Preoperative anxiety relieving method and system based on virtual reality technology

Abstract

The invention relates to the technical field of data processing, in particular to a preoperative anxiety relieving method and system based on the virtual reality technology, and the method comprises the steps: obtaining preoperative physiological rhythm data of a patient, the physiological rhythm data at least comprising respiratory frequency and heart rate change information; constructing a virtual natural scene with dynamic light and shadow changes based on the physiological rhythm data, wherein the virtual natural scene comprises light and shadow fluctuation elements corresponding to the respiratory frequency; a patient wears a VR head-mounted display and a bone conduction earphone, the equipment automatically loads a customized virtual scene, breathing key points are prompted in real time through virtual guidance, and the patient is helped to establish perception association of rhythm elements; the physiological data of the patient is analyzed in real time to obtain the respiratory rate, the heart rate variability and the skin electrical activity value, and when the difference value between the light and shadow fluctuation frequency and the respiratory rate exceeds a threshold value, the light and shadow frequency is adjusted step by step to approach the respiratory rate. According to the multi-dimensional evaluation system, the anxiety state of the patient is reflected more comprehensively, and insufficient intervention caused by evaluation deviation is avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a preoperative anxiety relief method and system based on virtual reality technology. BACKGROUND

[0002] Preoperative anxiety is a common psychological stress response of surgical patients, mainly manifested as preoperative emotional tension, rapid heart rate, rapid breathing and other physiological and psychological abnormalities. Severe cases may lead to decreased tolerance to surgery, delayed postoperative recovery and even induce cardiovascular complications. Currently, the main methods for relieving preoperative anxiety in clinical practice include three categories: Drug intervention: such as benzodiazepine sedative drugs, which can quickly relieve anxiety, but may cause drowsiness, respiratory depression and other side effects, and have the risk of drug allergy for some patients, and affect the metabolic balance of anesthetic drugs during surgery; Traditional psychological intervention: including verbal reassurance by medical staff, music therapy, progressive muscle relaxation training, etc., which relies on manual operation and is greatly influenced by the experience of medical staff, and lacks personalized adaptation, with limited relief effect; Conventional virtual reality technology: some studies use pre-set virtual scenes (such as static natural landscapes) to distract patients, but the scenes are mostly fixed content and cannot respond to changes in the patient's real-time physiological state, and do not combine physiological indicators with psychological state correlation analysis, making it difficult to achieve "rhythm synchronization" deep relaxation, and the effect is not good for patients with moderate to severe anxiety; In addition, the existing technology generally has the problem of splitting physiological and psychological data: either relying only on subjective scale to assess anxiety state, ignoring the objective feedback of physiological indicators, or simply monitoring physiological data, lacking dynamic correlation with psychological state, resulting in insufficient pertinence of intervention measures. SUMMARY

[0003] The present application provides a preoperative anxiety relief method and system based on virtual reality technology to solve the technical problems in the prior art.

[0004] The technical solution of the present application to solve the above technical problems is as follows: a preoperative anxiety relief method based on virtual reality technology, comprising the following steps: S101, obtaining physiological rhythm data of a patient before surgery, the physiological rhythm data at least including respiratory rate and heart rate change information; S102, constructing a virtual natural scene with dynamic light and shadow changes based on the physiological rhythm data, the virtual natural scene containing light and shadow fluctuation elements corresponding to the respiratory rate; S103, wearing a VR headset and a bone conduction earphone for the patient, the device automatically loads the customized virtual scene, and the virtual guide prompts the breathing points in real time to help the patient establish the perception association of the rhythm elements; S104, real-time physiological data analysis to obtain respiratory rate, heart rate variability and skin electrical activity values, when the difference between the light and shadow fluctuation frequency and the respiratory rate exceeds the threshold value, step by step adjust the light and shadow frequency to approach the respiratory rate.

