Method and system for analyzing and providing feedback on heart rate variability data in combination with virtual reality technology

Abstract

The present invention relates to the field of virtual reality technology, and discloses a method and system for analyzing and feedback of heart rate variability data combined with virtual reality technology, including: collecting the heart rate data of a tester at rest; using virtual reality technology to switch the virtual environment of the tester and collecting the heart rate data after the environment switch; in each environment, analyzing the heart rate variability data to obtain the reaction intensity of the tester; through the reaction intensity, feeding back to the virtual environment and generating characteristic parameters for the tester; through the characteristic parameters, generating a characteristic distribution of the tester on the test characteristics. By combining VR technology with heart rate variability (HRV) analysis, the physiological signals of the tester are monitored in real time, and the virtual environment is dynamically adjusted to adapt to the state of the tester. Through characteristic parameter analysis and neural network optimization, irregular signals can be identified and corrected, improving the analysis accuracy and feedback response efficiency.

Description

Technical Field

[0001] The present invention relates to the field of virtual reality technology, and specifically to a method and system for analyzing and feedback of heart rate variability data combined with virtual reality technology. Background Art

[0002] With the development of virtual reality (VR) technology, its applications in the fields of medical treatment, education, entertainment and training are becoming more and more extensive. Especially in psychological stress assessment, emotion regulation and rehabilitation training, VR provides a new way for personalized testing and intervention by creating a highly immersive virtual environment. However, traditional VR testing methods only rely on behavioral performance and subjective feedback, lacking real-time monitoring and analysis of the physiological signals of the test subjects, and unable to accurately evaluate the state changes of the test subjects in the virtual environment. Heart rate variability (HRV), as an important physiological index of the activity of the autonomic nervous system, can objectively reflect the stress level, emotional state and adaptability of the test subjects. However, the existing technologies mainly focus on the static analysis of HRV data and cannot dynamically adjust the virtual environment to meet the personalized needs of the test subjects. Therefore, combining HRV analysis with VR technology and realizing dynamic environment feedback through intelligent algorithms is an important direction of current technological development and the key to solving the above problems. Summary of the Invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the technical problems solved by the present invention are: the existing methods for analyzing heart rate variability data have great contingency in the analysis results and cannot comprehensively reflect the heart rate conditions of the test subjects, etc.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: A method for analyzing and feedback of heart rate variability data combined with virtual reality technology, including:

[0006] Collecting the heart rate data of the test subject at rest;

[0007] Using virtual reality technology to switch the virtual environment for the test subject and collecting the heart rate data after the environment switch;

[0008] Analyzing the heart rate variability data in each environment to obtain the reaction intensity of the test subject;

[0009] Feedbacking to the virtual environment through the reaction intensity and generating characteristic parameters for the test subject;

[0010] Generating a characteristic distribution of the test subject on the test characteristics through the characteristic parameters.

[0011] As a preferred embodiment of the method for analyzing and feedback of heart rate variability data combined with virtual reality technology according to the present invention, wherein: the heart rate data includes wearing a sensing device by a tester, collecting a monitoring signal through the sensing device, and converting the monitoring signal into heart rate data;

[0012] The sensing device includes ECG and PPG;

[0013] Input the monitoring signal of ECG or PPG into an embedded processing unit for data calculation:

[0014] Calculation of ECG monitoring signal: Extract the position of the R wave and calculate the consecutive R-R intervals;

[0015] Calculation of the monitoring signal of PPG: Extract the pulse peak points and calculate the pulse wave intervals;

[0016] Calculate the heart rate variability data from the heart rate data.

[0017] As a preferred embodiment of the method for analyzing and feedback of heart rate variability data combined with virtual reality technology according to the present invention, wherein: the virtual environment switching includes setting an environment sequence, and the stimulation of the environment in the sequence to the heart rate gradually increases;

[0018] When the tester uses virtual reality technology, the initial environment of the virtual environment is: the first environment in the environment sequence;

[0019] When the tester is in the current virtual environment, when the monitoring signal reaches the dynamic threshold, the virtual environment is switched to enter the next virtual environment in the environment sequence.

