State adjusting system and method based on intelligent wearable device and related product

Abstract

The invention provides a state adjustment system and method based on an intelligent wearable device and a related product, and the system comprises an execution unit which is configured to execute an adjustment scheme for a user; the data acquisition unit is configured to acquire physiological signals of the user before and in the execution process of the adjustment scheme to obtain state feedback data; and the main control unit is configured to adjust the scheme elements in the adjustment scheme based on the state feedback data. According to the state adjusting system, the physiological signals of the user in the process of executing the adjusting scheme can be monitored in real time, and the adjusting scheme is optimized based on changes of the physiological signals.

Description

Technical Field

[0001] This invention relates to the technical field of smart wearable devices, and more particularly to a state adjustment system, smart glasses, a state adjustment method, a computer-readable storage medium, and an electronic device based on a smart wearable device. Background Technology

[0002] Mental health is a major concern in modern society. Traditional psychotherapy methods include counseling and medication. Meanwhile, technological approaches are increasingly being applied, such as Virtual Reality (VR) and biosignal acquisition and feedback technology. VR technology creates virtual natural environments or tranquil scenes, allowing users to immerse themselves and temporarily escape the pressures of reality. Biosignal acquisition and feedback technology converts physiological information, such as heart rate and brainwaves, into visual signals that are then fed back to the user, helping them understand their physiological state and regulate themselves.

[0003] Current methods of using technology for psychotherapy primarily involve hypnosis through audio or video. For example, VR devices create highly realistic virtual environments, such as tranquil beaches or lush forests, allowing users to feel as if they are in nature, thereby relaxing their mind and body and relieving stress. Users wear VR headsets and controllers to interact and experience within the virtual environment. Alternatively, specific audio content, such as soothing music or hypnotic prompts, can be played to guide users into a relaxed or hypnotic state. Users typically listen to the audio through headphones and perform relaxation exercises in a quiet environment.

[0004] In practical applications, the effectiveness of audio or video hypnosis varies from person to person. Therefore, most users do not achieve the desired stress relief effect when using VR devices for hypnosis. Summary of the Invention

[0005] Based on the above situation, the main objective of this invention is to provide a state adjustment system based on a smart wearable device, which can monitor the physiological signals of the user in real time during the execution of the adjustment plan, and optimize the adjustment plan based on the changes in the physiological signals.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: The state adjustment system proposed in the first aspect of this invention includes: an execution unit configured to execute an adjustment scheme on a user, the adjustment scheme including at least two execution modes: visual mode, audio mode, and vibration mode; wherein each of the execution modes has at least one adjustable scheme element; a data acquisition unit configured to acquire the user's physiological signals before and during the execution of the adjustment scheme to obtain state feedback data; the state feedback data is the change of the physiological signals of the user over a period of time during the execution of the adjustment scheme; the physiological signals include at least one of the following: eye movement signal, electroencephalogram (EEG) signal, heart rate signal, and skin conductance signal; and a main control unit configured to adjust the scheme elements in the adjustment scheme based on the state feedback data.

[0007] Preferably, the main control unit is further configured to: confirm the adjustment mode to be executed, the adjustment mode including: sleep aid mode, stress relief mode and incentive mode; wherein, the sleep aid mode is used to hypnotize users who need sleep to help them enter a sleep state; the stress relief mode is used to relieve stress for users with high mental stress; the incentive mode is used to provide mental stimulation for users who need to improve their concentration and / or excitement; wherein, different adjustment modes correspond to different physiological indicators.

[0008] Preferably, the main control unit is further configured to: invoke a corresponding data acquisition unit based on the adjustment mode to acquire the user's physiological signals; assess the user's mental state based on the physiological signals; and determine the adjustment level of the adjustment mode based on the assessment result; wherein the adjustment level includes at least two levels, and the level of adjustment is proportional to the degree of intervention formed by the elements of the adjustment scheme; and invoke a corresponding execution unit based on the adjustment mode to execute an adjustment scheme matching the adjustment level on the user.

[0009] Preferably, the main control unit is further configured to: after acquiring the status feedback data, identify the user's adjustment feedback status based on the status feedback data, the adjustment feedback status including: status improvement, status maintenance, and status deterioration; if the adjustment feedback status is status improvement and the current adjustment level is not the lowest level, then reduce the adjustment level of the adjustment scheme; if the adjustment feedback status is status improvement and the current adjustment level is the lowest level, then stop executing the adjustment scheme; if the adjustment feedback status is status maintenance and the current adjustment level is not the highest level, then maintain the currently executing adjustment scheme or increase the adjustment level of the adjustment scheme; if the adjustment feedback status is status maintenance and the current adjustment level is the highest level, then maintain the currently executing adjustment scheme; if the adjustment feedback status is status deterioration and the current adjustment level is not the highest level, then increase the adjustment level of the adjustment scheme; if the adjustment feedback status is status deterioration and the current adjustment level is the highest level, then maintain the currently executing adjustment scheme.

[0010] Preferably, the main control unit is further configured to: acquire the user's identity identifier, and perform the following steps for the adjustment scheme corresponding to the identity identifier: based on the status feedback data of the completed adjustment scheme, record the adjustment effect corresponding to the scheme element or combination of scheme elements in the adjustment scheme; the status feedback data includes: the adjustment effect corresponding to different scheme elements or combinations of scheme elements in the adjustment scheme, the adjustment effect being represented by the changes in the user's physiological signals over a period of time; update the weights corresponding to different scheme elements in the adjustment scheme based on the adjustment effect.

