorithm here can be implemented based on the relevant algorithm of deep learning. The specific method and principle of its evaluation implementation can be found in the public information of the relevant technology, and this application will not be repeated here; The evaluation results are fed back to the user-end device in real time. For example, the evaluation results include evaluations and suggestions on pronunciation accuracy, speaking speed, intonation, etc. The user-end device displays them to the patient in a preset form, such as in the form of a graph or table on a display screen.

[0032] Furthermore, in order to further improve the user experience, in some implementation scenarios, the evaluation results can be presented to the patient in an auditory manner based on real speech synthesis technology, such as using the timbre characteristics of a person familiar to the patient as the synthesis basis and outputting speech through a speaker for relevant presentation, so as to help reduce the external pressure that the patient may feel; It is easy for those skilled in the art to understand that real speech synthesis technology is a technology that can automatically convert text into speech that sounds like real people speaking. It uses computer algorithms and sound processing technology to convert input text information into coherent and fluent speech output. Generally speaking, real speech synthesis technology usually includes the following steps: Text parsing: parsing text symbols to understand the vocabulary, grammar and semantic information therein, which is the basis for ensuring that the system can accurately understand and express the text content; speech generation: generating corresponding speech signals based on the parsed text information, which usually involves selecting or generating phonemes that match the text content and combining them into a continuous speech stream. Specifically in the application scenario of this application, the phonemes here correspond to the timbre feature information that needs to be pre-configured for the specific patient's condition; sound processing: the generated speech signal also needs to go through a series of sound processing steps to make it sound more natural and smooth, such as adjusting the pitch, timbre, volume, etc., and adding appropriate intonation, pauses and stress, etc., to simulate human speech characteristics; output: the processed speech signal will be output as an audible audio file or a real-time voice stream; Further implementation principles of real speech synthesis technology can be found in relevant public technical materials, and this application will not elaborate on this.

[0033] In this embodiment, in order to further ensure the application effect of the system, the system is also configured with an incentive support mechanism. In actual implementation, this incentive support mechanism can be implemented through relevant software configuration on the cloud server and / or the user terminal device; As a specific implementation method, the incentive support mechanism includes: Display encouraging words, achievement badges, or progress bars on the user’s device; Set up a reward mechanism to unlock new training modules or receive virtual rewards after completing specific training tasks.

[0034] It should be noted here that in the rehabilitation treatment of aphasia, whether language training is carried out effectively and smoothly is an important factor affecting the rehabilitation effect; based on this, considering the implementation method of the technical solution of this application, the implementation principle similar to that of multimedia teaching courseware can be adopted, based on the recording of process events corresponding to the training tasks, and using technical means such as script writing and UI design to implement the incentive support mechanism.

[0035] Furthermore, as described in the background technology, the technical solution of the present application is proposed out of consideration for individuals conducting rehabilitation training alone, which is conducive to eliminating the situation where individuals may have resistance to language training when facing strangers such as therapists, so as to ensure the effective implementation of rehabilitation training; but on the other hand, in the scenario of the present application, the encouragement and guidance of patients by highly qualified therapists at the right time during the rehabilitation training process is also a kind of incentive support from the perspective of purpose, and is also a positive factor for the effective implementation of rehabilitation training. The implementation of the technical solution in the previous embodiment, on the contrary, weakens this positive factor.

[0036] Therefore, based on the considerations and analysis in the above invention creation process, considering that the user portrait characteristics of the user in this application are relatively introverted and the external expression is not prominent, in order to solve this new problem, on the basis of the above embodiment, this application specifically proposes the following technical improvements; Specifically, in response to the newly emerged problem, in order to further improve the effectiveness of the incentive support mechanism in the application scenario of this application, as a preferred implementation method, in the technical solution of this application, the user terminal device is also equipped with a camera device. Combined with the above description of the user terminal device, the user terminal device in this application is equipped with at least one microphone, one speaker, one display screen, one camera device and one network communication module, and it should be noted that the performance of the camera device here should meet certain requirements to meet the image data collection requirements of subsequent micro-expression recognition; From the perspective of easy implementation, it is obvious that the user-end device here can be implemented based on a smart mobile terminal, such as an existing tablet computer, smart screen product, and a high-frame rate video camera device.

[0037] like Figure 2 and Figure 3 As shown, in this implementation, the implementation process of the incentive support mechanism includes: a. Obtain the patient's facial image data during the training process through a camera device.