[0020] As a preferred embodiment of the method for analyzing and feedback of heart rate variability data combined with virtual reality technology according to the present invention, wherein: the dynamic threshold includes performing feature analysis on the monitoring signal of the tester in the current environment. If the analysis result shows that the monitoring signal is regular, it is determined that the dynamic threshold is reached; if the analysis result shows that the monitoring signal is irregular, it is determined that the dynamic threshold is not reached;

[0021] The feature analysis includes identifying the irregular features in the monitoring signal when the tester is calm and obtaining the accompanying features according to the irregular features; matching the irregular features and the accompanying features in the monitoring signal in the current virtual environment; the specific steps include:

[0022] Using a pre-trained neural network algorithm, three-step recognition is completed; in the first step, the regular part in the monitoring signal is recognized; in the second step, the part that does not conform to the regularity in the regular part is recognized; and all the parts recognized as not conforming to the regularity are classified; in the third step, the type of each part that does not conform to the regularity is recognized The feature that appears simultaneously with the regular part of the monitoring signal when it appears For And Are matched to obtain the associated feature matched with the type of each part that does not conform to the regularity, denoted as ;

[0023] After the tester enters the virtual environment, the waveform in the monitoring signal with a similarity greater than the preset value to Is corrected: the part that does not conform to the regularity is removed, and only the removed associated feature is retained;

[0024] The corrected monitoring signal is recognized using the neural network algorithm. After the monitoring signal shows regularity, after a continuous sampling time T, the original monitoring signal corresponding to the regular part is output; and the new In the corrected monitoring signal is recognized to supplement the set E; after the output is completed, it is determined whether the dynamic threshold is reached;

[0025] Wherein, Represents the pairing set jointly composed of the type of each part that does not conform to the regularity and its matched associated feature; Represents the pairing combination composed of the type of the i-th part that does not conform to the regularity and its matched associated feature; Represents the i-th type recognized as not conforming to the regularity in the environment; Represents The associated feature of; the irregular feature includes the irregular part that appears in the regular monitoring signal of the tester; the associated feature includes the feature that appears simultaneously in the regular part when the irregular feature occurs.

[0026] As a preferred scheme of the heart rate variability data analysis and feedback method combining virtual reality technology according to the present invention, wherein: the reaction intensity includes obtaining corresponding heart rate variability data according to the original monitoring signal;

[0027] Let the heart rate variability data be , representing the heart rate variability data in the n-th environment;

[0028] When the tester is calm and has not entered the environment, denoted as ;

[0029] For the change situation of the heart rate variability data before and after each environment switch, compare it with the standard change situation, and give feedback on the virtual environment according to the comparison result to achieve the jump of the virtual environment;

[0030] The change situation of the heart rate variability data includes, for each , calculate the difference from , and denote it as ;

[0031] The standard change situation includes, according to the historical records of the database, calculate the difference of the heart rate variability data between every two environments to obtain the standard value of the heart rate variability data between every two environments.

[0032] As a preferred solution of the heart rate variability data analysis and feedback method combining virtual reality technology according to the present invention, wherein: the feedback on the virtual environment includes that the change situations of the heart rate variability data for consecutive m times are all less than the standard change situation, and for consecutive m times, all satisfy When, trigger the feedback mechanism to jump the virtual environment, the number of jumped scenarios is R, and directly jump from the current nth environment to the postponed (n + R)th environment;

[0033] Among them, represents The corresponding standard change situation; represents that for consecutive m times, when the change situations of the heart rate variability data are all less than the standard change situation, the average value of the change situations of the m heart rate variability data; represents that for consecutive m times, during the process that the change situations of the heart rate variability data are all less than the standard change situation, the maximum value of the standard change situation.

[0034] As a preferred solution of the heart rate variability data analysis and feedback method combining virtual reality technology according to the present invention, wherein: the characteristic parameters include that the tester obtains the heart rate variability data in each virtual environment, matches the heart rate variability data with the corresponding virtual environment, and records the matching result and ;

[0035] For each virtual environment visited by the tester, respectively output the corresponding and , to obtain the set of characteristic parameters visited ;

[0036] Using the trained Bayesian network, the output is U, which is the probability distribution of the test features.

[0037] Among them, represents the specific environment corresponding to the nth environment, and N represents the number of virtual environments visited by the tester.

[0038] The test features include the preset labels used as target values during the training of the Bayesian network.

[0039] A heart rate variability data analysis and feedback system combined with virtual reality technology using the method described in the present invention, wherein:

[0040] The acquisition unit acquires the heart rate data of the tester when calm.

[0041] The simulation unit uses virtual reality technology to switch the virtual environment for the tester and acquires the heart rate data after the environment switch.

[0042] The analysis unit analyzes the heart rate variability data in each environment to obtain the reaction intensity of the tester.

[0043] The feedback unit feeds back the virtual environment through the reaction intensity and generates characteristic parameters for the tester.

[0044] The output unit generates the characteristic distribution of the tester on the test features through the characteristic parameters.

