A mindfulness prediction-intervention-feedback system and control method

Abstract

The application discloses a mindfulness prediction-intervention-feedback system and a control method, relates to the technical field of mindfulness training systems, and comprises a wearable user interaction device and sequentially connected user management module, mindfulness intervention training module, dynamic prediction module, intervention effect evaluation module and feedback interaction module; the initial mindfulness level, depression level and anxiety level of a user are determined; based on the depression level and anxiety level of the user, a target training scheme of the user is determined, and the user is prompted to perform mindfulness training according to the target training scheme; the target training scheme is adjusted based on an emotional state factor; the real-time mindfulness level of the user and the completion progress of the target training scheme by the user are determined according to a preset time interval, a mindfulness training report is generated in combination with the initial mindfulness level, and visual processing is performed; the mindfulness training method is selected by evaluating the mindfulness level of the user, and the rationality of the mindfulness training scheme can be improved.

Description

Technical Field

[0001] This application relates to the field of mindfulness training system technology, and in particular to a mindfulness prediction-intervention-feedback system and control method. Background Technology

[0002] Mindfulness training is a popular psychotherapy that helps users improve focus, relieve stress, eliminate negative emotions, achieve better sleep, and increase well-being. Existing mindfulness training methods often only provide audio courses or audio instructions for self-practice, but they lack assessment of learning and training effectiveness, feedback on progress, and immediate guidance. Learners cannot accurately assess their progress, making it easy to give up halfway, significantly reducing the effectiveness of mindfulness practice and impacting enthusiasm.

[0003] Therefore, in order to improve the effectiveness of mindfulness training, there is an urgent need to provide a mindfulness training method or system. Summary of the Invention

[0004] The purpose of this application is to provide a mindfulness prediction-intervention-feedback system and control method, which can improve the rationality of mindfulness training program formulation by assessing the user's mindfulness level and selecting mindfulness training methods.

[0005] To achieve the above objectives, this application provides the following solution:

[0006] In a first aspect, this application provides a mindfulness prediction-intervention-feedback method, comprising: a wearable user interaction device and a processing device;

[0007] The wearable user interaction device is connected to the processing device;

[0008] The processing device includes: a user management module, a mindfulness intervention training module, a dynamic prediction module, an intervention effect evaluation module, and a feedback interaction module connected in sequence;

[0009] The user management module is used to complete user account login and user account verification, and to determine the user's initial mindfulness level, depression level and anxiety level;

[0010] The mindfulness intervention training module is used to determine the user's target training plan based on the user's depression and anxiety levels, and prompt the user to carry out mindfulness training according to the target training plan;

[0011] The dynamic prediction module is used to determine the user's emotional state factor based on user feedback data, and to adjust the target training scheme based on the emotional state factor.

[0012] The intervention effect evaluation module is used to determine the user's real-time mindfulness level according to a preset time interval, construct a mindfulness level change table in combination with the initial mindfulness level, and construct a completion progress change table according to the user's training completion progress.

[0013] The feedback and interaction module is used to generate a mindfulness training report based on the mindfulness level change table and the completion progress change table, according to a preset template, and to visualize the mindfulness training report.

[0014] The wearable user interaction device is connected to the mindfulness intervention training module and the dynamic prediction module respectively; the wearable user interaction device can be worn on the user's head; the wearable user interaction device is used to play the animation scene and sound scene corresponding to the target single training when receiving the target single training start instruction in the target training scheme; the sound scene includes voice prompts with background music; the wearable user interaction device is also used to obtain user feedback data.

[0015] Optionally, the wearable user interaction device includes AR glasses.

[0016] Optionally, the target training scheme is a special training scheme or a system training scheme.

[0017] Secondly, this application provides a control method for a mindfulness prediction-intervention-feedback system. Optionally, the control method for the mindfulness prediction-intervention-feedback system is applied to the mindfulness prediction-intervention-feedback system, and the control method for the mindfulness prediction-intervention-feedback system includes:

[0018] When a user logs in for the first time, their initial levels of mindfulness, depression, and anxiety are determined.

[0019] Based on the user's depression and anxiety levels, a target training plan is determined for the user; the target training plan is either a specialized training plan or a systematic training plan.

[0020] When a user logs in for the first time, prompt the user to perform mindfulness training according to the target training plan; the prompting the user to perform mindfulness training according to the target training plan includes: when receiving the target single training start instruction in the target training plan, playing the animation scene and sound scene corresponding to the target single training, and obtaining user feedback data;

[0021] The user's emotional state factors are determined based on user feedback data, and the target training program is adjusted based on the emotional state factors.

[0022] The user’s real-time mindfulness level is determined according to a preset time interval, and a mindfulness level change table is constructed in combination with the initial mindfulness level.

[0023] Based on the user's training completion progress, construct a progress change table;

[0024] Based on the mindfulness level change table and the completion progress change table, a mindfulness training report is generated according to a preset template, and the mindfulness training report is visualized.

[0025] Optionally, upon a user's first login, their initial levels of mindfulness, depression, and anxiety are determined, including:

[0026] Obtain the mindfulness level test completed by the user;

[0027] Based on the standard score calculation method and the mindfulness level test completed by the user, the mindfulness level score is determined as the user's initial mindfulness level;

[0028] Obtain the user-completed depression level questionnaire;

[0029] Depression level scores were determined based on standard score statistical methods and user-completed depression level questionnaires.

[0030] The depression level corresponding to the interval in which the depression level score falls is determined as the user's depression level.

[0031] Obtain the anxiety level questionnaire filled out by the user;

[0032] Anxiety level scores were determined based on standard score statistical methods and user-completed anxiety level questionnaires.

[0033] The anxiety level is determined by the range in which the anxiety level score falls.

[0034] Optionally, the depression level includes mild depression, moderate depression, and severe depression;

[0035] The anxiety levels are categorized as mild, moderate, and severe.