[0005] In a preferred embodiment, in S101, preoperative physiological rhythm data of patients in resting state are continuously collected by integrated physiological monitoring devices, including chest strap respiratory sensors, wrist heart rate monitors and skin electrical sensors, specifically including respiratory rate, heart rate variability and skin electrical activity, and motion interference and device noise outliers are removed by data cleaning algorithms to form a standardized physiological data set; After the physiological data collection is completed, the patient's subjective anxiety score is collected using the self-rating anxiety scale, which contains n entries, of which the positive scoring entries , and the reverse scoring entries , The score calculation formula of the self-rating anxiety scale is as follows: ; Wherein, represents the total score of the self-rating anxiety scale, represents the score of the positive scoring entries, represents the total score conversion coefficient, and the specific calculation formula of the self-rating anxiety scale standard score is as follows: ; Wherein, represents the integer symbol, when , it means that the patient is in normal state, , it means that the patient has moderate anxiety tendency, , it means that the patient has severe anxiety tendency; A physiological-mental dual-dimension anxiety benchmark model is constructed combined with physiological data, physiological indicators include respiratory rate standardized value , heart rate variability standardized value , skin electrical activity standardized value , mental indicators include subjective anxiety score as self-rating anxiety scale standard score , and the specific calculation formula of the physiological-mental dual-dimension anxiety benchmark model is as follows: ; Wherein, is the weight coefficient, which satisfies , represents the self-rating anxiety scale score standardization denominator, which is used to map the score to the [0, 1] interval, and the anxiety index demarcation value is set as , when , it means that the patient is in mild anxiety state, and when When the value of the anxiety level is between 0 and 1, it indicates that the patient is in a mild anxiety state, When the value of the anxiety level is between 1 and 2, it indicates that the patient is in a severe anxiety state. The initial anxiety state of the patient is determined, the type preference of the patient to the virtual scene, the light and shadow sensitivity, and the sound acceptance range are collected through a preference questionnaire, which provides a basis for scene customization, and the initial parameters of the basic dynamic elements of the virtual scene are adjusted according to the anxiety level based on the initial anxiety state; The physiological stability value, the standard score of the anxiety self-rating scale, and the corresponding physiological interval are integrated to generate a physiological-mental dual-dimension anxiety benchmark model, wherein the physiological stability value and the standard score of the anxiety self-rating scale are the benchmark values of real-time monitoring.

[0006] In a preferred embodiment, in S102, after obtaining the dual-dimension anxiety benchmark model and the scene preference data, a virtual scene matching the patient's preference is first called from the scene material library. The virtual scene is essentially a computer-generated simulated environment. The lighting effect in the scene is adjusted according to the time change to establish a dynamic light and shadow change. The construction process relies on a three-dimensional graphics rendering engine. The basic scene is built by importing a natural environment model and configuring light source attributes. The dynamic light and shadow change is not a preset animation, but is driven by physiological rhythm data, and its change logic is directly related to the patient's individual physiological rhythm data. In the virtual scene, a light and shadow fluctuation element corresponding to the breathing frequency is established to establish a mapping relationship between the breathing frequency data and the light and shadow parameters. Let the breathing frequency be , the light and shadow fluctuation element be , and the mapping relationship be as follows: ; Wherein, represents a frequency conversion coefficient, which can be adjusted according to the scene comfort, represents a frequency offset. The higher the breathing frequency, the faster the light and shadow fluctuation frequency, realizing the rhythm synchronization of breathing-light. The breathing frequency signal is received in real time and converted into a light and shadow fluctuation parameter. The skin electricity activity value of the patient is extracted from the physiological-mental dual-dimension anxiety benchmark model, and the fluctuation amplitude is dynamically adjusted. If the skin electricity activity value is higher than the benchmark interval, the fluctuation amplitude is reduced.

[0007] In a preferred embodiment, in S103, after the patient enters the preoperative preparation room, the VR headset and the bone conduction earphone are worn. The bone conduction earphone adopts the principle of temporal bone vibration sound transmission to ensure that the patient can receive the virtual scene audio and the instructions of the medical staff in the environment at the same time. After the equipment is worn, the customized virtual scene is automatically loaded. After the virtual scene is activated, voice guidance instructions are issued by the virtual guide in the scene. The instructions include two technical effects: S1, requiring the patient to fixate on the light and shadow fluctuation element synchronized with the breathing frequency; S2, establish the following relationship between the patient's autonomous breathing behavior and the light and shadow fluctuation cycle, and output voice instructions through bone conduction earphones to ensure that the sound source direction sense matches the virtual guide position in space; In the initial adaptation stage, a phased regulation strategy is performed: S1, maintain a fluctuation cycle that is completely consistent with the patient's preoperative baseline respiratory frequency; S2, the virtual guide triggers a voice prompt at the breathing cycle node, which establishes a mapping rule between the light and dark change direction and the breathing phase: the process of light intensity rising from the lowest value to the highest value corresponds to the inspiration phase, and the process of light intensity falling from the highest value to the lowest value corresponds to the expiration phase. Through voice prompts, help patients establish the perception association between visual brightness gradient change and respiratory muscle movement.