[0011] Preferably, the main control unit includes: a state assessment model, which is capable of outputting the user's mental state assessment result based on the input physiological signals; wherein, the training samples of the state assessment model include: state monitoring data and / or state feedback data; the state monitoring data includes physiological parameters monitored when the user is in different mental states, and the state feedback data includes the adjustment effects corresponding to different scheme elements or combinations of scheme elements in the adjustment scheme, the adjustment effects being represented by changes in the user's physiological signals over a period of time; the state assessment model is capable of learning the correlation between the user's different mental states and physiological signals through the training samples.

[0012] Preferably, the main control unit includes: a scheme generation model, which can output an adjustment scheme adapted to the user's current state based on the physiological signals collected from the user; wherein, the training samples of the scheme generation model include: state feedback data; the scheme generation model can learn the adjustment effects of various scheme elements or combinations of scheme elements on the user, as well as the correlation between various scheme elements, through the training samples.

[0013] Preferably, the main control unit is further configured to perform preprocessing operations on the acquired physiological signals, the preprocessing operations including signal amplification, signal filtering, and algorithm averaging.

[0014] Preferably, the execution method further includes: a guidance method and / or an electrical stimulation method; the guidance method is used to guide the user to meditate, regulate breathing rhythm, or perform head movements through voice instructions; the electrical stimulation method is used to stimulate the user through pulsed current.

[0015] Preferably, the physiological indicators of the sleep aid mode include: electroencephalogram (EEG) signals, heart rate signals, and eye detection signals; the weighting order of the physiological signals is: EEG signals > heart rate signals > eye detection signals; the adjustment levels of the sleep aid mode include: a first level, a second level, and a third level; the intervention levels corresponding to the first level, the second level, and the third level increase sequentially; the scheme elements corresponding to the first level include only: white noise at a first volume; the scheme elements corresponding to the second level include: "white noise + theta wave induced audio" at a second volume, and warm-colored visual patterns with a first brightness and a first flicker frequency; the scheme elements corresponding to the third level include: "white noise + alpha wave induced audio" at a third volume, wherein warm-colored visual patterns with a second brightness and a second flicker frequency; wherein, the first volume < the second volume < the third volume, the first brightness < the second brightness, and the first flicker frequency < the second flicker frequency.

[0016] Preferably, the sleep aid mode further includes: an abnormal state intervention mechanism; the abnormal state intervention mechanism includes: when the main control unit detects an abnormal state event, stopping the execution of the adjustment scheme and waking the user through voice prompts and / or vibration prompts; the abnormal state events include: the time it takes for the user to enter a sleep state exceeds a preset duration; the heart rate signal and / or the skin conductance signal exceeds a safety threshold.

[0017] Preferably, the physiological indicators of the stress relief mode include: electroencephalogram (EEG) signals, heart rate signals, and skin conductance signals; the physiological indicators include quiet scene indicators and noisy scene indicators; the weighting of the quiet scene indicators is: EEG signals > heart rate signals > skin conductance signals; the weighting of the noisy scene indicators is: heart rate signals > skin conductance signals > EEG signals; the adjustment levels of the stress relief mode include: a first level and a second level; the intervention levels corresponding to the first level and the second level increase sequentially; the scheme elements corresponding to the second level include: a second volume of "natural sound + alpha wave induced audio", and a second brightness of green visual pattern; the scheme elements corresponding to the first level include: a first volume of "white noise", and a first brightness of green visual pattern; wherein, the first volume < the second volume, and the first brightness < the second brightness.

[0018] Preferably, the stress relief mode further includes an over-relaxation intervention mechanism; the over-relaxation intervention mechanism includes: after the second-level adjustment scheme has been executed for a preset duration, the user's physiological signals are collected by the data acquisition unit, and the user's mental state is assessed based on the currently collected physiological signals. If it is determined that the user is currently in an over-relaxed state, the following adjustment scheme is executed: the guidance mode is turned off, the visual mode is changed from a green visual pattern to a blue-green alternating pattern; the audio mode reduces the audio volume, maintains a natural sound, adds light music segments, and reduces the frequency band of alpha wave induced audio.

[0019] Preferably, the physiological indicators of the stimulation mode include: quiet scene indicators and noisy scene indicators; the quiet scene indicators include: electroencephalogram (EEG) signals, eye movement signals, and skin conductance signals; the weights of the quiet scene indicators are: EEG signals > eye movement signals > skin conductance signals; the noisy scene indicators include: heart rate signals, skin conductance signals, and eye movement signals; the weights of the noisy scene indicators are: heart rate signals > skin conductance signals > eye movement signals; the adjustment levels of the stimulation mode include: a first level and a second level; the intervention levels corresponding to the first level and the second level increase sequentially; the scheme elements corresponding to the second level include: fast-paced audio with a second volume and a second beat frequency, and a blue light pulse visual pattern with a second brightness; the scheme elements corresponding to the first level include: fast-paced audio with a first volume and a first beat frequency, and a blue light pulse visual pattern with a first brightness; wherein, the first volume < the second volume, the first beat frequency < the second beat frequency, and the first brightness < the second brightness.

[0020] Preferably, the incentive mode further includes an overstimulation intervention mechanism; the overstimulation intervention mechanism includes: after the second-level adjustment scheme has been executed for a preset duration, the user's physiological signals are collected by the data acquisition unit, and the user's mental state is assessed based on the currently collected physiological signals. If it is determined that the user is currently in an overexcited state, the following adjustment scheme is executed: the current stimulation mode is turned off, the visual mode is changed from a blue light pulse pattern to a blue-green pulse pattern, and the pulse frequency is reduced; the audio mode reduces the audio volume, replaces high-frequency sound waves with low-frequency white noise, and reduces the beat frequency of fast-paced audio.