[0036] Optionally, based on the user's depression and anxiety levels, a target training program is determined for the user, including:

[0037] Determine whether the user is suffering from severe depression or severe anxiety to obtain the initial assessment result;

[0038] If the first assessment result is yes, then a prompt will be issued saying "You are experiencing severe emotional distress, please seek medical attention promptly";

[0039] If the first judgment result is negative, then determine whether the user is simultaneously experiencing mild depression and mild anxiety to obtain the second judgment result;

[0040] If the second judgment result is yes, then a "mild emotional distress" prompt will be issued, and the specific training program will be determined as the target training program;

[0041] If the second judgment result is negative, a "moderate emotional distress" prompt will be issued, and the system training program will be identified as the target training program.

[0042] Optionally, before determining the user's initial level of mindfulness, depression, and anxiety, the method also includes:

[0043] The system acquires feedback data from multiple test subjects, as well as the corresponding emotional state factors of the test subject when measuring each feedback data point; the feedback data includes: brain oxygenated hemoglobin, deoxygenated hemoglobin, event-related potentials, heart rate, and respiratory rate;

[0044] The emotional state factor of the test subject at the time of obtaining each feedback data point;

[0045] Using feedback data as input and the corresponding emotional state factor of the test subject when measuring each feedback data point as output, the quantum hidden Markov algorithm model is trained to obtain an emotional state factor prediction model.

[0046] The emotional state factor prediction model is deployed in the dynamic prediction module of the mindfulness prediction-intervention-feedback system.

[0047] Optionally, determining the user's emotional state factor based on user feedback data, and adjusting the target training scheme based on the emotional state factor, includes:

[0048] User feedback data is input into the emotion state factor prediction model to obtain the user's emotion state factor;

[0049] The training scheme corresponding to the interval of the user's emotional state factor is determined as the adjusted target training scheme.

[0050] Optionally, the user's real-time mindfulness level is determined according to a preset time interval, and a mindfulness level change table is constructed based on the initial mindfulness level, including:

[0051] The user's real-time mindfulness level is determined according to a preset time interval. The method for determining the real-time mindfulness level is as follows: obtain the mindfulness level test paper filled out by the user in real time; compare the mindfulness level test paper filled out by the user in real time with the mindfulness level test paper with the standard answer, and obtain the mindfulness level score as the user's real-time mindfulness level.

[0052] A mindfulness level variation table is constructed with test time as the horizontal axis and mindfulness level as the vertical axis; the mindfulness level includes the initial mindfulness level and the real-time mindfulness level corresponding to multiple test times.

[0053] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0054] This application provides a mindfulness prediction-intervention-feedback system and control method, including: a wearable user interaction device and a processing device. The wearable user interaction device is connected to the processing device. The processing device includes: a user management module, a mindfulness intervention training module, a dynamic prediction module, an intervention effect evaluation module, and a feedback interaction module connected sequentially. The user management module is used to complete user account login and verification, and determine the user's initial mindfulness level, depression level, and anxiety level. The mindfulness intervention training module is used to determine the user's target training plan based on the user's depression and anxiety levels, and prompt the user to perform mindfulness training according to the target training plan. The target training plan can be a specific training plan or a system training plan. The dynamic prediction module is used to determine the user's emotional state factors based on user feedback data, and adjust the target training plan based on the emotional state factors. The intervention effect evaluation module is used to determine the user's real-time mindfulness level at preset time intervals, construct a mindfulness level change table based on the initial mindfulness level, and construct a completion progress change table based on the user's training completion progress. The feedback and interaction module generates mindfulness training reports based on mindfulness level change tables and progress change tables, following a preset template, and visualizes these reports. By providing convenient and efficient mindfulness training services, it helps users alleviate psychological stress, cultivate a positive mindset, and offers psychological support and services to more users. Compared with existing mindfulness training software, this application can provide targeted guidance for subsequent practice based on feedback from practice data and assessment results, thereby improving training effectiveness. The multi-scenario training mode provided by this application, supported by a scientific theoretical framework, offers users targeted and personalized training solutions. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a schematic diagram of a mindfulness prediction-intervention-feedback system structure in one embodiment of this application;

[0057] Figure 2 This is a flowchart of the control method of a mindfulness prediction-intervention-feedback system in one embodiment of this application. Detailed Implementation

[0058] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0059] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0060] Currently, emotion prediction mainly relies on current emotional levels and mindfulness assessment scale scores to predict emotional levels at a future point in time. However, factors influencing training effectiveness also include mindfulness attitude levels, physiological states, task performance, and training consistency during mindfulness intervention. Therefore, this application provides, in an exemplary embodiment, a mindfulness prediction-intervention-feedback system that performs multi-dimensional prediction and assessment of mindfulness therapy task recommendations. Compared to existing technologies, this system more dynamically and accurately comprehensively assesses the user's training process characteristics, recommending more suitable and matching tasks to maximize training effectiveness and improve compliance. Figure 1 As shown, this embodiment provides a mindfulness prediction-intervention-feedback system, including: a wearable user interaction device and a processing device. The wearable user interaction device is connected to the processing device.

[0061] The processing device includes: a user management module, a mindfulness intervention training module, a dynamic prediction module, an intervention effect evaluation module, and a feedback interaction module, which are connected in sequence.

[0062] The user management module is used to complete user account login and user account verification, and to determine the user's initial mindfulness level, depression level, and anxiety level.

[0063] The mindfulness intervention training module is used to determine a user's target training plan based on their depression and anxiety levels, and then guide the user to perform mindfulness training according to the target training plan. The target training plan can be a specific training plan or a systemic training plan.

[0064] The dynamic prediction module is used to determine the user's emotional state factors based on user feedback data and adjust the target training program accordingly. The roles of depression and anxiety levels are similar to those of other physiological indicators. The system determines the level of emotional state factors and the corresponding training program based on depression, anxiety, and physiological indicator levels.