[0008] In a preferred embodiment, in S104, after starting to implement physiological monitoring, automatically set the data acquisition time interval T, and every T time interval, synchronously collect the patient's current physiological rhythm data through integrated sensors, including real-time respiratory frequency, real-time heart rate variability, and real-time skin electrical activity, and transmit the data to the edge computing unit for rapid analysis. The time stamp of the original data is preserved during the analysis process to ensure time synchronization with the dynamic adjustment of the virtual scene; After analysis, calculate the difference between the current light and shadow fluctuation frequency and the respiratory frequency If is greater than the preset threshold , trigger the light and shadow frequency dynamic adjustment mechanism to gradually narrow the gap between the light and shadow fluctuation frequency and the real-time respiratory frequency by a fixed step , and the time interval of each adjustment is set to , until If , it is determined that the current light and shadow frequency and the respiratory rhythm have reached the adaptation state. After reaching the adaptation state, positive feedback is given through auxiliary dynamic elements in the virtual scene to strengthen the patient's perception of rhythm synchronization; Continuously compare the real-time physiological rhythm data with the baseline values in the physiological- psychological two-dimensional anxiety baseline model, calculate the improvement ratio of real-time heart rate variability relative to heart rate variability baseline and the change ratio of real-time skin electrical activity relative to skin electrical activity baseline, if the heart rate variability improvement ratio and the skin electrical activity decline ratio , it is determined that the patient's anxiety state is relieved, and the level of light and shadow fluctuation can be automatically increased, if the skin electrical activity improvement ratio , immediately start the scene pacification mechanism, and reduce the contrast of light and shadow to 1-k of the original value, k represents the contrast adjustment coefficient, and pacification voice is issued through the virtual guide in the scene to reduce sensory stimulation and alleviate anxiety escalation.

[0009] The application also provides a preoperative anxiety relief system based on virtual reality technology, comprising: A data acquisition and personalized benchmark establishment module: physiological rhythm data of a patient before surgery is acquired, and the physiological rhythm data at least includes respiratory frequency and heart rate variability information; A dynamic virtual scene generation module: a virtual natural scene with dynamic light and shadow changes is constructed based on the physiological rhythm data, and the virtual natural scene contains light and shadow fluctuation elements corresponding to the respiratory frequency; A guidance and rhythm synchronization module: a VR headset and a bone conduction earphone are worn by the patient, the device automatically loads a customized virtual scene, and a virtual guide is used to real-time prompt respiratory points, so as to help the patient establish a perception association with the rhythm elements; A dynamic rhythm adjustment module: respiratory frequency, heart rate variability and skin electrical activity values are obtained by real-time analysis of physiological data of the patient, and when the difference between the light and shadow fluctuation frequency and the respiratory frequency exceeds a threshold value, the light and shadow frequency is step by step adjusted to approach the respiratory frequency.

[0010] The application has the beneficial effects that: based on a physiological-mental dual-dimension anxiety benchmark model, objective physiological data of respiratory frequency, heart rate variability and skin electrical activity are collected by an integrated sensor, subjective anxiety scores of an anxiety self-rating scale are combined, a multi-dimension evaluation system is formed, the anxiety state of the patient can be more comprehensively reflected, precise basis is provided for subsequent intervention, intervention deficiency and excessive stimulation caused by evaluation deviation are avoided, through a preoperative preference questionnaire, preferences of the patient for a virtual scene type, light and shadow sensitivity and sound acceptance range are collected, scene parameters are customized in combination with an initial anxiety level, compared with a fixed scene of a conventional virtual reality technology, individual customization can reduce the resistance of the patient to the scene, the scene intensity matching the anxiety degree can improve the immersion and acceptance; The respiratory frequency is mapped to the light and shadow fluctuation frequency by real-time driving of the scene change through physiological data, the precise synchronization of the light and shadow and the breath is realized through dynamic adjustment of the step length, and the scene complexity is adjusted based on the real-time changes of the heart rate variability and the skin electrical activity, the rhythm anchoring mechanism uses the instinctive following reaction of human beings to periodic visual stimulation, compared with the simple attention distraction of a static scene, the autonomic nervous relaxation can be more effectively induced. BRIEF DESCRIPTION OF DRAWINGS

[0011] Figure 1 A method flowchart of the application; Figure 2 A system block diagram of the application. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0013] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the described features. In the description of this application, "plurality" means two or more, unless otherwise specifically specified.

[0014] In the description of this application, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art will recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in this application.