[0045] A computer device includes: a memory and a processor; the memory stores a computer program, wherein: when the processor executes the computer program, the steps of the method described in any one of the present invention are implemented.

[0046] A computer-readable storage medium stores a computer program thereon, wherein: when the computer program is executed by a processor, the steps of the method described in any one of the present invention are implemented.

[0047] The beneficial effects of the present invention: By combining VR technology with heart rate variability (HRV) analysis, the present invention can monitor the physiological signals of the tester in real time and dynamically adjust the virtual environment to adapt to the tester's state. Through characteristic parameter analysis and neural network optimization, irregular signals can be identified and corrected, improving the analysis accuracy and feedback response efficiency. The system realizes personalized scene adaptation, optimizes the user experience, and is applicable to fields such as psychological assessment, emotion regulation, and rehabilitation training. Description of the Drawings

[0048] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0049] Figure 1 This is the overall flowchart of the heart rate variability data analysis and feedback method combined with virtual reality technology provided by the first embodiment of the present invention. Specific embodiments

[0050] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the specific embodiments of the present invention in detail with reference to the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0051] Example 1, referring to Figure 1 This is an embodiment of the present invention, providing a heart rate variability data analysis and feedback method combined with virtual reality technology, including:

[0052] S1: Collect the heart rate data of the tester at rest.

[0053] Furthermore, the heart rate data includes wearing a sensing device on the tester, collecting monitoring signals through the sensing device, and converting the monitoring signals into heart rate data.

[0054] The sensing device includes ECG and PPG. Input the monitoring signals of ECG or PPG into the embedded processing unit for data calculation:

[0055] Calculation of ECG monitoring signals: Extract the R-wave position and calculate the consecutive R-R intervals.

[0056] Calculation of PPG monitoring signals: Extract the pulse peak points and calculate the pulse wave intervals.

[0057] Calculate the heart rate variability data from the heart rate data. Heart rate variability data (HRV) is a set of characteristic values calculated based on heart rate data (such as R-R intervals or pulse wave intervals), which can reflect the activities of the autonomic nervous system and cardiovascular health status. HRV data includes time-domain analysis, frequency-domain analysis, and non-linear analysis.

[0058] Time-domain analysis reflects the overall level and short-term changes of heart rate variability by statistically analyzing the characteristics of the heart beat interval time series. Main indicators:

[0059] SDNN (Standard Deviation): Represents the standard deviation of all heartbeat intervals and reflects the overall level of heart rate variability.

[0060] RMSSD (Root Mean Square of the Successive Differences): Represents the root mean square of the differences between adjacent heartbeat intervals and reflects parasympathetic nerve activity.

[0061] pNN50: Represents the proportion of differences between adjacent heartbeat intervals greater than 50 ms and reflects short-term fluctuations.

[0062] AVNN (Average Heartbeat Interval): Represents the average of all heartbeat intervals and is used to evaluate the overall heart rate.

[0063] HR (Heart Rate): The number of heartbeats per minute calculated based on the heartbeat intervals.

[0064] Frequency domain analysis reveals the dynamic balance between the sympathetic and parasympathetic nerves by performing spectral decomposition on the time series of heartbeat intervals and analyzing the power of different frequency components. Main indicators:

[0065] VLF (Very Low Frequency Power): Frequency range: 0.003 - 0.04 Hz.

[0066] May be related to thermoregulation or other slow physiological fluctuations.

[0067] LF (Low Frequency Power): Frequency range: 0.04 - 0.15 Hz.

[0068] Reflects the combined activity of the sympathetic and parasympathetic nerves.

[0069] HF (High Frequency Power): Frequency range: 0.15 - 0.4 Hz.

[0070] Primarily controlled by the parasympathetic nerve and reflects the relaxed state.

[0071] TP (Total Power): The total spectral power, representing the sum of variations in all frequency bands.

[0072] LF / HF Ratio: Represents the balance state between the sympathetic and parasympathetic nerves.

[0073] Nonlinear analysis focuses on the complexity, dynamics, and nonlinear characteristics of the heart rate variability time series and can reflect complex physiological regulation mechanisms.

[0074] Main indicators:

[0075] Approximate Entropy (ApEn): Measures the complexity of the time series, and a higher value indicates more complex fluctuations.

[0076] Sample Entropy (SampEn): Measures the irregularity of the time series and is more robust to short time series.

[0077] Poincaré diagram features: SD1: Reflects short-term fluctuations in heart rate (instantaneous variability).

[0078] SD2: Reflects long-term fluctuations in heart rate (overall variability).

[0079] Detrended fluctuation analysis (DFA): Evaluates the self-similarity and long-term correlation of time series.