[0065] The intervention effect evaluation module is used to determine the user's real-time mindfulness level at preset time intervals, construct a mindfulness level change table based on the initial mindfulness level, and construct a completion progress change table based on the user's training completion progress.

[0066] The feedback and interaction module is used to generate a mindfulness training report based on the mindfulness level change table and the completion progress change table, according to a preset template, and to visualize the mindfulness training report.

[0067] The wearable user interaction device is connected to both the mindfulness intervention training module and the dynamic prediction module. The device can be worn on the user's head. Upon receiving a command to begin a single training session from the target training plan, the device plays the corresponding animation and sound scenes for that single training session. The sound scenes include voice prompts with background music. The device also acquires user feedback data. The wearable user interaction device includes AR glasses.

[0068] 1. User Management Module

[0069] The user management module is responsible for user account login and verification. Upon first login, users must complete basic information, including name, age, and gender, and take an emotional level assessment. The assessment tools include the Mindfulness Assessment Scale (FFMQ, corresponding to mindfulness level), the Depression Screening Scale (PHQ-9, corresponding to depression level), and the Anxiety Screening Scale (GAD-7, corresponding to anxiety level). All questions must be completed, scored, and a score calculated. The system collects user emotional level assessment data through these scales and stores it in the user database. Based on user responses, the system calculates and generates basic information on the user's mindfulness (used to evaluate practice effectiveness) and emotional level (used to push training plans), and then pushes training plans and work suggestions.

[0070] The PHQ-9 rating scale is a scale used to assess the severity of depression. Scores range from 0 to 27, each corresponding to a different degree of depression. Example: Lack of motivation or interest in activities. Completely none: 0 points; several days: 1 point; more than a week: 2 points; almost every day: 3 points. Depression levels are classified according to the following rules:

[0071] Mild (no depression, mild depression): 0-4 points indicate no depression. 5-9 points indicate mild depression.

[0072] Moderate (moderate depression, moderate to severe depression): 10-14 points indicates moderate depression. 15-19 points indicates moderate to severe depression.

[0073] Severe (seek medical advice and follow doctor's instructions): A score of 20-27 indicates severe depression.

[0074] The GAD-7 scoring system is used for screening and assessment of generalized anxiety disorder. Example: Feeling tense, anxious, or anxious. No anxiety at all: 0 points; several days: 1 point; more than a week: 2 points; almost every day: 3 points. Anxiety levels are classified according to the following rules:

[0075] Mild (normal anxiety, mild anxiety): 0-4 points: normal anxiety, 5-9 points: mild anxiety.

[0076] Moderate (moderate anxiety, moderate to severe anxiety): 10-13 points: moderate anxiety, 14-18 points: moderate to severe anxiety.

[0077] Severe (seek medical attention and follow doctor's advice): 19-21 points: Severe anxiety.

[0078] Mood Level Prediction: The system assesses the user's depression and anxiety levels. For users with PHQ-9 and GAD-7 scores below 9 (i.e., depression and anxiety levels below mild), the system will recommend specific training. For users with PHQ-9 scores between 10 and 19 and GAD-7 scores between 10 and 14 (i.e., depression and anxiety levels above moderate), the system will recommend daily training and set corresponding training plans (only training methods are recommended; the frequency is controlled by the user) and reminders (Table 1). This recommendation result is displayed to the user through the personalized recommendation module in the user interface.

[0079] Table 1. Rules for Selecting Target Training Programs

[0080]

[0081] As an alternative implementation, the user management module is used for user information management, including basic information collection, scale assessment of depression and anxiety indicators, a Mindfulness-Based 12 Factor (A_L) questionnaire, and physiological indicators. Based on a quantum hidden Markov model, it uses current anxiety scores, depression scores, Mindfulness-Based 12 Factor scores, and physiological indicator scores to predict emotional state at a future point in time. The prediction algorithm is expressed by the following quantum hidden Markov algorithm:

[0082] (1)

[0083] in, This represents the probability of predicting emotional state at a future point in time. Represents a given environment subspace The conditional density matrix of a quantum system (i.e., in the current emotional state). The density matrix describes the state of the system in the current emotional state, that is, the probability of predicting the emotional state at a future point in time. L is a physiological measurement indicator, such as heart rate, cerebral blood flow, etc. This represents the summation index, used to sum over all possible Kraus operators or other related operators. It represents the time variable and is used to describe the evolution of a quantum system over time. This represents the integration variable, used to indicate the lower limit of integration in time integration. It is usually used together with time t to describe the influence of the system's past states on the current state. Represents the environment subspace Perform partial trace operation. This operation is used to eliminate the environmental degrees of freedom from the density matrix of the entire system, obtaining the conditional density matrix of the system. The space representing the environment is divided into different subspaces. Indicates the first Subspace. L′ represents the Liouvillian superoperator associated with system-environment coupling. It describes the impact of the environment on the system, i.e., predictive index data such as anxiety score, depression score, heart rate data, cerebral blood flow data, etc. Under the weak coupling approximation, L′ can be regarded as a perturbation. The Green's function, which is associated with the system's Hamiltonian HS, describes the system's evolution from time τ to time t. This represents the Liouvillian supercomputer with system-environment coupling at time τ. This represents the conjugate transpose of the Green's function, used to describe the inverse process of system evolution. This represents the total density matrix of the entire system (including the quantum system and the environment) at time t.

[0084] 2. Mindfulness Intervention Training Module

[0085] Based on the emotional state prediction results, a user's emotional state can be categorized into three levels: severe, moderate, and mild. Using repeated emotional measurement data and S2 emotion prediction results, a quantum hidden Markov algorithm is employed to determine mindfulness factor scores. A push notification strategy is determined based on each mindfulness factor score and the emotional state prediction results. When the predicted emotional state is severe, medical information is directly pushed. When the predicted emotional state is mild or moderate, training content corresponding to the lower mindfulness factor score is packaged into a training plan. When the predicted emotional state is mild and all mindfulness factor scores are higher than the preset scores, the user can choose a training plan corresponding to at least one factor for self-training.