[0015] like Figure 1 This embodiment provides: a method for alleviating preoperative anxiety based on virtual reality technology, comprising the following steps: S101. Obtaining preoperative physiological rhythm data of the patient, where the physiological rhythm data includes at least respiratory rate and heart rate variation information; Furthermore, preoperatively, integrated physiological monitoring equipment, including a chest-strap respiratory sensor, a wrist heart rate monitor, and a skin electrode sensor, continuously collects the patient's resting physiological rhythm data, including respiratory rate, heart rate variability, and skin electrode activity. A data cleaning algorithm is used to remove motion interference and equipment noise anomalies to form a standardized physiological data set. After the physiological data collection is completed, the patient's subjective anxiety score is collected using the anxiety self-rating scale. The scale contains n items, of which the positive scoring items are Reverse-scored items indivual, The scoring formula of the anxiety self-rating scale is as follows: ; wherein, represents the total score of the self-rating anxiety scale, represents the positive score item score, represents the total score conversion coefficient, the specific calculation formula of the self-rating anxiety scale standard score is as follows: ; wherein, represents the rounding symbol, when , it indicates that the patient is in a normal state, , it indicates that the patient has a moderate anxiety tendency, , it indicates that the patient has a severe anxiety tendency; Combine physiological data to construct a physiological- psychological two-dimensional anxiety benchmark model, the physiological indicators include the respiratory rate standardized value , heart rate variability standardized value , skin electrical activity standardized value , the psychological indicators include the subjective anxiety score as the self-rating anxiety scale standard score , the specific calculation formula of the physiological- psychological two-dimensional anxiety benchmark model is as follows: ; wherein, is a weight coefficient, satisfying , represents the self-rating anxiety scale score standardization denominator, which is used to map the score to the [0, 1] interval, set the anxiety index demarcation value as , when , it indicates that the patient is in a mild anxiety state, when , it indicates that the patient is in a moderate anxiety state, , it indicates that the patient is in a severe anxiety state; Determine the initial anxiety state of the patient, collect the patient's type tendency, light sensitivity and sound acceptance range of the virtual scene through the preference questionnaire, provide basis for scene customization, based on the initial anxiety state, the initial parameters of the virtual scene basic dynamic elements are adjusted according to the anxiety level; Integrate individual physiological stability value, self-rating anxiety scale standard score and corresponding physiological interval to generate a physiological- psychological two-dimensional anxiety benchmark model, wherein the physiological stability value and the self-rating anxiety scale standard score are the benchmark values of real-time monitoring.

[0016] It should be noted that within 12 hours before the operation, the integrated physiological monitoring equipment should be calibrated first to ensure that the tightness of the chest strap respiratory sensor is adapted to the patient's chest, the wrist heart rate monitor is fitted 2 cm above the wrist crease, and the electrode is in full contact with the skin. The skin electrical sensor is fixed to the ring finger and middle finger of the patient's non-dominant hand. The equipment is connected to the data acquisition terminal and the self-test program is started to confirm that the signal transmission of each sensor is stable (respiratory signal-to-noise ratio ≥30d8, heart rate signal error <2 times / minute). Then, the patient is guided into a quiet monitoring room, the chair is adjusted to a semi-recumbent position, and the patient is asked to remain at rest (avoid talking and large-scale limb movements). At the same time, the monitoring process is explained to reduce the patient's tension, and then the equipment is started for 30 consecutive The first 5 minutes of physiological data collection are for acclimation and are not included in the analysis. The respiratory rate, heart rate variability (HRV), and electrodermal activity (EDA) data for the next 25 minutes are transmitted to the terminal in real time. The terminal's built-in adaptive filtering algorithm automatically identifies and removes motion interference signals caused by the patient's slight turning and swallowing movements, as well as noise spikes caused by poor device contact. After standardization, the data is converted into a structured physiological data set and stored. After the physiological data collection is completed, the patient is given a Self-Rating Anxiety Scale (SAS). Medical staff assist in interpreting the scale items (to avoid distorted scoring due to misunderstandings in patients). Patients complete the scoring of 20 items based on their anxiety status in the past week. After the scale is collected, the total SAS score is calculated using the scoring rule (adding the scores of each item and multiplying by 1.25 to obtain the integer). The total score is then correlated with the aforementioned physiological data to construct a two-dimensional anxiety benchmark model corresponding to the "subjective score of physiological indicators" to clarify the patient's initial anxiety level.

[0017] S102, constructing a virtual natural scene with dynamic light and shadow changes based on the physiological rhythm data, wherein the virtual natural scene includes light and shadow fluctuation elements corresponding to the respiratory frequency; Furthermore, after obtaining the dual-dimensional anxiety baseline model and scene preference data, a virtual scene matching the patient's preferences is first retrieved from the scene material library. The essence of this virtual scene is a computer-generated simulated environment. The lighting effects in the scene are adjusted according to time changes to establish dynamic light and shadow changes. The construction process relies on a three-dimensional graphics rendering engine. The basic scene is built by importing a natural environment model and configuring light source properties. The dynamic light and shadow changes are not preset animations, but are driven by physiological rhythm data. The change logic is directly related to the patient's individual physiological rhythm data. The virtual scene includes light and shadow fluctuation elements corresponding to the respiratory frequency, and a mapping relationship between the respiratory frequency data and the light and shadow parameters is established. Suppose the respiratory frequency is , the light and shadow wave elements are , the mapping relationship is as follows: ; wherein, represents a frequency conversion coefficient, which can be adjusted according to the scene comfort, represents a frequency offset, the higher the breathing frequency, the faster the light and shadow fluctuation frequency, realizing the rhythm synchronization of breathing-light and shadow, converting the breathing frequency signal into a light and shadow fluctuation parameter in real time, extracting the skin electricity activity value of the patient from the physiological- psychological two-dimensional anxiety benchmark model, and dynamically adjusting the fluctuation amplitude. If the skin electricity activity value is higher than the benchmark interval, the fluctuation amplitude is reduced.