[0080] Lyapunov exponent: Measures the sensitivity of time series and reflects the dynamic stability of signals.

[0081] S2: Using virtual reality technology, the tester is switched to a virtual environment, and the heart rate data after the environment switch is collected. In each environment, the heart rate variability data is analyzed to obtain the reaction intensity of the tester. Through the reaction intensity, feedback is provided to the virtual environment, and characteristic parameters for the tester are generated.

[0082] Furthermore, the virtual environment switch includes setting an environment sequence, and the stimuli of the environments in the sequence to the heart rate gradually increase. When the tester uses virtual reality technology, the initial environment of the virtual environment is the first environment in the environment sequence. When the monitoring signal reaches the dynamic threshold in the current virtual environment, the tester switches to the next virtual environment in the environment sequence.

[0083] For the existing technology, the environment switch is measured by a time threshold. This is not suitable for content with large fluctuations such as heart rate. Using a dynamic threshold for improvement, the dynamic threshold includes performing characteristic analysis on the monitoring signal of the tester in the current environment. If the analysis result shows that the monitoring signal is regular, it is determined that the dynamic threshold is reached; if the analysis result shows that the monitoring signal is not regular, it is determined that the dynamic threshold is not reached.

[0084] The characteristic analysis includes identifying the irregular features in the monitoring signal when the tester is calm, and obtaining accompanying features based on the irregular features; matching the irregular features and the accompanying features in the monitoring signal in the current virtual environment; the specific steps include:

[0085] Using a pre-trained neural network algorithm (this algorithm is a simple recognition algorithm, which can be a simplified version of the neural network because only "special" recognition is required. That is, the irregular components mixed in the regularity of the monitoring data) to complete three-step recognition; the first step is to identify the regular part in the monitoring signal; the second step is to identify the part that does not conform to the regularity in the regular part and classify all the parts identified as not conforming to the regularity; the third step is to identify the type of each part that does not conform to the regularity When it appears, the characteristic that the regular part of the monitoring signal appears simultaneously , for and perform matching to obtain the type of each part that does not conform to the regular part and its matching accompanying characteristics, denoted as .

[0086] After the tester enters the virtual environment, correct the waveforms in the monitoring signal whose similarity to is greater than the preset value: remove the parts that do not conform to the regularity, and only retain the accompanying characteristics of the removed parts. Use the neural network algorithm to identify the corrected monitoring signal. After the monitoring signal shows regularity, after a continuous sampling time T, output the original monitoring signal corresponding to the regular part (the signal without being removed, corresponding to the start and end moments of the output signal in the time series); and identify the new in the corrected monitoring signal, and supplement the set E; after the output is completed, then judge whether the dynamic threshold is reached.

[0087] It should be noted that although a neural network (here, LSTM or a convolutional neural network can be used) can achieve identification, the identification period is a bit long. For the switching of multiple environments, if each environment takes too long, it is not good for the experience. Therefore, by identifying the "irregular" parts that occur concomitantly, which are actually the mutated parts, for these parts, if there are signs of occurrence in the regular part, then it can be fully understood as a part of the regularity. In this way, both fast identification can be completed, and the computational amount of the neural network can be reduced, so as to adapt to a simplified version of the neural network.

[0088] Among them, represents the pairing set composed of the type of each part that does not conform to the regular part and its matching accompanying characteristics; represents the pairing combination composed of the type of the i-th part that does not conform to the regular part and its matching accompanying characteristics; represents the i-th type identified as not conforming to the regular part in the environment; represents 's accompanying characteristics; the irregular characteristics include the irregular parts that appear in the regular monitoring signal of the tester; the accompanying characteristics include the characteristics that appear simultaneously in the regular part when the irregular characteristics occur. For example, in the regular part, when the value is greater than a certain value or less than a certain value (this value still belongs to the category of the regular part), an irregular characteristic will follow. Or when the peak value of the PPG monitoring signal reaches a certain value, the accompanying special situation. Therefore, this technical means has different meanings for ECG and PPG. For the detection signal of ECG.