[0021] Preferably, the execution unit includes at least one of the following: a visual display module configured to provide the user with a visual hypnotic signal of a specific pattern; an audio output module configured to provide the user with an audio hypnotic signal; and a vibration module configured to provide vibration massage to the user's temples. The data acquisition unit includes at least one of the following: an eyeball detection module configured to detect changes in the user's eyeballs, the changes in the eyeballs including at least one of blink frequency, eye movement frequency, and pupil diameter; an electroencephalogram (EEG) interface module configured to acquire the user's brainwave signals, the brainwave signals including at least one of alpha wave signals, beta wave signals, theta wave signals, and gamma wave signals; a heart rate detection module configured to acquire the user's heart rate signals; and a conductance detection module configured to acquire the user's skin conductance signals.

[0022] Preferably, the data acquisition unit further includes: an attitude detection module configured to detect the user's head attitude using an accelerometer and a gyroscope, and to monitor the user's head attitude in real time under the guidance mode to detect whether the user performs the correct head movement according to the voice instructions.

[0023] Preferably, the visual display module, the audio output module, the vibration module, the eye detection module, and the EEG interface module are integrated into the same smart glasses.

[0024] Preferably, the heart rate detection module and the conductivity detection module are integrated into the same smart bracelet or smartwatch.

[0025] Preferably, the EEG interface module includes four dry electrodes; wherein the four dry electrodes are symmetrically distributed on both sides of the temples of the smart glasses, with two dry electrodes on one temple respectively positioned near the temple and at the end of the temple.

[0026] Preferably, the temple of the smart glasses is provided with a moving device, and one of the dry electrodes on one side of the temple and the vibration module are disposed on the moving device, so that the dry electrode and the vibration module can move on the temple with the moving device.

[0027] Preferably, the dry electrode has an input mode and an output mode, wherein the input mode is used to acquire the EEG signal, and the output mode is used to release a stimulation current with adjustable intensity and frequency.

[0028] A second aspect of this invention provides a smart glasses system for executing an adjustment scheme to regulate a user's mental state. The adjustment scheme includes at least two execution modes: a visual mode, an audio mode, and a vibration mode; wherein each execution mode has at least one adjustable scheme element; the smart glasses include: A visual display module is configured to provide the user with visual hypnotic signals of specific patterns, thereby realizing the visual mode in the adjustment scheme; An audio output module is configured to provide audio hypnotic signals to the user, thereby implementing the audio mode in the aforementioned adjustment scheme; The vibration module is configured to provide vibration massage to the user's temples, thereby implementing the vibration mode in the adjustment scheme. An eye detection module is configured to detect changes in a user's eye movements, wherein the changes in eye movements include at least one of blink frequency, eye movement frequency, and pupil diameter. An electroencephalogram (EEG) interface module is configured to acquire a user's brainwave signals; the brainwave signals include at least one of alpha wave signals, beta wave signals, theta wave signals, and gamma wave signals. The main control unit is configured to, before and during the execution of the adjustment scheme, call the eye detection module and the EEG interface module to collect the user's physiological signals and obtain state feedback data; adjust the scheme elements in the adjustment scheme based on the state feedback data; the state feedback data is the change of the user's physiological signals over a period of time during the execution of the adjustment scheme; the physiological signals include: eye movement signals and EEG signals.

[0029] Preferably, the EEG interface module includes four dry electrodes; wherein the four dry electrodes are symmetrically distributed on both sides of the temples of the smart glasses, with two dry electrodes on one temple respectively positioned near the temple and at the end of the temple.

[0030] Preferably, the temple of the smart glasses is provided with a moving device, and one of the dry electrodes on one side of the temple and the vibration module are disposed on the moving device, so that the dry electrode and the vibration module can move on the temple with the moving device.

[0031] Preferably, the dry electrode has an input mode and an output mode, wherein the input mode is used to acquire the EEG signal, and the output mode is used to release a stimulation current with adjustable intensity and frequency.

[0032] Preferably, the execution method further includes: a guidance method; the guidance method is used to guide the user to meditate, adjust breathing rhythm, or perform head movements through voice instructions; the audio output module is also configured to implement the function of the guidance method.

[0033] Preferably, the smart glasses further include: a posture detection module, configured to detect the user's head posture using an accelerometer and a gyroscope, and to monitor the user's head posture in real time under the guidance mode to detect whether the user performs the correct head movements according to the voice instructions.

[0034] A third aspect of this invention provides a state adjustment method based on a smart wearable device, comprising: An adjustment scheme is implemented on the user, the adjustment scheme including at least two of the following execution modes: visual mode, audio mode, and vibration mode; wherein each of the execution modes has at least one adjustable scheme element; before and during the implementation of the adjustment scheme, the user's physiological signals are collected to obtain state feedback data; the state feedback data is the change of the user's physiological signals over a period of time during the implementation of the adjustment scheme; the physiological signals include at least one of the following: eye movement signal, electroencephalogram (EEG) signal, heart rate signal, and skin conductance signal; the scheme elements in the adjustment scheme are adjusted based on the state feedback data.

[0035] Preferably, before implementing the adjustment plan on the user, the process includes: confirming the adjustment mode to be executed, wherein the adjustment mode includes: a sleep aid mode, a stress relief mode, and a motivation mode; wherein, the sleep aid mode is used to hypnotize users who need sleep in order to help them enter a sleep state; the stress relief mode is used to relieve stress for users with high levels of mental stress; and the motivation mode is used to provide mental stimulation for users who need to improve their concentration and / or arousal; wherein, different adjustment modes correspond to different physiological indicators.

