A method and system for analyzing psychological stress based on heart rate and respiration

Abstract

The application provides a psychological stress analysis method and system based on heart rate and respiration, and relates to the technical field of mental health care.The method collects user respiration signals and heart rate signals in real time, extracts respiration cycle data and heart rate variability data, and performs synchronous correlation analysis to reveal the interaction between the autonomic nervous system and the respiration rhythm.The system maps the heart rate variability data to a color control parameter in a virtual reality environment, and maps the respiration cycle data to a motion control parameter to form a multi-sensory biological feedback.In a dual-user mode, the light domain fusion effect is triggered by monitoring the respiration phase difference and the motion trajectory coincidence degree, color mixing and motion trajectory cooperation are realized, and the training level difficulty is dynamically adjusted in combination with the trigger frequency.The application solves the problems of single signal fragmentation, lack of social interaction and insufficient dynamic adaptation capability, and improves the immersion, compliance and individualized adaptation capability of breast cancer patient rehabilitation training.

Description

Technical Field

[0001] This invention relates to the field of mental health technology, and in particular to a method and system for analyzing psychological stress based on heart rate and respiration. Background Technology

[0002] In the postoperative rehabilitation of breast cancer patients, physical symptoms (such as fatigue, insomnia, and pain) are intertwined with psychological stress. While traditional biofeedback technology can assist in relaxation training, it suffers from the following problems: First, lack of targeted design: existing systems do not address the specific needs of breast cancer patients after surgery, such as limited upper limb movement and decreased respiratory function, and cannot effectively alleviate somatic symptoms. Second, single feedback mechanism: traditional methods rely on single physiological signals (such as heart rate or respiration) for audio-visual cues, making it difficult for users to perceive changes in their physical and mental state through multi-sensory interaction, resulting in low training compliance. Third, lack of multi-person collaboration mode: existing technologies only support single-person training, while breast cancer patients have a strong need for emotional support and urgently require socialized training to enhance their rehabilitation motivation. Fourth, insufficient dynamic adjustment capability: traditional systems use fixed difficulty thresholds and cannot adjust the training intensity according to the user's real-time physiological state, making it difficult to adapt to individualized rehabilitation pathways. Summary of the Invention

[0003] To address the aforementioned problems in existing technologies, the first aspect of this invention proposes a method for analyzing psychological stress based on heart rate and respiration, comprising: Step 1: Real-time acquisition of the user's heart rate and respiratory signals; Step 2: Use the data processing module to extract heart rate variability data from the heart rate signal and respiratory cycle data from the respiratory signal, and perform synchronous correlation analysis on the heart rate variability data and respiratory cycle data. Step 3: Generate color control parameters based on heart rate variability data and motion control parameters based on respiratory cycle data. Input the color control parameters and motion control parameters into the virtual reality system to control the visual elements in the virtual reality system to change color and adjust motion status. Step 4: In dual-user mode, the color control parameters and motion control parameters of the two users are acquired synchronously. When the phase difference of the change in the breathing cycle data of the two users is less than the preset phase difference threshold, the light domain fusion effect is triggered in the virtual reality system. The light domain fusion effect is achieved by the color mixing of the visual elements of the two users and the overlap of their motion trajectories. Step 5: Dynamically adjust the difficulty parameters of the training levels in the virtual reality system based on the number of times and duration of the light field fusion effect.

[0004] In conjunction with the first aspect, in some implementations, in step 1, a flexible strain sensor is used to collect respiratory signals. The flexible strain sensor is set in a wearable breathing belt to detect the expansion and contraction of the user's abdomen and convert it into a resistance change signal. The resistance change signal is then transmitted to the data processing module to generate respiratory cycle data.

[0005] In conjunction with the first aspect, in some implementations, in step 1, heart rate signals are acquired by a photoplethysmography (PPG) sensor. The PPG sensor is placed on the user's wrist or finger and generates a pulse wave signal based on the periodic changes in blood volume. Peak detection is performed on the pulse wave signal to extract the RR interval sequence, and heart rate variability data is calculated based on the RR interval sequence.

[0006] In conjunction with the first aspect, in some implementations, in step 2, the data processing module receives heart rate and respiratory signals through a wireless communication network. The wireless communication network includes a low-latency transmission protocol and a high-reliability transmission protocol. The low-latency transmission protocol is used to transmit respiratory cycle data in real time, and the high-reliability transmission protocol is used to synchronize heart rate variability data.