[0018] It should be noted that the construction of the virtual scene loads the basic terrain data of the template through the three-dimensional graphics rendering engine, including water height, mountain profile, vegetation distribution and other information, and calls A1 auxiliary modeling tool to optimize the details of the scene. According to the patient's SAS score, moderate anxiety patients use softer blue-purple tones, and set the cloud density according to the HRV value. The lower the HRV, the less the cloud cover, the more open, and then start the dynamic element configuration program. Automatically associate the physical parameters of the basic dynamic elements, such as cloud drift and vegetation shaking, with the patient's anxiety level: if it is a severe anxiety patient, reduce the cloud drift speed, reduce the vegetation shaking amplitude, and reduce the frequency of fast-moving elements in the scene to avoid visual stimulation and aggravate anxiety; After completing the scene framework, enter the core rhythm element configuration stage: if the patient prefers a lake scene, the system is positioned to the water area in the scene, and the lake surface wave effect is generated through the GPU particle system, which is set as the core fluctuation element; If you prefer a starry sky scene, add a starlight particle group to the dome layer, set the brightness change period of each particle to be independently controllable, then call the patient's preoperative benchmark breathing frequency data, and convert it to a light and shadow fluctuation frequency parameter. If the benchmark breathing frequency is 20 times / minute, the corresponding period is calculated to be 3 seconds / time, and the frequency parameter is bound to the material properties of the core fluctuation element, such as transparency, brightness or position offset, through the animation curve editor, to ensure that the light and shadow fluctuation is completely synchronized with the patient's breathing rhythm. At the same time, extract the patient's skin electricity activity value from the anxiety benchmark model, and dynamically adjust the fluctuation amplitude: if the skin electricity activity value is higher than the benchmark interval, reduce the fluctuation amplitude and increase the gradual transition time, so that the light and shadow change is more smooth and comfortable, reducing the sensory stimulation. Finally, the scene is checked for lighting consistency to ensure that the core fluctuation element is coordinated with the environment light effect, such as matching the starlight flicker intensity with the moonlight brightness in the starry sky scene, and generating a dynamic virtual scene instance that can respond to physiological data changes in real time.

[0019] S103, wearing a VR headset and bone conduction earphones for the patient, the device automatically loads the customized virtual scene, and the virtual guide prompts the breathing points in real time to help the patient establish a perceptual association with the rhythm elements; Further, after the patient enters the preoperative preparation room, the VR headset and bone conduction earphones are worn. The bone conduction earphones adopt the principle of temporal bone vibration sound transmission, ensuring that the patient can simultaneously receive virtual scene audio and instructions from medical staff in the environment. After wearing the device, the customized virtual scene is automatically loaded. After the virtual scene is activated, voice guidance instructions are issued by the virtual guide in the scene. The instructions include two technical effects: S1, requiring the patient to fixate on the light and shadow fluctuation element synchronized with the respiratory frequency; S2, clearly establishing the following relationship between the patient's autonomous breathing behavior and the light and shadow fluctuation cycle. Voice instructions are output through bone conduction earphones to ensure that the sound source direction sense matches the virtual guide position in space; During the initial adaptation stage, a phased control strategy is implemented: S1, maintaining a fluctuation cycle that is completely consistent with the patient's preoperative baseline respiratory frequency; S2, the virtual guide triggers voice prompts at the respiratory cycle nodes. The voice prompts establish a mapping rule between light and shadow direction changes and respiratory phases: the process of light and shadow brightness rising from the lowest value to the highest value corresponds to the inspiration phase, and the process of light and shadow brightness falling from the highest value to the lowest value corresponds to the expiration phase. Through voice prompts, the patient can help establish a perceptual association between visual brightness gradient changes and respiratory muscle movements.