[0086] After each day's practice, assess the user's emotional state, mindfulness factor level, number of training tasks completed, and practice duration. Starting from the second day of the week, set the user's training plan from the previous day as the initial training plan for the day. Adjust the difficulty ratio of training items in the daily mindfulness training task library based on the user's emotional state and mindfulness factor level. Adjust the number of training items and the practice duration of each item in the daily mindfulness training tasks based on the actual number of tasks completed and the practice duration from the previous day. Provide practice reminders and psychological education to users who have not practiced, explaining the week's practice tasks and their effects. After completing the week's training, before the next week's training, calculate the daily change rate of emotional state and mindfulness factor level, as well as the overall change rate of emotional state and mindfulness factor level for the week, based on the combined daily and overall change rates of emotional state and mindfulness factor level for the week. Determine whether to adjust the difficulty ratio of training items in the next week's training task library based on these rates.

[0087] Recommended tasks within the topic: After each day's practice, assess the user's emotional state, mindfulness factor level, number of completed training tasks, and practice duration. Starting the next day, set the user's previous day's training plan as the initial training plan for the day. Adjust the difficulty ratio of training items in the daily mindfulness training task library based on the user's emotional state and mindfulness factor level. Adjust the number of training items and the practice duration of each item in the daily mindfulness training tasks based on the user's actual number of completed tasks and practice duration from the previous day. Provide practice reminders and psychological education to users who haven't practiced, explaining the week's practice tasks and their effects. After completing this topic's training, before starting the next topic's training, calculate the daily change rate of emotional state and mindfulness factor level for this topic, as well as the overall weekly change rate of emotional state and mindfulness factor level. Based on these rates, determine whether to adjust the difficulty ratio of training items in the next topic's training task library.

[0088] As an alternative implementation method, the mindfulness training module includes two intervention programs: systemic training and specific training. Training plans are automatically pushed based on the user's level of emotional distress. Each program comes with a detailed introduction and provides voice guidance to help users plan, understand, and complete the training.

[0089] 2.1 System Training

[0090] The System Training Program is an intervention program for emotional distress developed by the developers based on existing mindfulness-based cognitive therapy, primarily targeting individuals with moderate emotional distress. The System Training Program includes a 48-day systematic training plan (comprising eight themes: mindfulness awareness, perception, attention, flexibility, acceptance, thinking, behavior, and mindful living, with each theme containing six days of training content).

[0091] Training Process and Feedback: Daily training content includes detailed introductions, operation steps, explanations of key knowledge points, audio practice, and voice guidance. Users can conduct self-training based on system prompts. After completing the training, users are reminded to click the "Complete Training" button (the system backend indicates the progress of the training, such as "Current training is 50% complete"). After daily training, the system will push family practice tasks to users.

[0092] Training Execution and Progress Management: After a user completes their training for the day, the system automatically records the user's training duration and progress (e.g., completing the training audio counts as 50%, submitting the home practice task counts as 100%), and pushes the training content for the next day according to the plan. And so on.

[0093] 2.2 Specialized Training

[0094] The specialized training includes four themes: Awakening, Pause, Cultivation, and Enjoyment. Each theme contains multiple similar training programs, and the system guides users through mindfulness practice via training audio. Specialized training execution: The system automatically enters the specialized training mode based on the user's level of emotional distress (the aforementioned emotional assessment falls within the range of mild depression and mild anxiety). Awakening-themed training is pushed out according to the plan, and subsequent training is pushed out based on dynamic prediction results.

[0095] 3. Dynamic Prediction Module

[0096] The system collects, stores, and analyzes user learning data, emotional level assessment data, and feedback data. Through data analysis, the system can understand the user's current state, learning habits, training effects, and emotional changes, thereby dynamically predicting the user's future emotional and mental health status and providing more personalized and accurate training recommendations and guidance. Simultaneously, the system can apply the data analysis results to subsequent training content optimization and function improvements, continuously enhancing the user experience and training effectiveness. Based on the model's prediction results, the system pushes training topics, each corresponding to one or more emotion management characteristic factors, forming a complete emotion management training intervention plan.

[0097] The assessment targets the stable psychological and behavioral characteristics of the target customer group, as well as the fluctuations in their daily psychological and behavioral states. The assessment primarily employs physiological measurements, integrating fNIRS near-infrared brain imaging, EEG electroencephalography (EEG), blood oxygen saturation detection, and PPG optical heart rate detection. It collects real-time data on EEG, heart rate, and blood oxygenation. Key measurement indicators include (but are not limited to) cerebral oxygenated hemoglobin, deoxygenated hemoglobin, event-related potentials, heart rate, and respiratory rate. The models of the physiological measurement devices are listed in Table 2.

[0098] Table 2. Model List of Physiological Measurement Devices

[0099]

[0100] Users can use the system's prediction module to predict their emotional and mental health status at any time they need, based on existing assessment data (the system defaults to predicting the next seven days; customers can modify the prediction range according to their own needs, with a minimum prediction of one day and a maximum prediction of 15 days).

[0101] Based on the mechanism of mindfulness-based emotion management and clinical experience, a mindfulness training program was independently developed. This program collects large-scale user data and uses a generalized structural equation model to fit user emotion management characteristic factors. This analysis process can be expressed by the following algorithm:

[0102] = + (2)

[0103] in, The predicted probability of the emotional state in expression (1) is expressed as follows: Indicates the characteristic factors of emotion management. This indicates the strength of the relationship between observed indicators and emotion management characteristic factors. This represents the measurement error. Using this algorithm, the characteristic factors of college students' emotional management can be fitted, clarifying the relationship between the observed indicators and the characteristic factors of emotional management. Taking the fitting of six observed indicators to two characteristic factors of emotional management as an example, equation (2) can be expressed as:

[0104] + (3)

[0105] in, , , , , Represents the predicted probability of an emotional state. , , , , , This indicates the strength of the relationship between predictive indicators and emotion management characteristic factors. , Indicates the characteristic factors of emotion management. , , , , , This indicates measurement error.