[0007] In conjunction with the first aspect, in some implementation methods, step 3, adjusting the motion state of the visual elements includes: The rate of ascent of visual elements is generated based on the duration of the inspiratory phase in the respiratory cycle data. The rate of visual element descent is generated based on the duration of the expiratory phase in the respiratory cycle data. The ascent and descent rates are input into the virtual reality system, driving visual elements to alternately rise and fall with each breathing phase.

[0008] In conjunction with the first aspect, in some implementations, step 3, the generation of color control parameters includes: Extract the root mean square difference of consecutive heartbeat intervals from heart rate variability data; Based on the numerical range of the root mean square error, it is mapped to a linear gradient parameter from cool to warm tones; When the root mean square error is lower than the preset root mean square error threshold, the linear gradient parameter transitions from blue to red.

[0009] In conjunction with the first aspect, in some implementation methods, step 3, the adjustment of the motion state of visual elements includes the coordinated mapping of breathing and yoga movements, specifically including: Yoga movement data is generated by detecting the user's arm extension angle and movement trajectory; The yoga movement data is time-synchronized and matched with the duration of the inhalation and exhalation phases in the breathing cycle data to generate collaborative control instructions that include vertical displacement parameters and transparency parameters. Based on the vertical displacement parameters, the outer particle ribbons that drive the visual elements in the virtual reality system rise during the inhalation phase and fall during the exhalation phase. Based on the duration of the expiratory phase in the respiratory cycle data, a transparency parameter for the particle streamer is generated. The longer the expiratory phase, the lower the transparency parameter. In dual-user mode, when the overlap of the yoga movement data of the two users exceeds a preset overlap threshold and the phase difference of the breathing cycle data is less than the phase difference threshold, a Möbius ring light flow effect is generated around the two users in the virtual reality system.

[0010] In conjunction with the first aspect, in some implementations, step 4, the triggering of the light domain blending effect includes: Map the color control parameters of the first user to the first light gamut color, and map the color control parameters of the second user to the second light gamut color; Based on the inspiratory phase of the respiratory cycle data, the light domain contraction velocity of the first and second users is generated; based on the expiratory phase of the respiratory cycle data, the light domain expansion velocity of the first and second users is generated. When the phase difference between the light domain contraction speed of the first user and the second user, or the phase difference between the light domain expansion speed of the first user and the second user, is less than a preset phase difference threshold, the light domain fusion effect is triggered, and the color of the first light domain and the color of the second light domain are mixed into a dynamic gradient color wheel.

[0011] In conjunction with the first aspect, in some implementation methods, the dynamic adjustment of the difficulty parameter in step 5 includes: The number of times the light domain fusion effect is triggered per unit time is counted, and the duration of each light domain fusion effect is recorded; Based on the number of triggers and the duration, the effective synchronization index per unit time is calculated. The effective synchronization index is positively correlated with the number of triggers and positively correlated with the average duration. When the effective synchronization index is higher than the first preset threshold, the target value of the expiratory duration in the respiratory cycle data is increased. When the effective synchronization index is lower than the second preset threshold, the playback frequency of the breathing guidance audio is superimposed in the virtual reality system.

[0012] Secondly, the present invention provides a psychological stress analysis system based on heart rate and respiration, the system employing the method provided in any of the above embodiments, the system comprising: The signal acquisition module is used to acquire the user's heart rate and respiratory signals in real time; The data processing module, connected to the signal acquisition module, is used to extract heart rate variability data from heart rate signals, extract respiratory cycle data from respiratory signals, and perform synchronous correlation analysis on heart rate variability data and respiratory cycle data. The virtual reality control module, connected to the data processing module, is used to generate color control parameters based on heart rate variability data and motion control parameters based on respiratory cycle data. The color control parameters and motion control parameters are then input into the virtual reality system to control the visual elements in the virtual reality system to change color and adjust motion status. The dual-user interaction module is connected to the virtual reality control module and is used to synchronously acquire the color control parameters and motion control parameters of two users in dual-user mode. When the phase difference of the change in the breathing cycle data of the two users is less than the preset phase difference threshold, the light domain fusion effect is triggered in the virtual reality system. The light domain fusion effect is achieved by the color mixing of the visual elements of the two users and the overlap of their motion trajectories. The difficulty adjustment module, connected to the dual-user interaction module, is used to dynamically adjust the difficulty parameters of training levels in the virtual reality system based on the number of triggers and duration of the light field fusion effect.