[0089] It should be noted that in the monitoring signal, due to physiological or environmental interference factors, the data may contain irregular components. These components will affect the analysis of the regular part of the signal, thereby reducing the accuracy of the virtual environment feedback. Through the following steps: The first step: Extract the regular part of the signal and separate the main components that conform to the rules in the original signal. The second step: Identify the irregular components in the regular part, classify them, and clarify their sources or characteristics. The third step: Match the irregular components with the regular features, extract the associated information, and ensure that the processing of the abnormal part does not affect the integrity of the regular signal. This design ensures that when analyzing the signal, the irregular components will not mislead the judgment of the regular data, improving the accuracy of signal processing. Through the classification of the irregular part and the matching of the accompanying features: clarify the type of irregularity: such as sudden anomalies, gradual drifts, spike fluctuations, etc. Identify its accompanying features: for example, when the irregularity appears, the change pattern of the regular signal (such as the associated changes in amplitude and frequency). This classification and matching help to construct a refined processing scheme for the irregular signal, avoiding simply removing the irregular part and losing important associated information. As the data accumulates, the system gradually increases its understanding of the types of irregularities and their associated features, improving the recognition ability. The rich feature set E provides high-quality data for the retraining of the subsequent model, continuously enhancing the learning ability and feedback accuracy of the system.

[0090] Finally, based on the process of recognition, correction, and dynamic threshold judgment: monitoring signal → regular recognition → correction → dynamic threshold judgment → environmental adjustment. Reduce the stay time of the tester in a single environment and avoid repeated or ineffective tests.

[0091] The reaction intensity includes obtaining corresponding heart rate variability data according to the original monitoring signal. Let the heart rate variability data be , representing the heart rate variability data in the nth environment. When the tester is calm and has not entered the environment, it is recorded as . Compare the change situation of the heart rate variability data before and after each environment switch with the standard change situation, and give feedback on the virtual environment according to the comparison result to achieve the jump of the virtual environment.

[0092] The change situation of the heart rate variability data includes, for each , calculate the difference from , and record it as . The standard change situation includes calculating the difference in heart rate variability data between every two environments according to the historical records in the database to obtain the standard value of the heart rate variability data between every two environments.

[0093] Furthermore, providing feedback to the virtual environment includes that the change situation of the heart rate variability data for consecutive m times is less than the standard change situation, and for consecutive m times, it satisfies When this occurs, trigger the feedback mechanism to jump to the virtual environment. The number of scenes to jump to is R, and directly jump from the current nth environment to the postponed (n + R)th environment.

[0094] Wherein, represents the corresponding standard change situation; represents that for consecutive m times, when the change situation of the heart rate variability data is less than the standard change situation, the average value of the change situations of the m heart rate variability data; represents the maximum value of the standard change situation during the process that for consecutive m times, the change situation of the heart rate variability data is less than the standard change situation.

[0095] It should be noted that by comparing the actual change value of HRV with the historical standard in the database, it is judged whether the current environment has an expected stimulating effect on the tester. The change of single - time HRV data may be affected by external noise or short - term fluctuations, making it difficult to accurately reflect the overall state of the tester. Therefore, a judgment mechanism for consecutive m times is designed: when the tester switches among multiple consecutive environments and the HRV change situation is less than the standard change value, it indicates that the stimulation of the current environment on the tester is gradually weakening. By comparing the average value of the HRV change situations for consecutive m times with the maximum value of the standard change, the effectiveness of the environmental stimulation is further confirmed. Through the judgment of consecutive m - time changes, short - term abnormal fluctuations are filtered out, enhancing the stability and scientific nature of the judgment of the tester's physiological state changes. In virtual environment testing, staying in an ineffective or overly stressful scene for too long may cause the tester to experience excessive fatigue or emotional stress. Through the real - time monitoring of HRV changes and the jump mechanism, it is ensured that the tester remains safe and comfortable throughout the testing process.

[0096] S3: Generate the characteristic distribution of the tester on the test characteristics through the characteristic parameters.

[0097] Furthermore, the characteristic parameters include that the tester obtains the heart rate variability data in each virtual environment, matches the heart rate variability data with the corresponding virtual environment, and records the matching result and . For each virtual environment visited by the tester, respectively output the corresponding and , and obtain the set of characteristic parameters of the visited virtual environments . Using the trained Bayesian network, the output is U, and the output is the probability distribution of the test characteristics.

[0098] wherein, represents the specific environment corresponding to the nth environment, and N represents the number of virtual environments traveled by the tester. The test features include preset labels used as target values during the training of the Bayesian network.

[0099] By analyzing the changes in the physiological state of the tester in different virtual environments, comprehensively evaluate their physiological adaptability, emotional fluctuations, and recovery ability. As the tester travels through the virtual environment, the system can update the set of feature parameters in real time, dynamically reflecting their physiological and psychological states.

[0100] During the training stage of the Bayesian network, set preset labels for the target values (such as high adaptability, low adaptability). The system optimizes the feature distribution and model parameters by comparing the actual output with the target value. The system can perform real-time and intelligent evaluation of the physiological and psychological states of the tester. By quickly judging the state of the tester, timely adjust the design of the virtual environment to improve the user experience.