[0036] Preferably, after confirming the adjustment mode to be executed, the method further includes: collecting the user's physiological signals based on the adjustment mode; assessing the user's mental state based on the physiological signals; determining the adjustment level of the adjustment mode based on the assessment result; wherein the adjustment level includes at least two levels, and the level of adjustment is proportional to the degree of intervention formed by the elements of the adjustment plan; and executing an adjustment plan matching the adjustment level on the user based on the adjustment mode.

[0037] Preferably, adjusting the scheme elements in the adjustment scheme based on the status feedback data includes: after obtaining the status feedback data, identifying the user's adjustment feedback status based on the status feedback data, the adjustment feedback status including: status improvement, status maintenance, and status deterioration; if the adjustment feedback status is status improvement and the current adjustment level is not the lowest level, then reducing the adjustment level of the adjustment scheme; if the adjustment feedback status is status improvement and the current adjustment level is the lowest level, then stopping the execution of the adjustment scheme; if the adjustment feedback status is status maintenance and the current adjustment level is not the highest level, then maintaining the currently executing adjustment scheme or increasing the adjustment level of the adjustment scheme; if the adjustment feedback status is status maintenance and the current adjustment level is the highest level, then maintaining the currently executing adjustment scheme; if the adjustment feedback status is status deterioration and the current adjustment level is not the highest level, then increasing the adjustment level of the adjustment scheme; if the adjustment feedback status is status deterioration and the current adjustment level is the highest level, then maintaining the currently executing adjustment scheme.

[0038] Preferably, adjusting the scheme elements in the adjustment scheme based on the state feedback data includes: obtaining the user's identity identifier, and performing the following steps on the adjustment scheme corresponding to the identity identifier: recording the adjustment effect corresponding to the scheme element or combination of scheme elements in the adjustment scheme based on the state feedback data of the completed adjustment scheme; updating the weights corresponding to different scheme elements in the adjustment scheme based on the adjustment effect; the state feedback data includes: the adjustment effect corresponding to different scheme elements or combinations of scheme elements in the adjustment scheme, and the adjustment effect is represented by the changes in the user's physiological signals over a period of time.

[0039] Preferably, the assessment of the user's mental state based on the physiological signals includes: inputting the collected physiological signals into a state assessment model, and obtaining the mental state assessment result output by the state assessment model; wherein, the training samples of the state assessment model include: state monitoring data and / or state feedback data; the state monitoring data includes physiological parameters monitored when the user is in different mental states, and the state feedback data includes the regulatory effects corresponding to different scheme elements or combinations of scheme elements in the regulation scheme, the regulatory effects being represented by changes in the user's physiological signals over a period of time; the state assessment model can learn the correlation between the user's different mental states and physiological signals through the training samples.

[0040] Preferably, after collecting the user's physiological signals based on the regulation mode, the method further includes: inputting the collected physiological signals into a scheme generation model to obtain a regulation scheme output by the scheme generation model that is adapted to the user's current state; wherein, the training samples of the scheme generation model include: the state feedback data; the scheme generation model can learn the regulation effects of various scheme elements or combinations of scheme elements on the user, as well as the correlation between various scheme elements, through the training samples.

[0041] Preferably, after collecting the user's physiological signals based on the adjustment mode, the method further includes: performing preprocessing operations on the collected physiological signals, the preprocessing operations including: signal amplification, signal filtering, and algorithm averaging.

[0042] Preferably, the execution method further includes: a guidance method and / or an electrical stimulation method; the guidance method is used to guide the user to meditate, regulate breathing rhythm, or perform head movements through voice instructions; the electrical stimulation method is used to stimulate the user through pulsed current.

[0043] Preferably, the physiological indicators of the sleep aid mode include: electroencephalogram (EEG) signals, heart rate signals, and eye detection signals; the weighting order of the physiological signals is: EEG signals > heart rate signals > eye detection signals; the adjustment levels of the sleep aid mode include: a first level, a second level, and a third level; the intervention levels corresponding to the first level, the second level, and the third level increase sequentially; the scheme elements corresponding to the first level include only: white noise at a first volume; the scheme elements corresponding to the second level include: "white noise + theta wave induced audio" at a second volume, and a warm-colored visual pattern with a first brightness and a first flashing frequency; the scheme elements corresponding to the third level include: "white noise + alpha wave induced audio" at a third volume, and a warm-colored visual pattern with a second brightness and a second flashing frequency; wherein, the first volume < the second volume < the third volume, the first brightness < the second brightness, and the first flashing frequency < the second flashing frequency.

[0044] Preferably, the sleep aid mode further includes: an abnormal state intervention mechanism; the abnormal state intervention mechanism includes: when an abnormal state event is detected, stopping the execution of the adjustment scheme and waking the user up through voice prompts and / or vibration prompts; the abnormal state event includes: the time it takes for the user to enter a sleep state exceeds a preset duration; the heart rate signal and / or the skin conductance signal exceed a safety threshold.