[0013] Compared with existing technologies, the advantages of this invention are as follows: It simultaneously acquires heart rate and respiratory signals, overcoming the limitations of single signal feedback; it timestamps and correlates heart rate variability data with respiratory cycle data, revealing the interaction mechanism between the autonomic nervous system and respiratory rhythm; it generates color control parameters based on heart rate variability data and motion control parameters based on respiratory cycle data, driving visual elements in the virtual reality system to simultaneously change color and adjust movement, enhancing the user's intuitive perception of stress through visual and physiological linkage; in dual-user mode, it determines synchronicity through the phase difference of respiratory cycle data, triggering a light domain fusion effect, causing the visual elements of the two users to mix colors and overlap trajectories, enhancing training fun and participation through social interaction; and it adjusts the difficulty parameters of training levels in real time based on the number of triggers and duration of the light domain fusion effect, such as extending the exhalation duration or increasing the intensity of guidance prompts, achieving personalized adaptation of the rehabilitation path. This invention constructs a rehabilitation training system that meets the social needs of breast cancer patients. Attached Figure Description

[0014] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0015] Figure 1The diagram shown is a flowchart of a psychological stress analysis method based on heart rate and respiration provided in an embodiment of the present invention. Figure 2 The diagram shown illustrates the interaction mechanism between breathing and yoga movements in a dual-user mode according to an embodiment of the present invention. Figure 3 The diagram shown is an interactive mechanism diagram of breathing training in dual-user mode provided by an embodiment of the present invention; Figure 4 The diagram shown is a structural schematic of a psychological stress analysis system based on heart rate and respiration provided in an embodiment of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0017] The specific embodiments of the present invention will be described below.

[0018] Example 1 like Figure 1 As shown, this invention proposes a method for analyzing psychological stress based on heart rate and respiration, including: Step 1: Real-time acquisition of the user's heart rate and respiratory signals; Step 2: Use the data processing module to extract heart rate variability data from the heart rate signal and respiratory cycle data from the respiratory signal, and perform synchronous correlation analysis on the heart rate variability data and respiratory cycle data. Step 3: Generate color control parameters based on heart rate variability data and motion control parameters based on respiratory cycle data. Input the color control parameters and motion control parameters into the virtual reality system to control the visual elements in the virtual reality system to change color and adjust motion status. Step 4: In dual-user mode, the color control parameters and motion control parameters of the two users are acquired synchronously. When the phase difference of the change in the breathing cycle data of the two users is less than the preset phase difference threshold, the light domain fusion effect is triggered in the virtual reality system. The light domain fusion effect is achieved by the color mixing of the visual elements of the two users and the overlap of their motion trajectories. Step 5: Dynamically adjust the difficulty parameters of the training levels in the virtual reality system based on the number of times and duration of the light field fusion effect.

[0019] The psychological stress analysis method of this invention constructs a multimodal physiological data foundation by real-time acquisition of the user's heart rate and respiratory signals. Specifically, the heart rate signal is acquired through a photoplethysmography sensor worn on the user's wrist or finger, which generates a pulse wave signal by detecting changes in blood volume. The respiratory signal is acquired through a wearable breathing band integrating a flexible strain sensor, which detects changes in resistance caused by abdominal expansion and contraction and converts them into respiratory cycle data. The data processing module performs synchronous correlation analysis on the two types of signals, extracting heart rate variability (HRV) data and respiratory cycle features. By mapping HRV data to color control parameters (such as a gradient from cool to warm colors) and respiratory cycle data to motion control parameters (such as the rising and falling rates of visual elements), visual elements in the virtual reality system can simultaneously present color changes and dynamic movements, forming a multi-sensory feedback that links vision and physiology, enhancing the user's intuitive perception of stress.

[0020] In dual-user mode, the system monitors the phase difference of the breathing cycle data of two users in real time. When the phase difference is less than a preset threshold, a light domain fusion effect is triggered, where visual elements of two colors mix to form a gradient color ring, and their movement trajectories overlap. This mechanism leverages social interaction to enhance the fun of training and strengthens the sense of resonance between users through physiological synchronization. During training, the system dynamically counts the number of times the light domain fusion effect is triggered and its duration, adjusting the difficulty parameters of the training levels accordingly. For example, when users frequently achieve synchronization, the system automatically extends the target value of exhalation duration to increase the challenge; conversely, it assists users in adjusting their rhythm by increasing the playback frequency of breathing guidance audio.

[0021] This method enhances users' ability to autonomously regulate their physiological state through a dual mapping mechanism of color and motion. The dual-user collaborative mode, combined with dynamic difficulty adjustment, not only meets the emotional support needs of breast cancer patients but also achieves precise matching of rehabilitation intensity, forming a rehabilitation training system.

[0022] In conjunction with the first aspect, in some implementations, in step 1, a flexible strain sensor is used to collect respiratory signals. The flexible strain sensor is set in a wearable breathing belt to detect the expansion and contraction of the user's abdomen and convert it into a resistance change signal. The resistance change signal is then transmitted to the data processing module to generate respiratory cycle data.