[0020] S104, real-time analysis of patient physiological data to obtain respiratory frequency, heart rate variability, and skin electrical activity values. When the difference between the light and shadow fluctuation frequency and the respiratory frequency exceeds the threshold value, the light and shadow frequency is gradually adjusted to approach the respiratory frequency in steps; Further, after starting the physiological monitoring, the data collection time interval T is automatically set. Every T time interval, the current physiological rhythm data of the patient is synchronously collected through the integrated sensor, including real-time respiratory frequency, real-time heart rate variability, and real-time skin electrical activity. The data is transmitted to the edge computing unit for rapid analysis. The timestamp of the original data is preserved during the analysis process to ensure time synchronization with the dynamic adjustment of the virtual scene; After analysis, the difference between the current light and shadow fluctuation frequency and the respiratory frequency is calculated If is greater than the preset threshold value ( representing the frequency-sensitive difference, representing the critical value that needs to be actively adjusted) the light and shadow frequency dynamic adjustment mechanism is triggered to gradually reduce the difference between the light and shadow fluctuation frequency and the real-time respiratory frequency in fixed steps , and the time interval for each adjustment is set to , until If , it is determined that the current light and shadow frequency and the respiratory rhythm have reached the adaptation state. After reaching the adaptation state, positive feedback is given through the auxiliary dynamic elements in the virtual scene to strengthen the patient's perception of rhythm synchronization; continuously comparing the real-time physiological rhythm data with the benchmark values in the physiological-mental dual-dimension anxiety benchmark model, calculating the promotion ratio of real-time heart rate variability relative to the heart rate variability benchmark and the change ratio of real-time skin electricity activity relative to the skin electricity activity benchmark, if the heart rate variability promotion ratio and the skin electricity activity decline ratio , the patient's anxiety state is determined to be relieved, and the level of light and shadow fluctuation can be automatically increased, if the skin electricity activity promotion ratio ,( which represents a deterioration threshold value, representing anxiety aggravation), the scene pacification mechanism is immediately started, the light and shadow contrast is adjusted to 1-k of the original value, k represents a contrast adjustment coefficient, and pacification voice (such as breathing in the rhythm of light and shadow) is issued through the virtual guide built in the scene to reduce sensory stimulation and relieve anxiety escalation.

[0021] It should be noted that during the entire adjustment process, all parameters can be dynamically adapted according to the initial anxiety level of the patient, for example, the value of a severe anxiety patient is smaller, which can trigger frequency adjustment earlier, and the value is lower, so as to identify anxiety aggravation earlier and start the pacification mechanism, and ensure that the dynamic change of the virtual scene is always accurately linked with the physiological state of the patient.

[0022] The embodiment of the present application also provides a preoperative anxiety relief system based on virtual reality technology, comprising: a data acquisition and personalized benchmark establishment module: obtaining physiological rhythm data of a patient before operation, the physiological rhythm data at least including breathing frequency and heart rate variation information; a dynamic virtual scene generation module: constructing a virtual natural scene with dynamic light and shadow changes based on the physiological rhythm data, the virtual natural scene containing light and shadow fluctuation elements corresponding to the breathing frequency; a guide and rhythm synchronization module: wearing a VR head-mounted display and a bone conduction earphone for the patient, the device automatically loads the customized virtual scene, and the virtual guide prompts breathing points in real time to help the patient establish a perceptual association with the rhythm elements; a dynamic rhythm adjustment module: analyzing the physiological data of the patient in real time to obtain the breathing frequency, heart rate variability and skin electricity activity value, and adjusting the light and shadow frequency step by step to approach the breathing frequency when the difference between the light and shadow fluctuation frequency and the breathing frequency exceeds the threshold value.

[0023] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0024] Those skilled in the art will appreciate that embodiments of the present application can be devised for a variety of applications. It is intended that the present application be limited only by the scope of the appended claims, and it is intended that various modifications and alterations made by those skilled in the art be considered as within the scope of the present application. The embodiments of the present application will be described with reference to the attached drawings, wherein:

[0025] The present application is described in reference to the drawings using a flowchart and / or a block diagram of the method, apparatus (system) and computer program product according to embodiments of the application. It will be understood that each block of the flowchart and / or block diagram, and combinations of blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 means for performing each of the functions specified in the flowchart and / or block diagram block or blocks.

[0026] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 means for performing each of the functions specified in the flowchart and / or block diagram block or blocks.

[0027] The computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 means for performing each of the functions specified in the flowchart and / or block diagram block or blocks.

[0028] While the preferred embodiments of the application have been described, additional variations and modifications can be employed by those skilled in the art. Therefore, the appended claims are intended to cover all such variations and modifications as falling within the scope of the present application.