[0101] The labels of the above target values are generally preset during the training stage and are preset according to the test requirements. They are designed according to the test requirements and scenarios and can reflect the physiological, psychological, and behavioral characteristics of the tester in multiple dimensions and at multiple levels. Examples of common target label contents:

[0102] (1) Single label: The state of the tester in the virtual environment is reflected in a single dimension.

[0103] Adaptability (high / medium / low)

[0104] Stress level (high / medium / low)

[0105] Recovery ability (fast / medium / slow)

[0106] (2) Composite label: The state of the tester is described by a composite label of multiple dimensions.

[0107] Example:

[0108] Label = (Adaptability: high, Stress: low, Recovery ability: fast)

[0109] Label = (Adaptability: high, Stress: low, Recovery ability: fast)

[0110] Label = (Task performance: average, Emotion: tense)

[0111] Label = (Task performance: average, Emotion: tense)

[0112] (3) Continuous label: The target value label can be a continuous value, reflecting the intensity or degree of the state.

[0113] Adaptability Score: A score within the range of [0, 1]. The higher the value, the stronger the adaptability.

[0114] Stress Index: The stress score calculated through HRV.

[0115] Recovery Time: Measured in seconds or minutes.

[0116] Label Example:

[0117] Stress Test Scenario:

[0118] Label: Stress Response.

[0119] High Stress: The HRV index drops by more than 50%, and the LF / HF ratio increases significantly.

[0120] Medium Stress: The HRV index drops by 20% - 50%, and the LF / HF ratio increases slightly.

[0121] Low Stress: The change in HRV is small, and the LF / HF ratio remains balanced.

[0122] Rehabilitation Training Scenario:

[0123] Label: Recovery Ability.

[0124] Quick Recovery: The heart rate variability index returns to the baseline value within 2 minutes.

[0125] Medium Recovery: The index recovery time is 2 - 5 minutes.

[0126] Slow Recovery: It has not recovered after more than 5 minutes.

[0127] Virtual Training Scenario:

[0128] Label: Adaptability.

[0129] High Adaptability: HRV is stable, and the task completion efficiency is high.

[0130] Medium Adaptability: HRV fluctuates, and the task completion efficiency is moderate.

[0131] Low Adaptability: HRV fluctuates violently, and the task completion efficiency is low.

[0132] The design of the target value labels is the key to training the Bayesian network, which can cover multiple dimensions such as adaptability, stress response, and recovery ability. The specific content should be flexibly preset according to the test requirements and scenarios. These labels not only support the effective learning of the model but also provide important basis for the optimization of the virtual environment and the improvement of user experience.

[0133] On the other hand, this embodiment also provides a heart rate variability data analysis and feedback system combined with virtual reality technology, which includes:

[0134] A collection unit that collects the heart rate data of the tester when at rest.

[0135] A simulation unit that uses virtual reality technology to switch the virtual environment for the tester and collects the heart rate data after the environment switch.

[0136] An analysis unit that analyzes the heart rate variability data in each environment to obtain the reaction intensity of the tester.

[0137] A feedback unit that provides feedback on the virtual environment based on the reaction intensity and generates characteristic parameters for the tester.

[0138] An output unit that generates a characteristic distribution of the tester in terms of test characteristics based on the characteristic parameters.

[0139] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, etc., all kinds of media that can store program codes.

[0140] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0141] More specific examples (nonexhaustive list) of computer-readable media include the following: electrical connections (electronic devices) with one or more wirings, portable computer disk cartridges (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber devices, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or, if necessary, other suitable processing, and then stored in a computer memory.

[0142] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGA), field-programmable gate arrays (FPGA), etc.

[0143] Example 2, an embodiment of the present invention, provides a method for analyzing and feedback of heart rate variability data combined with virtual reality technology. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0144] The goal of the experiment is to evaluate the improvements of the method of the present invention in terms of test efficiency, accuracy, and user experience through key parameters such as total test time, test accuracy score, and user experience score.

[0145] The experiment selected 45 volunteers, all healthy adults, divided into three groups, with 15 people in each group.

[0146] Experimental group (the method group of the present invention): The method for analyzing and feedback of heart rate variability data of the present invention is adopted, and the virtual environment switching is adjusted in real time through dynamic thresholds.

[0147] Control group A (fixed-time group): Each virtual environment is switched after staying for 2 minutes, without a feedback mechanism.

[0148] Control group B (manual operation group): The tester switches the scene through personal subjective judgment and operating the device.