[0023] This invention specifies a method for acquiring respiratory signals, specifically through a flexible strain sensor. The flexible strain sensor is integrated into a wearable breathing band, its core material being a resistive elastic fabric that deforms with the user's abdominal contraction and expansion, resulting in a linear change in resistance. The sensor is fixed to the non-elastic substrate of the breathing band using alligator clips or sewn on, ensuring stable contact with the skin. The resistance change signal is converted into a digital signal by a microcontroller and then transmitted wirelessly to a data processing module. The data processing module filters and extracts features from the signal to generate respiratory cycle data, including inspiratory duration, expiratory duration, and respiratory rate. The flexible strain sensor solves the problems of poor comfort and limited applicability of traditional respiratory monitoring devices.

[0024] In conjunction with the first aspect, in some implementations, in step 1, heart rate signals are acquired by a photoplethysmography (PPG) sensor. The PPG sensor is placed on the user's wrist or finger and generates a pulse wave signal based on the periodic changes in blood volume. Peak detection is performed on the pulse wave signal to extract the RR interval sequence, and heart rate variability data is calculated based on the RR interval sequence.

[0025] This invention specifies a method for acquiring heart rate signals, specifically through a photoplethysmography (PPG) sensor. This sensor is worn on the user's wrist or finger, using red and infrared light to illuminate the skin and detect differences in absorbance caused by periodic changes in blood volume, generating a pulse wave signal. The sensor incorporates a peak detection algorithm to extract the RR interval sequence (i.e., the time interval between adjacent heartbeats) from the pulse wave, and calculates heart rate variability (HRV) indices based on this, such as the root mean square difference (RMSSD) of consecutive heartbeat intervals.

[0026] To achieve high-precision monitoring, the sensor employs a dual-wavelength light source design to reduce motion artifact interference. For example, the sensor supports continuous pulse wave output mode, digitizing the analog signal via an onboard converter before transmitting it to the microcontroller. The microcontroller further performs noise reduction processing on the signal (such as moving average filtering) and uses a formula... Calculate the HRV parameters. Where RMSSD is the root mean square difference of consecutive heartbeat intervals, N is the number of heartbeat intervals, and RR... i For the i-th heartbeat interval, RR i+1 This represents the (i+1)th heartbeat interval.

[0027] The wrist-worn PPG sensor is fixed to the wrist with a 3D-printed shell, ensuring stable contact while allowing users to move freely, making it more suitable for long-term monitoring.

[0028] The PPG sensor combines non-invasiveness with high precision, making it suitable for use after breast cancer surgery; its dual-wavelength design and noise reduction algorithm effectively reduce signal interference.

[0029] In conjunction with the first aspect, in some implementations, in step 2, the data processing module receives heart rate and respiratory signals through a wireless communication network. The wireless communication network includes a low-latency transmission protocol and a high-reliability transmission protocol. The low-latency transmission protocol is used to transmit respiratory cycle data in real time, and the high-reliability transmission protocol is used to synchronize heart rate variability data.

[0030] The data processing module transmits respiratory cycle data in real time using a low-latency transmission protocol (such as UDP), while simultaneously synchronizing HRV data using a high-reliability transmission protocol (such as TCP). UDP has low header overhead and fast transmission speed, making it suitable for applications with high real-time requirements for respiratory signals; TCP ensures the integrity of HRV data, avoiding analysis errors caused by packet loss.

[0031] In conjunction with the first aspect, in some implementation methods, step 3, adjusting the motion state of the visual elements includes: The rate of ascent of visual elements is generated based on the duration of the inspiratory phase in the respiratory cycle data. The rate of visual element descent is generated based on the duration of the expiratory phase in the respiratory cycle data. The ascent and descent rates are input into the virtual reality system, driving visual elements to alternately rise and fall with each breathing phase.

[0032] The system generates the rising rate of visual elements based on the duration of the inhalation phase in the respiratory cycle data, and the falling rate based on the duration of the exhalation phase. For example, when the user's inhalation lasts for 4 seconds, the system calculates the rising rate as the increase in the height of the light field per unit time; when the exhalation lasts for 4 seconds, the falling rate is adjusted accordingly. After the rate parameters are input into the virtual reality engine, they drive the light field to rise and fall with the breathing rhythm, forming a dynamic energy field effect.