[0029] Obviously, many modifications and variations of the present application are possible in light of the above teachings. It is, therefore, to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A method for relieving preoperative anxiety based on virtual reality technology, characterized in that: The following steps are involved: S101. Obtaining preoperative physiological rhythm data of the patient, where the physiological rhythm data includes at least respiratory rate and heart rate variation information; S102, constructing a virtual natural scene with dynamic light and shadow changes based on the physiological rhythm data, wherein the virtual natural scene includes light and shadow fluctuation elements corresponding to the respiratory frequency; S103: The patient wears a VR headset and bone conduction headphones. The device automatically loads a customized virtual scene and provides real-time breathing instructions through a virtual guide, helping the patient establish a perceptual connection with rhythmic elements. S104. Real-time analysis of the patient's physiological data to obtain respiratory rate, heart rate variability, and skin electrical activity values. When the difference between the light and shadow fluctuation frequency and the respiratory rate exceeds a threshold, the light and shadow frequency is adjusted step by step to approach the respiratory rate.

2. The method for alleviating preoperative anxiety based on virtual reality technology according to claim 1, characterized in that: In S101, preoperatively, integrated physiological monitoring equipment, including a chest-strap respiratory sensor, a wrist heart rate monitor, and a skin electrode sensor, continuously collects the patient's resting physiological rhythm data, including respiratory rate, heart rate variability, and skin electrode activity. A data cleaning algorithm removes motion interference and device noise outliers to form a standardized physiological data set. After the physiological data collection is completed, the patient's subjective anxiety score is collected using the anxiety self-rating scale. The scale contains n items, of which the positive scoring items are Reverse-scored items indivual, The scoring formula of the anxiety self-rating scale is as follows: ； in, represents the total score of the Self-Rating Anxiety Scale. represents the score of the positive scoring item, It represents the total score conversion coefficient. The specific calculation formula of the standard score of the anxiety self-rating scale is as follows: ； in, Indicates the rounding symbol. When , it means the patient is in normal condition. , indicating that the patient has a moderate tendency to anxiety, It indicates that the patient has a tendency to severe anxiety.

3. The method for alleviating preoperative anxiety based on virtual reality technology according to claim 2, characterized in that: Combine physiological data to build a physiological-psychological dual-dimensional anxiety benchmark model, including physiological indicators such as normalized respiratory rate values , heart rate variability normalized value , normalized values ​​of electrodermal activity , psychological indicators include subjective anxiety score and standard score of anxiety self-rating scale The specific calculation formula of the physiological-psychological dual-dimensional anxiety benchmark model is as follows: ； in, is the weight coefficient, satisfying , Represents the standardized denominator of the anxiety self-rating scale score, which is used to map the score to the [0,1] interval and set the anxiety index cutoff value to ,when When When the patient is in a moderate state of anxiety, When the patient is in a state of severe anxiety; Determine the patient's initial anxiety state and collect their preferences for virtual scene types, light and shadow sensitivity, and sound acceptance range through a preference questionnaire to provide a basis for scene customization. Based on the initial anxiety state, the initial parameters of the basic dynamic elements of the virtual scene are adjusted according to the anxiety level; By integrating individual physiological stability values, standard scores of the anxiety self-rating scale and corresponding physiological intervals, a physiological-psychological dual-dimensional anxiety benchmark model is generated, in which the physiological stability values ​​and standard scores of the anxiety self-rating scale are the benchmark values ​​for real-time monitoring.

4. The method for alleviating preoperative anxiety based on virtual reality technology according to claim 1, characterized in that: In S102, after obtaining the two-dimensional anxiety benchmark model and scene preference data, a virtual scene that matches the patient's preference is first retrieved from the scene material library. The essence of this virtual scene is a computer-generated simulated environment. The lighting effects in the scene are adjusted according to time changes to establish dynamic light and shadow changes. The construction process relies on a three-dimensional graphics rendering engine, which realizes the basic scene construction by importing the natural environment model and configuring the light source properties. The dynamic light and shadow changes are not preset animations, but are driven by physiological rhythm data. The change logic is directly related to the patient's individual physiological rhythm data.

5. The method for relieving preoperative anxiety based on virtual reality technology according to claim 4, characterized in that: The virtual scene includes light and shadow fluctuation elements corresponding to the respiratory frequency, and a mapping relationship between the respiratory frequency data and the light and shadow parameters is established. Suppose the respiratory frequency is , the light and shadow wave elements are , the mapping relationship is as follows: ； in, Indicates the frequency conversion coefficient, which can be adjusted according to the comfort of the scene. It represents the frequency offset. The higher the breathing frequency, the faster the light and shadow fluctuation frequency, achieving breathing-light and shadow rhythm synchronization. The breathing frequency signal is received in real time and converted into light and shadow fluctuation parameters. The patient's skin electrode activity value is extracted from the physiological-psychological dual-dimensional anxiety benchmark model, and the fluctuation amplitude is dynamically adjusted. If the skin electrode activity value is higher than the benchmark range, the fluctuation amplitude is reduced.