[0038] Specifically, in the actual implementation process, the image data that can be directly obtained by the camera device is the frame image data within the field of view of the device. In this application, such frame image data is referred to as initial image data. Here, the process of obtaining the facial image data of the patient in the training process by the camera device includes: a-1. Monitor the implementation process of the training plan; a-2. In response to the execution of the preset plan node, face region segmentation processing is performed on the frame images of the preset number of frames before and after the moment in the acquired initial image data; For example, the face detector in OpenCV can be used here to implement face region segmentation processing, and it is easy to understand that in this process, the preset plan node refers to the training event used to characterize the training process, and the required frame image data is accurately obtained in response to the occurrence of the corresponding event, which can reduce the amount of data for related calculation processing, so as to facilitate the timeliness and accuracy of subsequent related recognition processing; for example, in actual implementation, the preset plan node here includes the node corresponding to the training project whose project difficulty attribute value in the training plan is greater than the preset threshold; Furthermore, the preset threshold here is determined based on the initial evaluation result. For example, the threshold used can be automatically determined based on the correspondence table between the severity level information of the patient's condition in the preset initial evaluation result and the threshold, so as to achieve targeted treatment for different patients, which is conducive to improving the accuracy of micro-expression capture and balancing the calculation and processing efficiency; Continue as Figure 3 As shown, step a-3 is then performed to pre-process the image data obtained by the segmentation process. For example, the pre-processing here may include alignment, standardization, etc., and the pre-processed data is used as the facial image data to be subsequently identified.

[0039] In addition, regarding the preset frame number in step a-2, it is necessary to consider the duration range of micro-expressions and select settings based on the actual performance configuration of the camera equipment so that it can capture effective facial changes. For example, in related research, the duration range of micro-expressions is generally 1 / 25 to 1 / 5 s, so the duration corresponding to the preset frame number here must be at least greater than 1 / 5 s. For example, in some embodiments, the performance configuration of the camera equipment is 250fps, and the corresponding preset frame number is 60 frames.

[0040] like Figure 2 As shown, after step a, step b is performed, and the patient's emotional state is classified and identified using a micro-expression recognition algorithm for the obtained facial image data; Specifically, in combination with the above, the implementation process of step b includes: For each frame of the facial image data, an optical flow method is used to analyze and process the pixel changes between frames. For example, the Lucas-Kanade algorithm can be used to calculate the optical flow field, so as to extract motion feature information representing facial changes. The motion feature information here includes spatial change information and temporal change information of pixel points. Input the above motion feature information into a pre-built and trained deep learning model to classify and identify the emotional state to obtain the corresponding recognition result. For example, a model required for training can be built based on a recurrent neural network; It can be understood by those skilled in the art that the optical flow method is an important technology in the field of computer vision. Optical flow refers to the motion pattern formed over time by the movement of pixels or feature points in an image. Its related algorithms are relatively mature and reliable. The principle of this method is applied in this application to realize the extraction of motion feature information of facial changes. Considering the high complexity of the solution of this method, this application adopts the corresponding settings for accurately obtaining and processing source data in combination with the characteristics of this scene, such as the settings described in steps a-1 to a-3 above, to ensure the feasibility of the overall technical solution.

[0041] After step b, proceed to step c to monitor and judge the recognition result. When the recognition result is a preset classification, the corresponding motivational words are displayed to the patient through the user-end device. For example, the preset classification here is the expression that represents the user's discouraged situation. The expression generally includes drooping eyes, drooping corners of the mouth, frowning, etc. In practice, when the recognition result is this situation, the corresponding words "Don't be discouraged, you have done a good job" can be displayed to the user.

[0042] In addition, it should be noted that, based on the above embodiments, in some embodiments, for data security considerations, the cloud server is also configured with data encryption and permission control functions to ensure the security and privacy of the patient's personal information and training data; It should be noted here that in the technical solution of this application, a camera and voice pickup device are set up on the user side to obtain individual feature information such as the appearance of the relevant individual, which involves personal privacy and data security issues, such as data storage and data transmission links; in order to meet the specific market needs of the product, such as the requirements of the General Data Protection Regulation in the EU region, it is necessary to use technical means such as data encryption and permission control to perform relevant configuration processing for data protection based on the data security regulations of the relevant regions; In actual implementation, based on the system architecture category characteristics of the technical solution of this application, the entire architecture of this application is a C / S architecture, and the configuration processing method of related software system products with similar architectures can be used to implement data security configuration. Taking into account the differences between different market segments, this application will not list and explain the specific regulatory requirements and the corresponding detailed configuration processing methods.

[0043] On the other hand, regarding the remote monitoring platform, based on the specific software function settings, therapists can view the patient's training status through the remote monitoring platform, such as training video playback, data reports, etc.; and when necessary, they can provide remote guidance through the video call function, answer patients' questions, adjust training plans, etc.