[0033] To achieve natural interaction, the system employs a smooth interpolation algorithm to handle abrupt rate changes. For example, when the user's breathing rhythm changes, the light field motion avoids visual jumps through gradual transitions. Furthermore, the co-mapping of breathing and yoga movements further enriches the motion feedback: the user's arm extension angle data is synchronized with the breathing phase to generate the vertical displacement and transparency parameters of the particle ribbon. During inhalation, the ribbon rises and its transparency increases; during exhalation, the ribbon descends and its transparency decreases, forming a closed-loop feedback loop of breathing-motion-vision.

[0034] In this embodiment of the invention, breathing-driven dynamic visual feedback helps users intuitively perceive their physiological state; smooth interpolation algorithms improve visual comfort; and breathing and movement synergistic mapping enhances the fun of training and promotes synchronized regulation of mind and body.

[0035] In conjunction with the first aspect, in some implementations, step 3, the generation of color control parameters includes: Extract the root mean square difference of consecutive heartbeat intervals from heart rate variability data; Based on the numerical range of the root mean square error, it is mapped to a linear gradient parameter from cool to warm tones; When the root mean square error is lower than the preset root mean square error threshold, the linear gradient parameter transitions from blue to red.

[0036] The system extracts the root mean square difference (RMSSD) of continuous heart rate intervals from heart rate variability data and maps its value range to a linear gradient parameter from cool to warm hues. For example, when the RMSSD is high (e.g., greater than 40ms), the user is in a relaxed state, and the system maps the light spectrum to light blue; when the RMSSD decreases to the 15ms to 40ms range, the color transitions to green, indicating that the user is in a normal but moderately relaxed state; if the RMSSD further falls below 15ms, the color gradually changes to pink, reflecting that the user may be in a state of high pressure or tension. This mapping process is implemented through a linear interpolation algorithm to ensure a smooth transition of color changes. For example, when the RMSSD decreases from 30ms to 10ms, the light spectrum color gradually transitions from green to pink, forming a dynamic visual warning.

[0037] To achieve precise mapping, the system presets multiple key threshold nodes and defines color gradation rates within different ranges. For example, near the RMSSD threshold (e.g., 15ms), the color change rate accelerates to enhance the user's sensitivity to stress levels. Furthermore, the system dynamically adjusts color saturation based on respiratory cycle data. As the user's exhalation duration increases, color saturation decreases, creating a linkage between breathing rhythm and color intensity.

[0038] The linear gradient design from cool to warm tones significantly enhances the user's intuitive perception of stress levels, prompting them to actively adjust their breathing rhythm to maintain an ideal color state.

[0039] In this embodiment of the invention, the color control parameters help users perceive the state of their autonomic nervous system in real time through a direct mapping from physiological data to visual feedback; the dynamic color gradient mechanism enhances the intuitiveness and alertness of the feedback.

[0040] In conjunction with the first aspect, some implementation methods refer to Figure 2 As shown, in step 3, the adjustment of the motion state of visual elements includes the coordinated mapping of breathing and yoga movements, specifically including: Yoga movement data is generated by detecting the user's arm extension angle and movement trajectory; The yoga movement data is time-synchronized and matched with the duration of the inhalation and exhalation phases in the breathing cycle data to generate collaborative control instructions that include vertical displacement parameters and transparency parameters. Based on the vertical displacement parameters, the outer particle ribbons that drive the visual elements in the virtual reality system rise during the inhalation phase and fall during the exhalation phase. Based on the duration of the expiratory phase in the respiratory cycle data, a transparency parameter for the particle streamer is generated. The longer the expiratory phase, the lower the transparency parameter. In dual-user mode, when the overlap of the yoga movement data of the two users exceeds a preset overlap threshold and the phase difference of the breathing cycle data is less than the phase difference threshold, a Möbius ring light flow effect is generated around the two users in the virtual reality system.

[0041] The system uses inertial measurement units or optical sensors to detect the user's arm extension angle and movement trajectory, generating yoga movement data. For example, in the "arms raised" movement, the sensor captures the angle between the arm and the vertical axis and calculates its speed. The data processing module synchronizes the yoga movement data with breathing cycle data, specifically aligning the inhalation / exhalation phases of the breath with the start / end points of the movement using timestamps. For example, the inhalation phase corresponds to arm abduction, and the system generates vertical displacement parameters to drive the virtual particle ribbon upward; the exhalation phase corresponds to the arm falling back, and the particle ribbon descends accordingly.

[0042] The transparency parameter dynamically adjusts based on the duration of the exhalation phase. As the user's exhalation time lengthens, the transparency of the particle ribbons gradually decreases, creating a visual dissipation effect and prompting the user to maintain a consistent exhalation rhythm. In dual-user mode, the system compares the overlap of yoga movement trajectories and the difference in breathing phase between the two users in real time. If the overlap exceeds a preset threshold (e.g., 80%) and the breathing phase difference is less than the threshold, a Möbius strip of light flow effect is generated around the two users in the virtual environment. This effect simulates the flow of light ribbons along a circular path using a particle system, enhancing the immersion and sense of accomplishment during collaborative training.