[0045] Preferably, the physiological indicators of the stress relief mode include: electroencephalogram (EEG) signals, heart rate signals, and skin conductance signals; the physiological indicators include quiet scene indicators and noisy scene indicators; the weighting of the quiet scene indicators is: EEG signals > heart rate signals > skin conductance signals; the weighting of the noisy scene indicators is: heart rate signals > skin conductance signals > EEG signals; the adjustment levels of the stress relief mode include: a first level and a second level; the intervention levels corresponding to the first level and the second level increase sequentially; the scheme elements corresponding to the second level include: a second volume of "natural sound + alpha wave induced audio", and a second brightness of green visual pattern; the scheme elements corresponding to the first level include: a first volume of "white noise", and a first brightness of green visual pattern; wherein, the first volume < the second volume, and the first brightness < the second brightness.

[0046] Preferably, the stress relief mode further includes: an over-relaxation intervention mechanism; The over-relaxation intervention mechanism includes: after the second-level adjustment scheme has been executed for a preset duration, collecting the user's physiological signals, assessing the user's mental state based on the currently collected physiological signals, and if it is determined that the user is currently in an over-relaxed state, then executing the following adjustment scheme: turning off the guidance mode, changing the visual mode from a green visual pattern to a blue-green alternating pattern; reducing the audio volume of the audio mode, maintaining a natural sound, adding light music segments, and reducing the frequency band of the alpha wave induced audio.

[0047] Preferably, the physiological indicators of the stimulation mode include: quiet scene indicators and noisy scene indicators; the quiet scene indicators include: electroencephalogram (EEG) signals, eye movement signals, and skin conductance signals; the weights of the quiet scene indicators are: EEG signals > eye movement signals > skin conductance signals; the noisy scene indicators include: heart rate signals, skin conductance signals, and eye movement signals; the weights of the noisy scene indicators are: heart rate signals > skin conductance signals > eye movement signals. The adjustment levels of the excitation mode include: a first level and a second level; the intervention levels corresponding to the first level and the second level increase sequentially; the scheme elements corresponding to the second level include: a fast-paced audio with a second volume and a second beat frequency, and a blue light pulse visual pattern with a second brightness; the scheme elements corresponding to the first level include: a fast-paced audio with a first volume and a first beat frequency, and a blue light pulse visual pattern with a first brightness; wherein, the first volume < the second volume, the first beat frequency < the second beat frequency, and the first brightness < the second brightness.

[0048] Preferably, the incentive mode further includes: an overstimulation intervention mechanism; The overstimulation intervention mechanism includes: after executing the second-level regulation scheme for a preset duration, collecting the user's physiological signals, assessing the user's mental state based on the currently collected physiological signals, and if it is determined that the user is currently in a state of over-excitement, then executing the following regulation scheme: turning off the electrical stimulation mode, changing the visual mode from a blue light pulse pattern to a blue-green pulse pattern, and reducing the pulse frequency; reducing the audio volume in the audio mode, replacing high-frequency sound waves with low-frequency white noise, and reducing the beat frequency of fast-paced audio.

[0049] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the state adjustment method as described in any of the third aspects above.

[0050] An electronic device according to a fifth aspect of the present invention includes a storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements the state adjustment method as described in the third aspect above.

[0051] In the adjustment scheme of this invention embodiment, there are at least two execution modes (e.g., any two of visual, audio, and vibration modes). Regardless of the execution mode, each mode has at least one adjustable scheme element. During the execution of the adjustment scheme on the user, the data acquisition unit can collect the user's physiological signals in real time to obtain state feedback data that reflects the changes in the user's physiological signals over a period of time. This allows the main control unit to determine the adjustment effect of the current adjustment scheme on the user based on the state feedback data, and to adjust the scheme elements in the adjustment scheme based on the adjustment effect to optimize the current adjustment scheme.

[0052] Other beneficial effects of the present invention will be explained in detail through the introduction of specific technical features and technical solutions in specific embodiments. Those skilled in the art should be able to understand the beneficial technical effects brought about by these technical features and technical solutions through the introduction of these technical features and technical solutions. Attached Figure Description

[0053] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings.

[0054] Figure 1 This invention illustrates an architecture diagram of a state adjustment system based on a smart wearable device in an embodiment of the present invention. Figure 2 This illustrates another architecture diagram of a state adjustment system based on a smart wearable device in an embodiment of the present invention; Figure 3 This diagram illustrates a state feedback data embodiment of the present invention. Figure 4 This illustrates another schematic diagram of state feedback data in an embodiment of the present invention; Figure 5 This diagram illustrates a structural schematic of smart glasses according to an embodiment of the present invention. Figure 6 This figure shows a structural schematic diagram of the state adjustment system in an embodiment of the present invention; Figure 7 This diagram illustrates a flow chart of a state adjustment method based on a smart wearable device according to an embodiment of the present invention. Figure 8 This illustrates another flowchart of the state adjustment method based on a smart wearable device in an embodiment of the present invention; Figure 9 This illustrates another flowchart of the state adjustment method based on a smart wearable device in an embodiment of the present invention; In the picture: 10. Data acquisition unit; 101. Eye detection module; 102. Brain-computer interface module; 1021. Dry electrode; 103. Heart rate detection module; 104. Conductivity detection module; 105. Posture detection module; 20. Main control unit; 30. Execution unit; 301. Video display module; 302. Audio output module; 303. Vibration module; 40. Mobile device; 801. Smart glasses; 802. Smartwatch; 803. EEG detection device. Detailed Implementation

[0055] The present invention is described below based on embodiments, but the present invention is not limited to these embodiments. In the following detailed description of the present invention, some specific details are described in detail, but well-known methods, processes, procedures, and elements are not described in detail in order to avoid obscuring the essence of the present invention.