[0106] Similarly, the above algorithm can be used to fit the relationship between emotion management feature factors and training topic factors, and training topics can be formulated accordingly. This analysis process can be expressed by the following algorithm:

[0107] + (4)

[0108] in, H represents the training topic factor. The strength of the relationship between specific training factors and emotion management characteristic factors This represents the measurement error. Using this algorithm, training topic factors can be fitted to clarify the relationship between emotion management feature factors and training topic factors. Taking the fitting of six emotion management feature factors with two training topic factors as an example, equation (4) can be expressed as:

[0109] + (5)

[0110] in, , , , , , Indicates the characteristic factors of emotion management. , , , , , This indicates the strength of the relationship between emotion management feature factors and training topic factors. , Represents training topic factors. 。 indicates measurement error.

[0111] The user's emotional or mental health status at a certain point in the future is predicted by Equation (1). The emotional or mental health status is matched with the emotional management feature factor by Equation (2). Furthermore, the emotional management feature factor is matched with the special training theme factor by Equation (4). This realizes the connection between the prediction of emotional or mental health status and the training plan, and improves the efficiency of accurate push of the training plan.

[0112] 4. Intervention Effectiveness Evaluation Module

[0113] The intervention effect evaluation module is another core technology of this application and a major innovation that distinguishes it from other similar mindfulness learning software.

[0114] The effectiveness of intervention training is evaluated through assessment data analysis. The intervention effectiveness evaluation module records data generated by users before, during, and after training, including two parts: training data (number of training sessions, training frequency, process data; percentage of progress completed; completion assessment; used to generate reports) and assessment data.

[0115] Training data includes training duration, training frequency, and training effect, while assessment data includes the client's own demographic data (such as age, gender, occupation, etc.) and psychological and behavioral assessment data, such as anxiety score, depression score, brain oxygenated hemoglobin, deoxygenated hemoglobin, event-related potentials, heart rate, etc.

[0116] Collect and record customer training and evaluation data. The data is stored on commercial cloud servers (such as Alibaba Cloud, Baidu Cloud, or Tencent Cloud). The evaluation system backend provides data download for system operation staff. The data file format is tab-delimited text or comma-delimited text.

[0117] The intervention effect evaluation is divided into two parts: individual effect evaluation (individual data analysis) and group effect evaluation (group data analysis).

[0118] In the individual data analysis section, after an individual customer submits their assessment results, the system first determines whether this is the customer's first submission and assigns a timestamp to the result. If the customer has taken the assessment multiple times, a timestamp is assigned to each result separately. Customers can choose to view the change in score for a specific item on a particular assessment over time, or they can choose to view the change in the entire assessment result over time. The data and trends of the entire assessment result are represented by the average of the original scores of the assessment items.

[0119] In the group data analysis section, organizations or enterprise clients can directly download raw data for self-analysis based on their needs, or request the assessment system to provide assessment results directly. The assessment system determines whether to provide cross-level correlation and multi-level modeling results based on the organizational hierarchy and repeated measures. Specifically, if there is repeated measures among participants within the organization, or if the organization is divided into different subgroups based on certain characteristics, the system provides cross-level correlation, indicating the extent to which the intra-individual variation trend of the measured phenomenon differs between individuals, or the extent to which the measured relationship differs between subgroups. If organizational clients want further detailed information, the system provides an interface for clients to customize their analysis models, assisting them in selecting predictor variables and outcome variables of interest at different organizational levels and customizing their model analysis. For high-level predictor variables (such as organizational characteristics), the system automatically performs overall centralization; for low-level predictor variables (such as individual characteristics), the system automatically performs group centralization. After the client submits the customized analysis, the system performs the analysis and outputs visualization results and analysis reports, such as three-line tables, line charts, and bar charts.

[0120] After completing the mindfulness-themed practice, a questionnaire assessment of mindfulness perception and description (a standard questionnaire is available) is conducted. If the change in the assessment results compared to the pre-training test does not reach the statistical standard, supplementary practice in the perception-themed practice is recommended until the standard is met. If the standard is met, the next pause-themed training session begins. If the change in the assessment results compared to the pre-training test does not reach the statistical standard, supplementary practice in the pause-themed practice is recommended again. This process is repeated for all four themes. Finally, an overall assessment of trait mindfulness level and emotional level is conducted, and a comprehensive practice report is provided to the user, along with a comparison of before and after data.

[0121] Whether the pre- and post-evaluation results reach a statistically significant level can be determined using the following algorithm:

[0122] T = / ( SD / (6)

[0123] Where T represents the test value of the pre- and post-test paired t-test. SD represents the mean of the differences between the preceding and following tests, SD represents the standard deviation of the differences between the preceding and following tests, and n is the paired sample size.

[0124] 5. Feedback and Interaction Module

[0125] Based on the data analysis results, the feedback and interaction module of this system can provide users with personalized course learning or training suggestions, helping users to develop training plans more scientifically. This is also one of the advantages of this system.

[0126] 5.1 Assessment Feedback

[0127] Regarding the training process, the system provides feedback on training process features based on training data, including the actual training situation at each stage, potential problems during the training process, and improvement suggestions. Regarding the evaluation results, the system provides feedback on the current evaluation status, recommends stages that need to be added to the training, and provides improvement suggestions.

[0128] If, after a user completes a phase of training, the mindfulness trait assessment results show that the expected training effect has not been achieved, that is, there is no significant difference in the relevant assessment results before and after training, the system will provide corresponding practice suggestions to encourage the user to continue practicing; if the relevant assessment results before and after training show significant improvement, the system will jump to other relatively weak training modules to continue practicing and improve the user's overall level of mindfulness traits.