[0043] This embodiment balances accuracy and efficiency by optimizing sensor layout (such as dual-node detection at the wrist and shoulder). User testing shows that the coordinated mapping of movement and breathing significantly improves training focus, and the user's synchronization achievement rate is improved when the Möbius strip effect is triggered.

[0044] In this embodiment of the invention, the synergistic mapping of yoga movements and breathing promotes synchronized regulation of mind and body; the displacement and transparency changes of particle ribbons provide intuitive motion feedback; the optical flow effect in dual-user mode enhances social interactivity and motivates users to maintain physiological and motor coordination through a visual reward mechanism.

[0045] In addition, yoga poses for upper limb rehabilitation after breast cancer surgery can include mountain pose, outward arm support, single arm extension, and half-moon pose.

[0046] In conjunction with the first aspect, some implementation methods refer to Figure 3 As shown, in step 4, the triggering of the light field blending effect includes: Map the color control parameters of the first user to the first light gamut color, and map the color control parameters of the second user to the second light gamut color; Based on the inspiratory phase of the respiratory cycle data, the light domain contraction velocity of the first and second users is generated; based on the expiratory phase of the respiratory cycle data, the light domain expansion velocity of the first and second users is generated. When the phase difference between the light domain contraction speed of the first user and the second user, or the phase difference between the light domain expansion speed of the first user and the second user, is less than a preset phase difference threshold, the light domain fusion effect is triggered, and the color of the first light domain and the color of the second light domain are mixed into a dynamic gradient color wheel.

[0047] The system maps the color control parameters of the first user to a first light domain color (e.g., blue), and the second user to a second light domain color (e.g., pink). Based on the respiratory cycle data, it generates the light domain contraction speed during the inhalation phase and the light domain expansion speed during the exhalation phase. For example, during inhalation, the user's light domain contracts inward at a rate of 5% per second; during exhalation, it expands outward at a rate of 3% per second. The phase difference between the light domain movement speeds of the two users is calculated through time-series analysis. If the phase difference between the contraction or expansion speeds is less than a preset threshold (e.g., 10%), the system triggers a light domain fusion effect, mixing the two colors into a dynamic gradient color wheel.

[0048] The gradient color wheel is implemented using a color overlay algorithm, such as interpolation mixing with the HSL color space. Blue and pink are mixed to generate a purple gradient, creating a flowing light effect along the edge of the light field. During the blending process, the transparency of the overlapping areas of the light fields decreases, highlighting the color fusion effect. Furthermore, the system adjusts the gradient rate based on the phase difference; the smaller the phase difference, the faster the gradient.

[0049] In this embodiment of the invention, the dual mapping of light domain color and motion speed intuitively reflects the differences in users' physiological states; the dynamic gradient color ring enhances the motivation for collaborative training through visual rewards; and the phase difference-driven gradient rate adjustment achieves a balance between feedback accuracy and incentive intensity, effectively improving the training effect of dual-user mode.

[0050] In conjunction with the first aspect, in some implementation methods, the dynamic adjustment of the difficulty parameter in step 5 includes: The number of times the light domain fusion effect is triggered per unit time is counted, and the duration of each light domain fusion effect is recorded; Based on the number of triggers and the duration, the effective synchronization index per unit time is calculated. The effective synchronization index is positively correlated with the number of triggers and positively correlated with the average duration. When the effective synchronization index is higher than the first preset threshold, the target value of the expiratory duration in the respiratory cycle data is increased. When the effective synchronization index is lower than the second preset threshold, the playback frequency of the breathing guidance audio is superimposed in the virtual reality system.