6. The method for alleviating preoperative anxiety based on virtual reality technology according to claim 1, characterized in that: In S103, after entering the preoperative preparation room, the patient wears a VR headset and bone conduction headphones. The bone conduction headphones use the principle of temporal bone vibration to transmit sound, ensuring that the patient can simultaneously receive the audio of the virtual scene and the instructions of the medical staff in the environment. After being put on, the device automatically loads the customized virtual scene. After the virtual scene is activated, the virtual guide in the scene issues voice guidance instructions. This instruction has two technical functions: S1. Ask the patient to focus on the light and shadow fluctuation elements that are synchronized with the breathing rate; S2. Clearly establish the relationship between the patient's spontaneous breathing behavior and the light and shadow fluctuation cycle. Voice commands are output through bone conduction headphones to ensure that the direction of the sound source matches the spatial position of the virtual guide. During the initial adaptation phase, a phased regulation strategy is implemented: S1. Maintain a fluctuation cycle that is completely consistent with the patient's preoperative baseline respiratory rate; S2. The virtual guide triggers voice prompts according to the nodes of the respiratory cycle. The voice prompts establish a mapping rule between the direction of light and dark changes and the respiratory phase: the process of light and shadow brightness rising from the lowest value to the highest value corresponds to the inhalation phase, and the process of light and shadow brightness falling from the highest value to the lowest value corresponds to the exhalation phase. Through voice prompts, patients are helped to establish a perceptual association between visual brightness gradient changes and respiratory muscle movements.

7. The method for relieving preoperative anxiety based on virtual reality technology according to claim 1, characterized in that: In S104, after physiological monitoring is started, the data collection time interval T is automatically set. Every T time, the patient's current physiological rhythm data, including real-time respiratory rate, real-time heart rate variability and real-time skin electrode activity, are synchronously collected through the integrated sensor, and the data is transmitted to the edge computing unit for rapid analysis. The timestamp of the original data is retained during the analysis process to ensure time synchronization with the dynamic adjustment of the virtual scene.

8. The method for alleviating preoperative anxiety based on virtual reality technology according to claim 7, characterized in that: After the analysis is completed, calculate the difference between the current light and shadow fluctuation frequency and the breathing frequency ,like Greater than the preset threshold The dynamic adjustment mechanism of light and shadow frequency is triggered with a fixed step size Gradually reduce the gap between the light and shadow fluctuation frequency and the real-time breathing frequency, and set the time interval for each adjustment to , until ,like , it is determined that the current light and shadow frequency and respiratory rhythm have reached an adaptation state. After reaching the adaptation state, positive feedback is given through auxiliary dynamic elements in the virtual scene to enhance the patient's perception of rhythm synchronization.

9. The method for alleviating preoperative anxiety based on virtual reality technology according to claim 7, characterized in that: Continuously compare real-time physiological rhythm data with the baseline values ​​in the physiological-psychological dual-dimensional anxiety baseline model, calculate the improvement ratio of real-time heart rate variability relative to the heart rate variability baseline and the change ratio of real-time skin electrodermal activity relative to the skin electrodermal activity baseline. If the heart rate variability improvement ratio is And the proportion of skin electrodermal activity decreased , it is determined that the patient's anxiety state is relieved, and the layering of light and shadow fluctuations can be automatically increased. If the skin electrical activity increases by , the scene soothing mechanism is immediately activated, the light and shadow contrast is reduced to 1-k of the original value, where k represents the contrast adjustment coefficient, and a soothing voice is issued through the built-in virtual guide in the scene to reduce sensory stimulation and alleviate escalating anxiety.

10. A preoperative anxiety relief system based on virtual reality technology is applied to a preoperative anxiety relief method based on virtual reality technology as claimed in any one of claims 1 to 9, characterized in that: include: Data collection and personalized benchmark establishment module: obtain the patient's preoperative physiological rhythm data, which at least includes respiratory rate and heart rate change information; Dynamic virtual scene generation module: This module constructs a virtual natural scene with dynamic light and shadow changes based on physiological rhythm data. The virtual natural scene includes light and shadow fluctuation elements corresponding to the respiratory rate. Guidance and Rhythm Synchronization Module: Patients wear a VR headset and bone conduction headphones, which automatically load a customized virtual scene. A virtual guide provides real-time breathing instructions, helping patients establish a perceptual connection with rhythmic elements. Dynamic rhythm adjustment module: Real-time analysis of the patient's physiological data to obtain respiratory rate, heart rate variability and skin electrodermal activity values. When the difference between the light and shadow fluctuation frequency and the respiratory rate exceeds the threshold, the light and shadow frequency is adjusted step by step to approach the respiratory rate.

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