[0129] For individual clients, the system provides access to and download of historical training and assessment data based on their individual accounts. This data includes graphs showing changes in historical training characteristics, trend charts of assessment results, descriptive statistics, and raw data. For organizational clients, the system provides raw data downloads based on administrator privileges. After anonymizing sensitive personal information, the data can be delivered to the organizational client.

[0130] The data analysis results are presented in various formats, including three-line tables, line charts, and bar charts, along with corresponding analysis reports. The reports can be opened directly in the user's default mobile browser or sent to a designated email address. If the customer has linked their personal email address to the system or logged in using their personal email address, the system will send the assessment results to that address by default. The assessment results document is sent as an email attachment, with options for PDF or docx format. The default email address can be changed in the system settings.

[0131] Organizational or individual clients can subscribe to the training and assessment system via email to receive relevant training information or learn about new features in system upgrades.

[0132] 5.2 Practice Feedback

[0133] An interactive feedback corpus was established based on the interactive dialogues between facilitators and participants in offline group coaching practices.

[0134] Based on the interactive feedback corpus, the system extracts key information from user-submitted experiences and questions, classifying and extracting it into three categories: the first category is a description of the user's practice experience; the second category is a description of the difficulties and discomfort encountered by the user during practice; and the third category is a description of the problems encountered by the user during practice.

[0135] The system categorizes and provides feedback based on the extracted information. For the first category of descriptions, the system pushes affirmative and encouraging expressions and reinforces mindfulness attitudes; for the second category of descriptions, the system pushes supportive and encouraging expressions and reinforces non-judgmental, non-reactive, and friendly attitudes; for the third category of descriptions, the system pushes relevant Q&A.

[0136] This application utilizes the WeChat Mini Program framework for front-end development, employing WXML and WXSS for page layout and styling, and JavaScript for page interaction and logic processing. Furthermore, it leverages the API interfaces provided by WeChat Mini Programs to implement functions such as user information retrieval, data storage, and transmission.

[0137] The backend is developed using the Node.js and Express frameworks, and uses MongoDB as the database to store user information and training data. The backend service is primarily responsible for handling frontend requests, implementing functions such as user authentication, data querying, and updates. Simultaneously, it utilizes data analysis tools to process and analyze the training data, providing users with personalized training suggestions.

[0138] This application provides strict protection for user information. During user registration and login, encryption technology is used to transmit and store sensitive information. Furthermore, the mini-program regularly backs up and encrypts user data to ensure data security and reliability.

[0139] During development, rigorous unit testing, integration testing, and performance testing will be conducted to ensure the stability and usability of the mini-program. After testing is completed, it will be deployed and released online to provide services to users.

[0140] Real-world example:

[0141] Let's say Mr. Zhang is an office worker who has recently been experiencing high work pressure, leading to frequent anxiety and tension, which has affected his quality of life and work efficiency. He decides to use the mindfulness self-help system developed by Guangdong University of Foreign Studies for self-training.

[0142] 1. User Management Module:

[0143] Mr. Zhang first registered and logged in through the system login interface, filling in his basic personal information, such as name, age, and occupation.

[0144] Next, he completed an emotional level assessment in the system, including anxiety level tests and depression level tests.

[0145] 2. Emotional Level Assessment, Prediction, and Recommendation Module:

[0146] Based on Mr. Zhang's assessment results, the system determined that his emotional level was mild anxiety.

[0147] Therefore, the system recommended that Mr. Zhang enter the specialized training module for customized training to address his anxiety.

[0148] 3. Training Module:

[0149] Following the system's recommendation, Mr. Zhang entered the specialized training module.

[0150] This module provides a series of mindfulness training exercises to address anxiety.

[0151] Following the system's guidance, Mr. Zhang conducted targeted training every day and submitted feedback after each training session.

[0152] 4. Dynamic Assessment Module:

[0153] During Mr. Zhang's specialized training, the system regularly assessed his emotional and physiological levels.

[0154] These assessment results are compared with previous data, and the system predicts the possible emotional state over the next 7 days. The system then pushes specialized training content for the next topic based on the prediction results to adapt to Mr. Zhang's training progress; or it continues to provide more training content on the same topic based on the problems he may still have.

[0155] 5. System training module.

[0156] If Mr. Zhang's emotional state has reached a moderate level, or if he requests updated mindfulness training, he can choose to enter the system training module. The system training module includes training in eight themes, such as mindfulness awareness, perception, and attention, which can help Mr. Zhang comprehensively improve his mindfulness level.

[0157] 6. Feedback and Evaluation:

[0158] After a period of specialized training, Mr. Zhang's anxiety was significantly relieved.

[0159] Based on Mr. Zhang's training data and assessment results, the system generated a detailed training report showcasing his learning progress and achievements.

[0160] In another exemplary embodiment of this application, such as Figure 2 A control method for a mindfulness prediction-intervention-feedback system is provided. This control method is applied to the mindfulness prediction-intervention-feedback system and includes:

[0161] Step 201: When a user logs in for the first time, determine the user's initial level of mindfulness, depression, and anxiety.

[0162] Step 202: Based on the user's depression and anxiety levels, determine the user's target training plan. The target training plan can be a specific training plan or a systematic training plan.

[0163] Step 203: When a user logs in for the first time, prompt the user to perform mindfulness training according to the target training plan. Prompting the user to perform mindfulness training according to the target training plan includes: upon receiving the instruction to start the target single training session in the target training plan, playing the animation and sound scenes corresponding to the target single training session, and obtaining user feedback data.

[0164] Step 204: Determine the user's emotional state factor based on user feedback data, and adjust the target training program based on the emotional state factor.