[0051] In step 5, the difficulty parameter is dynamically adjusted based on the number of triggers of the light domain fusion effect and the duration of each trigger. The system uses a fixed time length as the statistical unit time, accumulates the total number of triggers of the light domain fusion effect within the unit time, and records the start and end times for each trigger, calculating the difference between the start and end times as the single duration. The system calculates the effective synchronization index based on the number of triggers and the average of all single durations. The value of the effective synchronization index is positively correlated with the number of triggers and the average single duration. One calculation method is to use the product of the number of triggers and the average duration as the effective synchronization index. Another calculation method is to assign weight coefficients to the number of triggers and the average duration, and then perform a weighted summation, with the resulting value as the effective synchronization index. When the effective synchronization index is higher than a first preset threshold, it indicates that the breathing synchronization frequency of the two users within a unit time is high and the duration of the synchronization state is long, and the current training difficulty is lower than the users' coordination ability. The system increases the target value of the exhalation duration in the respiratory cycle data by an incremental step to extend the duration required for each exhalation by the user and increase the physiological load of subsequent training. When the effective synchronization index falls below the second preset threshold, it indicates that the breathing synchronization frequency of the two users is low or the synchronization state is difficult to maintain, and the current training difficulty may exceed the users' coordination ability. The system superimposes the playback frequency of breathing guidance audio into the virtual reality system, and by increasing the playback density of the prompts, assists users in adjusting their exhalation and inhalation rhythms, prompting them to gradually move closer to the target breathing rhythm.

[0052] This method achieves dynamic matching between training load and user collaboration ability by statistically analyzing the frequency and duration of synchronization events, thereby improving training compliance and physiological regulation efficiency.

[0053] Example 2 like Figure 4 As shown, in a second aspect, the present invention provides a psychological stress analysis system based on heart rate and respiration. The system employs the method provided in any of the above embodiments, and the system includes: The signal acquisition module is used to acquire the user's heart rate and respiratory signals in real time; The data processing module, connected to the signal acquisition module, is used to extract heart rate variability data from heart rate signals, extract respiratory cycle data from respiratory signals, and perform synchronous correlation analysis on heart rate variability data and respiratory cycle data. The virtual reality control module, connected to the data processing module, is used to generate color control parameters based on heart rate variability data and motion control parameters based on respiratory cycle data. The color control parameters and motion control parameters are then input into the virtual reality system to control the visual elements in the virtual reality system to change color and adjust motion status. The dual-user interaction module is connected to the virtual reality control module and is used to synchronously acquire the color control parameters and motion control parameters of two users in dual-user mode. When the phase difference of the change in the breathing cycle data of the two users is less than the preset phase difference threshold, the light domain fusion effect is triggered in the virtual reality system. The light domain fusion effect is achieved by the color mixing of the visual elements of the two users and the overlap of their motion trajectories. The difficulty adjustment module, connected to the dual-user interaction module, is used to dynamically adjust the difficulty parameters of training levels in the virtual reality system based on the number of triggers and duration of the light field fusion effect.

[0054] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A method for analyzing psychological stress based on heart rate and respiration, characterized in that, include: Step 1: Collect the user's heart rate and respiratory signals in real time; Step 2: Use the data processing module to extract heart rate variability data from the heart rate signal and respiratory cycle data from the respiratory signal, and perform synchronous correlation analysis on the heart rate variability data and the respiratory cycle data; Step 3: Generate color control parameters based on the heart rate variability data, generate motion control parameters based on the respiratory cycle data, input the color control parameters and motion control parameters into the virtual reality system, and control the visual elements in the virtual reality system to change color and adjust motion state. Step 4: In dual-user mode, the color control parameters and motion control parameters of the two users are acquired synchronously. When the phase difference of the change in the breathing cycle data of the two users is less than the preset phase difference threshold, the light domain fusion effect is triggered in the virtual reality system. The light domain fusion effect is achieved by the color mixing of the visual elements of the two users and the overlap of their motion trajectories. Step 5: Dynamically adjust the difficulty parameters of the training level in the virtual reality system based on the number of times and duration of the light domain fusion effect.

2. The psychological stress analysis method based on heart rate and respiration according to claim 1, characterized in that, In step 1, the breathing signal is acquired by a flexible strain sensor. The flexible strain sensor is set in the wearable breathing belt, detects the expansion and contraction of the user's abdomen and converts it into a resistance change signal. The resistance change signal is transmitted to the data processing module to generate the breathing cycle data.

3. The psychological stress analysis method based on heart rate and respiration according to claim 1, characterized in that, In step 1, the heart rate signal is acquired by a photoplethysmography (PPG) sensor, which is placed on the user's wrist or finger. The PPG sensor generates a pulse wave signal based on the periodic changes in blood volume. Peak detection is performed on the pulse wave signal to extract the RR interval sequence. The heart rate variability data is calculated based on the RR interval sequence.

4. The psychological stress analysis method based on heart rate and respiration according to claim 1, characterized in that, In step 2, the data processing module receives the heart rate signal and respiratory signal through a wireless communication network. The wireless communication network includes a low-latency transmission protocol and a high-reliability transmission protocol. The low-latency transmission protocol is used to transmit the respiratory cycle data in real time, and the high-reliability transmission protocol is used to synchronize the heart rate variability data.