[0056] Furthermore, those skilled in the art should understand that the accompanying drawings provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0057] Unless the context explicitly requires it, the words "comprising," "including," and similar terms throughout the specification and claims should be interpreted as encompassing rather than being exclusive or exhaustive; that is, meaning "including but not limited to."

[0058] In the description of this invention, it should be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0059] As described above, different users react differently to different types of audio / video hypnosis, and different users also have different sensitivities to the same hypnosis program. Therefore, existing hypnosis programs are difficult to meet the needs of most users for stress relief.

[0060] In view of this, embodiments of the present invention provide a state adjustment system based on a smart wearable device, which can monitor the physiological signals of the user in real time during the execution of the adjustment plan, and optimize the adjustment plan based on the changes in the physiological signals.

[0061] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0062] Please see Figure 1 and Figure 2 , Figure 1 This diagram illustrates an architecture of a state adjustment system based on a smart wearable device according to an embodiment of the present invention. Figure 2 This diagram illustrates another architecture of a state adjustment system based on a smart wearable device according to an embodiment of the present invention. The state adjustment system based on a smart wearable device according to this embodiment includes: an execution unit, a data acquisition unit, and a main control unit, wherein: The execution unit is configured to perform an adjustment scheme on the user. In this embodiment of the invention, the adjustment scheme is a comprehensive scheme for adjusting the user's mental state set within the system. This adjustment scheme includes at least two execution methods: visual, audio, and vibration, wherein each execution method has at least one adjustable scheme element. This scheme element can be a replaceable element in one execution method (e.g., pattern / music), or it can be an adjustable parameter in one execution method (e.g., vibration frequency or intensity).

[0063] For example, if the execution method is visual, the scheme element can be a black and white alternating spiral pattern, a pulsed green light dynamic pattern, or a wavy blue light pattern; if the execution method is audio, the scheme element can be light music or white noise. If the execution method is vibration, the scheme element can be vibration frequency or vibration intensity.

[0064] The data acquisition unit is configured to collect the user's physiological signals before and during the execution of the regulation plan, obtaining status feedback data. This status feedback data reflects the changes in the user's physiological signals over a period of time during the execution of the regulation plan. This period can be a preset duration, and the frequency of physiological signal collection can be set according to actual needs. The changes can include the direction of change (e.g., deterioration or improvement of indicators) and the degree of change (e.g., the magnitude of improvement). Specifically, the status feedback data can reflect the overall physiological signal changes corresponding to the regulation plan, or it can reflect the physiological signal changes corresponding to different elements or combinations of different elements within the regulation plan.

[0065] In embodiments of the present invention, physiological signals may include at least one of the following: eye movement signals, electroencephalogram (EEG) signals, heart rate signals, and skin conductance signals.

[0066] Eye movement signals include the user's gaze, blink frequency, eye movement frequency, and pupil diameter. Specifically, when a user is tense, their eyes may rapidly scan left and right, blink frequently, or twitch frequently, while in a relaxed state, eye movements are relatively smooth and slow. By continuously monitoring these subtle changes, the user's emotional state and level of concentration can be comprehensively judged through their gaze, blink frequency, and eye movement frequency. Furthermore, pupil diameter is related to alertness; it is largest when a person is fully awake and constricts when a person is drowsy or asleep. Eye movement signals can be collected using an eye detection module (e.g., an infrared optical eye-tracking device).

[0067] Electroencephalogram (EEG) signals include alpha, beta, theta, and gamma waves. Alpha waves are positively correlated with states of calm and relaxation; the more relaxed the user, the stronger the alpha wave signal. Beta waves are positively correlated with states of alertness and tension; the more tense the user, the stronger the beta wave signal. Theta waves are positively correlated with states of meditation and sleep; the closer the user is to a sleep state, the stronger the theta wave signal. Gamma waves are positively correlated with the state of synchronous integration of information from multiple brain regions; the more excited and focused the user, the stronger the gamma wave signal. EEG signals can be acquired using EEG interface modules (e.g., non-invasive dry electrodes).

[0068] Heart rate signals include heart rate variability (HRV), which refers to the minute fluctuations in the time interval between consecutive heartbeats. It can be represented by the ratio of the sympathetic left sympathetic (LF) to the parasympathetic right sympathetic (HF) ratio. Under stress, the LF / HF ratio is greater than 2.0, while under relaxation, it is less than or equal to 1.0. Heart rate variability can be acquired using heart rate detection modules (e.g., photoplethysmogram (PPG) sensors).

[0069] Skin conductance signals are specifically referred to as electrical activity (EDA). EDA signals comprise two core components: skin conductance response (SCR) and skin conductance level (SCL). In a relaxed state, the SCL decreases by 20%-30%, while the SCR tends to stabilize. Skin conductance signals can be acquired using a conductance detection module (e.g., an EDA sensor).

[0070] The main control unit is configured to adjust the scheme elements in the adjustment scheme based on status feedback data. In this invention, the status feedback data reflects the adjustment effect of the current adjustment scheme on the user, enabling the main control unit to adjust the scheme elements in the current adjustment scheme based on the adjustment effect. Furthermore, the adjustment of scheme elements can include two methods: one is to adjust at different stages of the same execution scheme, and the other is to adjust the scheme elements in the next adjustment scheme for the same user.

[0071] Furthermore, the collected eye movement signals, electroencephalogram (EEG) signals, heart rate signals, or skin conductance signals may generate noise due to factors such as the collection scene, collection accuracy, and wearing conditions. Therefore, the main control unit in this embodiment of the invention is also configured to perform preprocessing operations on the collected physiological signals. Preprocessing improves the validity of the data. The preprocessing operations include signal amplification, signal filtering, and algorithm averaging. Specifically, the algorithm averaging can be a sliding window incremental averaging algorithm.