[0044] Based on the description of the above embodiments, compared with the related products in the prior art, the system for language training of aphasia patients proposed in this application can achieve the following technical effects: 1) Patients do not need to go to hospitals or clinics, and can conduct rehabilitation training independently in a stress-free environment such as at home; 2) Generate personalized training plans based on the actual situation and needs of the patient, and ensure the visualization and immediacy of the training effect based on real-time voice feedback and evaluation; 3) Providing an incentive support mechanism to enhance patients' training motivation and self-confidence; and combined with the recognition and application of micro-expressions, while achieving the beneficial effects of 1) above, it is also beneficial to maintain the positive effects of incentive support achieved in the original way; and considering the scarcity of highly qualified therapists, to some extent, the application of this technical solution in this application even enhances the positive effects of this dimension, thereby achieving the ultimate effect of ensuring the effective implementation of rehabilitation training.

[0045] like Figure 4 FIG. 1 is a flow chart of a method for performing aphasia language training using the system in the above embodiment of the present application in one embodiment, which includes the following steps: Step S110, the patient performs an initial assessment through a user terminal device; Step S120, the cloud server generates a personalized training plan based on the initial evaluation results; Step S130, the patient performs self-help language rehabilitation training on the user terminal device according to the training plan; Step S140, the cloud server provides real-time voice feedback and evaluation results; Step S150: The therapist monitors the training status through the remote monitoring platform and provides remote guidance when necessary.

[0046] The specific implementation method of the system background in this embodiment has been described in detail in the previous embodiments of the system, so it will not be elaborated here.

[0047] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by any person familiar with the technology within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A system for language training of aphasia patients, characterized in that: include: A user-end device for receiving voice input from the patient, displaying training content, and providing real-time feedback; Cloud server, used to store patients’ personal information, training data, training plans, and provide voice recognition, intelligent analysis, and training plan generation functions; Remote monitoring platform, used by therapists to remotely monitor patients’ training status, adjust training plans, and provide necessary remote guidance; The user-end device and the remote monitoring platform are connected to the cloud server through a network. The cloud server automatically generates a personalized training plan based on the patient's evaluation results and displays it to the patient through the user-end device. The patient performs self-service language rehabilitation training according to the training plan and receives real-time feedback from the cloud server.

2. The system for language training of aphasia patients according to claim 1, characterized in that: The process of generating the personalized training plan includes: Analyze the patient's language ability based on the patient's initial assessment results using an artificial intelligence algorithm; Based on the analysis results, combined with the preset training templates and difficulty gradients, a training plan containing multiple training modules is automatically generated; Allows therapists to manually adjust and optimize training plans through the remote monitoring platform.

3. The system for language training of aphasia patients according to claim 1, characterized in that: The implementation process of the real-time feedback includes: Facilitating the user terminal device to capture the patient's voice input; Through the speech recognition technology of the cloud server, the voice input is converted into text or instructions; Based on intelligent analysis algorithms, the patient's speech characteristics are evaluated, including pronunciation accuracy, speaking speed, and intonation; The evaluation results are fed back to the user-end device in real time, so that the user-end device can display them to the patient in a preset form.

4. The system for language training of aphasia patients according to claim 3, characterized in that: The preset format is a display output format of a graph or a table.

5. The system for language training of aphasia patients according to claim 3, characterized in that: The process of presenting the patient in a preset form includes: Based on real speech synthesis technology, the assessment results are presented to patients in an auditory manner.

6. The system for language training of aphasia patients according to claim 1, characterized in that: It also includes incentive support mechanisms, which include: Display encouraging words, achievement badges, or progress bars on the user’s device; Set up a reward mechanism to unlock new training modules or receive virtual rewards after completing specific training tasks.

7. The system for language training of aphasia patients according to claim 6, characterized in that: The implementation of the incentive support mechanism includes: Acquire facial image data of patients during training; Based on the facial image data, a micro-expression recognition algorithm is used to classify and identify the patient's emotional state; When the recognition result is a preset classification, the corresponding motivational words are displayed to the patient through the user terminal device.

8. The system for language training of aphasia patients according to claim 7, characterized in that: The user terminal device is at least equipped with a microphone, a speaker, a display screen, a camera device and a network communication module.

9. The system for language training of aphasia patients according to claim 8, characterized in that: The user terminal device is implemented based on a smart mobile terminal.

10. The system for language training of aphasia patients according to claim 1, characterized in that: The cloud server is also equipped with data encryption and permission control functions to ensure the security and privacy of the patient's personal information and training data.

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