[0149] Experimental equipment and environment:

[0150] Five virtual scenarios were set up for the experiment, namely the quiet scenario (E1), the low-stress scenario (E2), the medium-stress scenario (E3), the high-stress scenario (E4), and the extremely high-stress scenario (E5). The stimulation of the scenarios gradually increases.

[0151] All testers wore ECG and PPG sensing devices to collect heart rate variability data for dynamic analysis, and recorded the switching time, total test duration, and user experience.

[0152] Experimental procedure:

[0153] Baseline data collection: Testers collected baseline data in the initial sitting state.

[0154] Virtual environment test:

[0155] Experimental group: The dynamic feedback mechanism automatically switches scenarios, and enters the next scenario when the heart rate variability data reaches the dynamic threshold.

[0156] Control group A: Switches the environment at a fixed time (every 2 minutes), regardless of the physiological data of the testers.

[0157] Control group B: The operator subjectively operates the device and switches to the next scenario that they think is suitable.

[0158] Data recording: Record the total test time, switching accuracy score, and user experience score of the three groups of testers.

[0159] Table 1 Data recording form

[0160]

[0161] By comparing the experimental data of the total test time, test accuracy, and user experience score, it can be seen that the method of the present invention is significantly superior to the prior art in terms of test efficiency, accuracy, and user experience.

[0162] The total test time of the experimental group was 480 seconds, significantly shorter than that of the fixed-time group (600 seconds) and the manual operation group (590 seconds). The dynamic feedback mechanism enables the experimental group to switch the virtual environment in a timely manner according to the changes in the physiological state of the testers, avoiding wasting time in ineffective scenarios. Compared with the fixed-time group, the method of the present invention reduces the test time by 20%, significantly improving the test efficiency.

[0163] The accuracy score of the experimental group was 95.2%, close to that of the manual operation group (91.7%), and significantly higher than that of the fixed-time group (80.3%). The accuracy of the fixed-time group was relatively low because it could not respond to the physiological state of the testers in real time, resulting in poor scenario switching timing. The method of the present invention can accurately capture the state changes of the testers during switching through dynamic threshold adjustment, reducing errors caused by premature or late switching.

[0164] The user experience score of the experimental group was 8.8, significantly higher than that of the fixed-time group (6.5) and the manual operation group (7.0). User feedback indicates that the dynamic feedback mechanism of the experimental group brought a smoother testing experience, and the timing of scene switching was more natural and in line with the physiological state of the testers. The fixed-time group had a poor experience because the switching was rigid and could not respond to the actual state of the testers. The user experience score of the manual operation group was also lower than that of the experimental group because the frequent manual switching increased the operation burden, causing some testers to feel fatigued.

[0165] The experimental results fully prove that the method of the present invention has significant advantages in shortening the total testing time, improving testing accuracy, and enhancing user experience, demonstrating outstanding innovation and practical value compared with the prior art. Through the dynamic feedback mechanism, the present invention realizes personalized scene adjustment, optimizes the overall process of virtual environment testing, and provides an efficient and scientific solution for practical applications.

[0166] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not restrictive. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A heart rate variability data analysis and feedback method combined with virtual reality technology, characterized in that: include: Collect the tester's heart rate data when he is calm; Using virtual reality technology, the tester switches the virtual environment and collects the heart rate data after the environment switches; In each environment, the heart rate variability data were analyzed to obtain the intensity of the test subject's response; Through the reaction intensity, the virtual environment is fed back and characteristic parameters for the tester are generated; Generate a characteristic distribution of the test subject on the test characteristic through the characteristic parameters; The heart rate data includes collecting monitoring signals by wearing a sensor device on the tester and converting the monitoring signals into heart rate data; The virtual environment switching includes setting an environment sequence, wherein the stimulation of the environment to the heart rate gradually increases; When the tester uses virtual reality technology, the initial environment of the virtual environment is: the first environment in the environment sequence; When the monitoring signal reaches a dynamic threshold in the current virtual environment, the tester switches the virtual environment to the next virtual environment in the environment sequence; The dynamic threshold includes performing feature analysis on the monitoring signal of the tester in the current environment, and if the analysis result shows that the monitoring signal presents regularity, then judging that the dynamic threshold is reached; If the analysis result shows that the monitoring signal does not show regularity, it is determined that the dynamic threshold is not reached; The feature analysis includes identifying irregular features in the monitoring signal when the test subject is calm, and obtaining accompanying features based on the irregular features; Matching the irregular features and the accompanying features with the monitoring signals in the current virtual environment; the specific steps include: Using the pre-trained neural network algorithm, a three-step recognition is completed; the first step is to identify the regular parts of the monitoring signal; the second step is to identify the parts that do not conform to the regularity in the regular parts; and classify all the parts that are identified as not conforming to the regularity; the third step is to identify the type of each part that does not conform to the regularity When the monitoring signal appears, the regular part of the monitoring signal appears at the same time ,right and Matching is performed to obtain the accompanying features of each type that does not conform to the regularity part and its matching, recorded as ; After the tester enters the virtual environment, the monitoring signal is The waveforms whose similarity is greater than a preset value are corrected: the parts that do not conform to the regularity are eliminated, and only the accompanying features of the eliminated parts are retained; The modified monitoring signal is identified by using the neural network algorithm. After the monitoring signal shows regularity, after continuous sampling time T, the original monitoring signal corresponding to the regular part is output; and the new , supplement the set E; after the output is completed, it is determined that the dynamic threshold is reached; in, It represents a pairing set consisting of each type that does not conform to the regularity and its accompanying features that match it; It represents the paired combination consisting of the type of the i-th part that does not conform to the regularity and its matching accompanying feature; It indicates the i-th type in the environment that is identified as not conforming to the regularity; express The accompanying characteristics; the irregular characteristics include the irregular parts that appear in the regular monitoring signal of the tester; the accompanying characteristics include the characteristics that appear simultaneously in the regular part when the irregular characteristics occur.