[0165] Step 205: Determine the user's real-time mindfulness level according to the preset time interval, and construct a mindfulness level change table in combination with the initial mindfulness level.

[0166] Step 206: Construct a progress change table based on the user's training completion progress.

[0167] Step 207: Based on the mindfulness level change table and the completion progress change table, generate a mindfulness training report according to the preset template, and visualize the mindfulness training report.

[0168] Step 201 includes:

[0169] Step 201-1: Obtain the mindfulness level test paper filled out by the user.

[0170] Step 201-2: Based on the standard score calculation method and the mindfulness level test completed by the user, determine the mindfulness level score as the user's initial mindfulness level.

[0171] Step 201-3: Obtain the depression level test completed by the user.

[0172] Step 201-4: Determine the depression level score based on the standard score statistical method and the depression level test completed by the user.

[0173] Step 201-5: Determine the depression level corresponding to the interval of the depression score as the user's depression level. Depression levels include mild depression, moderate depression, and severe depression.

[0174] Step 201-6: Obtain the anxiety level test paper filled out by the user.

[0175] Step 201-7: Determine the anxiety level score based on the standard score statistical method and the anxiety level test completed by the user.

[0176] Step 201-8: Determine the anxiety level corresponding to the range of the anxiety level score as the user's anxiety level. Anxiety levels include mild anxiety, moderate anxiety, and severe anxiety.

[0177] Step 202 includes:

[0178] Step 202-1: Determine whether the user is experiencing severe depression or severe anxiety, and obtain the first judgment result. If the first judgment result is yes, proceed to step 202-2; if the first judgment result is no, proceed to step 202-3.

[0179] Step 202-2: Issue a prompt: "You are experiencing severe emotional distress. Please seek medical attention promptly."

[0180] Step 202-3: Determine whether the user is simultaneously experiencing mild depression and mild anxiety, obtaining a second judgment result. If the first judgment result is yes, proceed to step 202-4; if the first judgment result is no, proceed to step 202-5.

[0181] Step 202-4: Issue a "mild emotional distress" prompt and determine the specific training plan as the target training plan.

[0182] Step 202-5: Issue a "moderate emotional distress" prompt and confirm the system training program as the target training program.

[0183] Before step 201, the following is also included:

[0184] Step 208: Obtain feedback data from multiple test subjects, as well as the corresponding emotional state factors for each test subject when measuring each feedback data point. Feedback data includes: anxiety score, depression score, brain oxygenated hemoglobin, deoxygenated hemoglobin, event-related potentials, heart rate, and respiratory rate.

[0185] Step 209: Analyze the emotional state factor of the test subject for each feedback data point.

[0186] Step 2010: Using the feedback data as input and the corresponding emotional state factor of the test subject when measuring each feedback data point as output, train the quantum hidden Markov algorithm model to obtain the emotional state factor prediction model.

[0187] Step 2011: Deploy the emotional state factor prediction model in the dynamic prediction module of the mindfulness prediction-intervention-feedback system.

[0188] Step 204 includes:

[0189] Step 204-1: Input user feedback data into the emotional state factor prediction model to obtain the user's emotional state factor.

[0190] Step 204-2: Determine the training scheme corresponding to the interval of the user's emotional state factor as the adjusted target training scheme.

[0191] Step 205 includes:

[0192] Step 205-1: Determine the user's real-time mindfulness level according to a preset time interval. The method for determining the real-time mindfulness level is as follows: Obtain the mindfulness level test paper filled out by the user in real time. Compare the mindfulness level test paper filled out by the user in real time with the mindfulness level test paper with standard answers to obtain the mindfulness level score as the user's real-time mindfulness level.

[0193] Step 205-2: Construct a mindfulness level change table with test time as the x-axis and mindfulness level as the y-axis. Mindfulness level includes the initial mindfulness level and the real-time mindfulness level corresponding to multiple test times.

[0194] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0195] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A mindfulness prediction-intervention-feedback system, characterized in that, include: Wearable user interaction devices and processing devices; The wearable user interaction device is connected to the processing device; The processing device includes: a user management module, a mindfulness intervention training module, a dynamic prediction module, an intervention effect evaluation module, and a feedback interaction module connected in sequence; The user management module is used to complete user account login and user account verification, and to determine the user's initial mindfulness level, depression level and anxiety level; The mindfulness intervention training module is used to determine the user's target training plan based on the user's depression and anxiety levels, and prompt the user to carry out mindfulness training according to the target training plan; The dynamic prediction module is used to determine the user's emotional state factor based on user feedback data, and adjust the target training scheme based on the emotional state factor. Specifically, determining the user's emotional state factor based on user feedback data and adjusting the target training scheme based on the emotional state factor includes: inputting user feedback data into an emotional state factor prediction model to obtain the user's emotional state factor; determining the training scheme corresponding to the interval of the user's emotional state factor as the adjusted target training scheme; the emotional state factor prediction model is trained on a quantum hidden Markov algorithm model, using feedback data from multiple testers as input and the emotional state factor of the corresponding tester when measuring each feedback data point as output; the feedback data includes: anxiety score, depression score, cerebral oxygenated hemoglobin, deoxygenated hemoglobin, event-related potentials, heart rate, and respiratory rate; the expression for the quantum hidden Markov algorithm is: ;in, This represents the probability of predicting emotional state at a future point in time. Represents a given environment subspace The conditional density matrix of a quantum system L For physiological measurement indicators, Indicates the summation index. Represents a time variable. Represents the integral variable. Represents the environment subspace Perform partial trace operation. The space representing the environment is divided into different subspaces. Indicates the first Individual space, This represents the Liouvillian supercomputer associated with system-environment coupling. The Green's function represents the function associated with the system's Hamiltonian HS. Indicates time τ The system-environment coupled Liouvillian supercomputing Represents the conjugate transpose of the Green's function. Indicates the entire system in time t The total density matrix; The intervention effect evaluation module is used to determine the user's real-time mindfulness level according to a preset time interval, construct a mindfulness level change table in combination with the initial mindfulness level, and construct a completion progress change table according to the user's training completion progress. The feedback and interaction module is used to generate a mindfulness training report based on the mindfulness level change table and the completion progress change table, according to a preset template, and to visualize the mindfulness training report. The wearable user interaction device is connected to the mindfulness intervention training module and the dynamic prediction module respectively; the wearable user interaction device can be worn on the user's head; the wearable user interaction device is used to play the animation scene and sound scene corresponding to the target single training when receiving the target single training start instruction in the target training scheme; the sound scene includes voice prompts with background music; the wearable user interaction device is also used to obtain user feedback data.