[0072] In this embodiment of the invention, the adjustment scheme includes at least two execution modes (e.g., visual mode, audio mode, and vibration mode). Regardless of the execution mode, it has at least one adjustable scheme element. During the execution of the adjustment scheme on the user, the data acquisition unit can collect the user's physiological signals in real time to obtain state feedback data that reflects the changes in the user's physiological signals over a period of time. This allows the main control unit to determine the adjustment effect of the current adjustment scheme on the user based on the state feedback data, and to adjust the scheme elements in the adjustment scheme based on the adjustment effect to optimize the current adjustment scheme.

[0073] For the same level of intervention adjustment scheme, different users have different sensitivities. It is difficult to match the appropriate level of intervention for the current user before implementing the adjustment scheme. Therefore, this invention proposes a method for feedback adjustment during the implementation of the adjustment scheme, including: The main control unit of the state regulation system confirms the regulation mode to be executed. Specifically, the state regulation system in this embodiment of the invention can collect physiological signals from multiple dimensions and has multiple execution methods, enabling it to achieve various regulation modes, such as sleep aid mode, stress relief mode, and incentive mode. The sleep aid mode is used to hypnotize users who need sleep, helping them enter a sleep state; its application scenarios include: afternoon naps, difficulty falling asleep, and frequent awakenings at night. The stress relief mode is used to relieve stress for users with high levels of mental stress; its application scenarios include: work / study breaks, feeling stressed before speeches / exams, and recovery after exercise. The incentive mode is used to provide mental stimulation for users who need to improve focus and / or excitement; its application scenarios include: before exercise, and boosting spirits before meetings / driving. Different regulation modes correspond to different physiological indicators, and some physiological indicators are even opposite. For example, the indicator of brainwave signals is the ratio of alpha waves to beta waves. In the stress relief mode, a higher ratio indicates a better stress-relieving effect; while in the incentive mode, a higher ratio indicates a worse incentive effect. Therefore, before implementing the adjustment plan, it is necessary to first identify the adjustment mode to be implemented.

[0074] After determining the adjustment mode, the main control unit calls the corresponding data acquisition unit to collect the user's physiological signals. After collecting the physiological signals, the user's mental state is assessed based on these signals, and the adjustment level of the adjustment mode is determined based on the assessment results. After confirming the adjustment level, the corresponding execution unit is called to execute an adjustment plan matching the adjustment level. After the adjustment plan has been executed for a preset duration, the main control unit acquires the current state feedback data at a preset frequency and identifies the user's adjustment feedback state (state improvement, state maintenance, and state deterioration) based on the state feedback data, adjusting the adjustment level of the adjustment plan accordingly. In this embodiment, the state feedback data reflects the overall changes in physiological signals corresponding to the adjustment plan, and the adjustments in this embodiment target different stages of the same execution plan.

[0075] For any given adjustment mode, the system pre-sets multiple adjustment schemes at different levels. Different adjustment levels correspond to different degrees of intervention (the adjustment level is directly proportional to the degree of intervention). If the user's current mental state is closer to the adjustment goal, a lower-level adjustment scheme is used; if the user's current mental state is further away from the adjustment goal, a higher-level adjustment scheme is used. Specifically, if the adjustment feedback status is "state improvement" and the current adjustment level is not the lowest, the adjustment scheme's level is lowered; if the adjustment feedback status is "state improvement" and the current adjustment level is the lowest, the adjustment scheme is stopped; if the adjustment feedback status is "state maintenance" and the current adjustment level is not the highest, the currently executing adjustment scheme remains unchanged or its adjustment level is increased; if the adjustment feedback status is "state maintenance" and the current adjustment level is the highest, the currently executing adjustment scheme remains unchanged; if the adjustment feedback status is "state deterioration" and the current adjustment level is not the highest, the adjustment scheme's level is increased; if the adjustment feedback status is "state deterioration" and the current adjustment level is the highest, the currently executing adjustment scheme remains unchanged.

[0076] For example, suppose the first adjustment scheme includes two adjustment levels. The first-level first adjustment scheme includes scheme element A1 (vibration mode) and scheme element B1 (audio mode), and the second-level first adjustment scheme includes scheme element A2 (vibration mode) and scheme element B2 (audio mode). Scheme elements A2 and B2 have a greater intervention level than scheme elements A1 and B1. Before executing the first adjustment scheme, suppose the user's physiological signal is detected as a first value. Corresponding to the second-level first adjustment scheme, after executing the first adjustment scheme for 5 minutes, the user's physiological signal is detected as a second value. The first value is used as a reference midpoint, and a preset range above and below the first value is used as a reference interval. If the second value is within the reference interval, the user's adjustment feedback state is "state maintained"; if the second value is greater than the reference interval, the user's adjustment feedback state is "state deteriorated"; if the second value is less than the reference interval, the user's adjustment feedback state is "state improved". The main control unit identifies the user's adjustment feedback state based on the current status feedback data. If the current adjustment feedback state is an improvement, the current first adjustment plan is adjusted from the second level to the first level, reducing the degree of intervention of the plan elements on the user, thereby making the user's mental state more naturally approach the adjustment goal of the first adjustment plan.

[0077] It should be noted that the above assumptions are merely examples. In practical applications, physiological signals can include multiple dimensions. There are other methods to determine a user's mental state based on physiological signals of multiple dimensions, which will not be elaborated here.