2. The heart rate variability data analysis and feedback method combined with virtual reality technology as claimed in claim 1, characterized in that: The sensing devices include ECG and PPG; The ECG or PPG monitoring signal is input into the embedded processing unit for data calculation: Calculation of ECG monitoring signals: extract R wave position and calculate continuous RR intervals; Calculation of PPG monitoring signal: extract pulse peak point and calculate pulse wave interval; The heart rate data is calculated to obtain heart rate variability data.

3. The heart rate variability data analysis and feedback method combined with virtual reality technology as claimed in claim 2, characterized in that: The reaction intensity includes obtaining corresponding heart rate variability data according to the original monitoring signal; Assume that the heart rate variability data is , represents the heart rate variability data in the nth environment; When the tester is calm and does not enter the environment, it is recorded as ; Comparing the changes in the heart rate variability data before and after each environment switch with the standard changes, and providing feedback to the virtual environment based on the comparison results to achieve the jump of the virtual environment; The changes in the heart rate variability data include: , calculation and The difference is recorded as ; The change of the standard includes calculating the difference of the heart rate variability data between every two environments according to the historical records of the database to obtain the standard value of the heart rate variability data between every two environments.

4. The heart rate variability data analysis and feedback method combined with virtual reality technology as claimed in claim 3, characterized in that: The feedback to the virtual environment includes that the change of the heart rate variability data is less than the change of the standard for m consecutive times, and for m consecutive times, it is satisfied When , the feedback mechanism is triggered to jump to the virtual environment. The number of scenes jumped is R, directly jumping from the current nth environment to the delayed n+Rth environment; in, express The corresponding changes in the standards; It represents the average value of the changes of the m heart rate variability data when the changes of the heart rate variability data are all less than the changes of the standard for m consecutive times; It indicates that the maximum value of the standard change is obtained when the change of the heart rate variability data is smaller than the change of the standard for m consecutive times.

5. The heart rate variability data analysis and feedback method combined with virtual reality technology as claimed in claim 4, characterized in that: The characteristic parameters include: the tester obtains the heart rate variability data in each virtual environment, matches the heart rate variability data with the corresponding virtual environment, and records the matching results. and ; For each virtual environment that the tester has visited, output the corresponding and , get the set of characteristic parameters that have been traveled ; Using the trained Bayesian network, the output is U, and the output is the probability distribution of the test feature; in, represents the specific environment corresponding to the nth environment, and N represents the number of virtual environments that the tester has visited; The test features include preset labels used as target values ​​when training the Bayesian network.

6. A heart rate variability data analysis and feedback system using the method according to any one of claims 1 to 5 combined with virtual reality technology, characterized in that: A collection unit collects the heart rate data of the tester when he is calm; The simulation unit uses virtual reality technology to switch the virtual environment of the test subject and collect the heart rate data after the environment is switched; The analysis unit analyzes the heart rate variability data in each environment to obtain the test subject's reaction intensity; A feedback unit, which provides feedback to the virtual environment through the reaction intensity and generates characteristic parameters for the tester; The output unit generates a feature distribution of the test subject on the test feature through the feature parameters.

7. A computer device comprising: Memory and processor; The memory stores a computer program, wherein the processor implements the steps of any one of the methods of claims 1-5 when executing the computer program.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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