5. The psychological stress analysis method based on heart rate and respiration according to claim 1, characterized in that, In step 3, the adjustment of the motion state of the visual element includes: The rate of ascent of the visual element is generated based on the duration of the inspiratory phase in the respiratory cycle data. The descent rate of the visual element is generated based on the duration of the expiratory phase in the respiratory cycle data. The rising rate and the falling rate are input into the virtual reality system to drive the visual elements to rise and fall alternately with the breathing phase.

6. The psychological stress analysis method based on heart rate and respiration according to claim 1, characterized in that, In step 3, the generation of the color control parameters includes: Extract the root mean square difference of consecutive heartbeat intervals from the heart rate variability data; Based on the numerical range of the root mean square error, it is mapped to a linear gradient parameter from cool to warm tones; When the root mean square error is lower than the preset root mean square error threshold, the linear gradient parameter transitions from blue to red.

7. The psychological stress analysis method based on heart rate and respiration according to claim 1, characterized in that, In step 3, the adjustment of the motion state of the visual elements includes the coordinated mapping of breathing and yoga movements, specifically including: Yoga movement data is generated by detecting the user's arm extension angle and movement trajectory; The yoga movement data is time-synchronized and matched with the inhalation and exhalation phases in the breathing cycle data to generate a collaborative control command that includes vertical displacement parameters and transparency parameters. Based on the vertical displacement parameters, the outer particle ribbons of the visual elements in the virtual reality system are driven to rise during the inhalation phase and fall during the exhalation phase. The transparency parameter of the particle streamer is generated based on the duration of the exhalation phase in the respiratory cycle data, wherein the longer the exhalation phase, the lower the transparency parameter. In dual-user mode, when the overlap of the yoga movement data of the two users exceeds a preset overlap threshold and the phase difference of the breathing cycle data is less than the phase difference threshold, a Möbius ring light flow effect is generated around the two users in the virtual reality system.

8. The psychological stress analysis method based on heart rate and respiration according to claim 1, characterized in that, In step 4, the triggering of the light domain fusion effect includes: The color control parameters of the first user are mapped to a first light gamut color, and the color control parameters of the second user are mapped to a second light gamut color; Based on the inspiratory phase of the respiratory cycle data, the light domain contraction velocity of the first user and the second user is generated; based on the expiratory phase of the respiratory cycle data, the light domain expansion velocity of the first user and the second user is generated. When the phase difference between the light domain contraction speed of the first user and the second user, or the phase difference between the light domain expansion speed of the first user and the second user, is less than a preset phase difference threshold, the light domain fusion effect is triggered, and the color of the first light domain and the color of the second light domain are mixed into a dynamic gradient color ring.

9. A psychological stress analysis method based on heart rate and respiration according to claim 1, characterized in that, In step 5, the dynamic adjustment of the difficulty parameter includes: The number of times the light domain fusion effect is triggered per unit time is counted, and the duration of each light domain fusion effect is recorded; Based on the number of triggers and the duration, the effective synchronization index per unit time is calculated. The effective synchronization index is positively correlated with the number of triggers and positively correlated with the average duration. When the effective synchronization index is higher than the first preset threshold, the target value of the expiratory duration in the respiratory cycle data is increased; When the effective synchronization index is lower than the second preset threshold, the playback frequency of the breathing guidance audio is superimposed in the virtual reality system.

10. A psychological stress analysis system based on heart rate and respiration, characterized in that, The system employs the method according to any one of claims 1 to 9, the system comprising: The signal acquisition module is used to acquire the user's heart rate and respiratory signals in real time; A data processing module, connected to the signal acquisition module, is used to extract heart rate variability data from the heart rate signal, extract respiratory cycle data from the respiratory signal, and perform synchronous correlation analysis on the heart rate variability data and the respiratory cycle data. A virtual reality control module, connected to the data processing module, is used to generate color control parameters based on the heart rate variability data, generate motion control parameters based on the respiratory cycle data, input the color control parameters and the motion control parameters into the virtual reality system, and control the visual elements in the virtual reality system to change color and adjust motion state. A dual-user interaction module, connected to the virtual reality control module, is used to synchronously acquire the color control parameters and motion control parameters of two users in dual-user mode. When the phase difference of the change in the breathing cycle data of the two users is less than a preset phase difference threshold, a light domain fusion effect is triggered in the virtual reality system. The light domain fusion effect is achieved by the color mixing of visual elements of the two users and the overlap of motion trajectories. The difficulty adjustment module, connected to the dual-user interaction module, is used to dynamically adjust the difficulty parameters of the training levels in the virtual reality system based on the number of triggers and duration of the light domain fusion effect.

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