2. The mindfulness prediction-intervention-feedback system according to claim 1, characterized in that, The wearable user interaction device includes AR glasses.

3. The mindfulness prediction-intervention-feedback system according to claim 1, characterized in that, The target training scheme is either a specialized training scheme or a system training scheme.

4. A control method for a mindfulness prediction-intervention-feedback system, characterized in that, The control method for the mindfulness prediction-intervention-feedback system is applied to the mindfulness prediction-intervention-feedback system as described in any one of claims 1-3, wherein the control method for the mindfulness prediction-intervention-feedback system includes: When a user logs in for the first time, their initial levels of mindfulness, depression, and anxiety are determined. Based on the user's depression and anxiety levels, a target training plan is determined for the user; the target training plan is either a specialized training plan or a systematic training plan. When a user logs in for the first time, prompt the user to perform mindfulness training according to the target training plan; the prompting the user to perform mindfulness training according to the target training plan includes: when receiving the target single training start instruction in the target training plan, playing the animation scene and sound scene corresponding to the target single training, and obtaining user feedback data; The user's emotional state factors are determined based on user feedback data, and the target training program is adjusted based on the emotional state factors. The user’s real-time mindfulness level is determined according to a preset time interval, and a mindfulness level change table is constructed in combination with the initial mindfulness level. Based on the user's training completion progress, construct a progress change table; Based on the mindfulness level change table and the completion progress change table, a mindfulness training report is generated according to a preset template, and the mindfulness training report is visualized.

5. The control method for the mindfulness prediction-intervention-feedback system according to claim 4, characterized in that, Upon a user's first login, their initial levels of mindfulness, depression, and anxiety are determined, including: Obtain the mindfulness level test completed by the user; Based on the standard score calculation method and the mindfulness level test completed by the user, the mindfulness level score is determined as the user's initial mindfulness level; Obtain the user-completed depression level questionnaire; Depression level scores were determined based on standard score statistical methods and user-completed depression level questionnaires. The depression level corresponding to the interval in which the depression level score falls is determined as the user's depression level. Obtain the anxiety level questionnaire filled out by the user; Anxiety level scores were determined based on standard score statistical methods and user-completed anxiety level questionnaires. The anxiety level is determined by the range in which the anxiety level score falls.

6. The control method for the mindfulness prediction-intervention-feedback system according to claim 5, characterized in that, The depression levels include mild depression, moderate depression, and severe depression; The anxiety levels are categorized as mild, moderate, and severe.

7. The control method for the mindfulness prediction-intervention-feedback system according to claim 5, characterized in that, Based on the user's depression and anxiety levels, a targeted training program is determined, including: Determine whether the user is suffering from severe depression or severe anxiety to obtain the initial assessment result; If the first assessment result is yes, then a prompt will be issued saying "You are experiencing severe emotional distress, please seek medical attention promptly"; If the first judgment result is negative, then determine whether the user is simultaneously experiencing mild depression and mild anxiety to obtain the second judgment result; If the second judgment result is yes, then issue a "mild emotional distress" prompt and determine the special training program as the target training program; If the second judgment result is negative, a "moderate emotional distress" prompt will be issued, and the system training program will be identified as the target training program.

8. The control method for the mindfulness prediction-intervention-feedback system according to claim 6, characterized in that, Before determining the user's initial levels of mindfulness, depression, and anxiety, the following steps are also included: The system acquires feedback data from multiple test subjects, as well as the corresponding emotional state factors of each test subject when measuring each feedback data point. The feedback data includes: anxiety score, depression score, cerebral oxygenated hemoglobin, deoxygenated hemoglobin, event-related potentials, heart rate, and respiratory rate. The emotional state factor of the test subject at the time of obtaining each feedback data point; Using feedback data as input and the corresponding emotional state factor of the test subject when measuring each feedback data point as output, the quantum hidden Markov algorithm model is trained to obtain an emotional state factor prediction model. The emotional state factor prediction model is deployed in the dynamic prediction module of the mindfulness prediction-intervention-feedback system.

9. The control method for the mindfulness prediction-intervention-feedback system according to claim 8, characterized in that, Determining the user's emotional state factors based on user feedback data, and adjusting the target training scheme based on the emotional state factors, including: User feedback data is input into the emotion state factor prediction model to obtain the user's emotion state factor; The training scheme corresponding to the interval of the user's emotional state factor is determined as the adjusted target training scheme.

10. The control method for the mindfulness prediction-intervention-feedback system according to claim 4, characterized in that, The user's real-time mindfulness level is determined according to a preset time interval, and a mindfulness level change table is constructed based on the initial mindfulness level, including: The user's real-time mindfulness level is determined according to a preset time interval. The method for determining the real-time mindfulness level is as follows: obtain the mindfulness level test paper filled out by the user in real time; compare the mindfulness level test paper filled out by the user in real time with the mindfulness level test paper with the standard answer, and obtain the mindfulness level score as the user's real-time mindfulness level. A mindfulness level variation table is constructed with test time as the horizontal axis and mindfulness level as the vertical axis; the mindfulness level includes the initial mindfulness level and the real-time mindfulness level corresponding to multiple test times.

Images