[0078] In practical applications, due to differences in the sensitivity of different users to the same adjustment scheme, the initial intervention level of the adjustment scheme may be insufficient to meet the user's adjustment needs. Therefore, for any adjustment mode, this embodiment of the invention sets multiple adjustment levels, with different adjustment levels corresponding to different intervention levels. If the initial adjustment level is effective (e.g., state improvement), the intervention level is gradually reduced; if the initial adjustment level is ineffective (e.g., state deterioration or persistent state maintenance), the intervention level is increased. This embodiment of the invention can dynamically adjust the adjustment scheme according to the actual adjustment situation, adapting to the sensitivity of different users to different schemes, thereby optimizing the adjustment effect of the adjustment scheme.

[0079] Since different users have varying sensitivities to different types of scheme elements, this invention proposes a method for adjusting subsequent schemes based on historical data of prior adjustment effects. This method can configure adjustment schemes tailored to individual users, including: The main control unit acquires the user's identity identifier. For example, facial recognition can be performed using a camera module to match the user's identity identifier recorded in the system. Alternatively, the user's identity identifier can be acquired through identity information input or voice recognition; the specific method is not limited here. After confirming the user's identity, if the system supports multiple adjustment modes, the user can select the desired adjustment mode (e.g., sleep aid mode, stress relief mode, and motivation mode). If the system only supports one adjustment mode, the user does not need to select a mode.

[0080] The main control unit calls the corresponding execution unit based on the adjustment mode to be executed, and performs the adjustment plan on the user. Before and during the execution of the adjustment plan, the data acquisition unit can collect various physiological signals of the user in real time to obtain status feedback data. Specifically, the status feedback data in this embodiment reflects the changes in physiological signals corresponding to different elements or combinations of different elements in the adjustment plan.

[0081] After the adjustment plan is executed and status feedback data is obtained, the main control unit can adjust the adjustment plan specific to the current user. Specifically, the main control unit can record the adjustment effects corresponding to different plan elements or combinations of plan elements in the adjustment plan based on the status feedback data of the executed adjustment plan; based on the current adjustment effect and historical data (e.g., the adjustment effects of previous plan elements), it updates the weights corresponding to different plan elements in the adjustment plan. When generating the user's adjustment plan later, it can select several plan elements with the highest weight values ​​to form the adjustment plan. In another implementation, a private generation model can be established for individual users. This generation model can use the status feedback data after each execution of the adjustment plan as training samples to generate an adjustment plan adapted to the user.

[0082] For example, suppose that a first adjustment scheme is used to regulate the mental state of a first user for the first time, and a second adjustment scheme is used to regulate the mental state of the same user for the second time. The first adjustment scheme includes two processes: a first process is executed for 5 minutes, followed by a second process for 5 minutes. The first process includes a visual scheme element A1 and an audio scheme element B1, and the second process includes a visual scheme element A2 and an audio scheme element B2. Before executing the first adjustment scheme, the user's physiological signal is detected as a first value; after executing the first process, the user's physiological signal is detected as a second value; and after executing the second process, the user's physiological signal is detected as a third value. The second adjustment scheme also includes two processes: a first process is executed for 5 minutes, followed by a second process for 5 minutes. The first process includes a visual scheme element A2 and an audio scheme element B2, and the second process includes a visual scheme element A1 and an audio scheme element B1. Before executing the second adjustment scheme, the user's physiological signal is detected as a fourth value; after executing the first process, the user's physiological signal is detected as a fifth value; and after executing the second process, the user's physiological signal is detected as a sixth value. By conducting comparative tests on adjustment schemes for the same user (e.g., comparing two adjustment schemes with the same elements but different execution orders), the adjustment effect of specific scheme elements on the user can be quantified. This allows for targeted adjustment scheme settings for the user in the future, thereby optimizing the adjustment effect of the adjustment scheme for the specific user.

[0083] It should be noted that the above assumptions are merely examples. In practical applications, there can be multiple combinations of solution elements, and adjusting the weights of solution elements needs to be determined based on more scenarios and the number of times they are used. No specific limitations are made here.

[0084] In practical applications, different users have varying sensitivities to different types of program elements. For example, some users are more susceptible to audio hypnosis, while others are more susceptible to visual image hypnosis, some to rotating spiral patterns, and others to recurring wavy patterns. Different program elements have varying effects on users. This invention quantifies the effects of different program elements on users by using historical data from multiple executions of different conditioning programs. This allows for the adjustment of the weights of different program elements, and the selection of several elements with higher weights to form a personalized conditioning program for that user, thereby providing a better mental state conditioning effect.

[0085] In practical applications, different regulation modes have different settings in multiple dimensions such as regulation goals, physiological indicators, regulation plans, and the presentation of intervention levels. The following describes three regulation modes: sleep aid mode, stress relief mode, and incentive mode: The details of the sleep aid mode are as follows: Target objective: To help users fall asleep.

[0086] Physiological indicators include electroencephalogram (EEG) signals, heart rate signals, and eye movement detection signals. In sleep-aid mode, the weighting of these three physiological indicators is as follows: EEG signals > heart rate signals > eye movement detection signals. For example, EEG signals account for 50%, heart rate signals (HRV) account for 35%, and eye movement detection signals (eye movement frequency) account for 15%.

[0087] Adjustment Levels: The adjustment schemes in the sleep aid mode include a first level, a second level, and a third level. The adjustment level is directly proportional to the degree of intervention of the scheme elements. For example, in this embodiment of the invention, the brainwave signal, heart rate signal, and eye movement detection signal can be normalized separately. Assuming the brainwave signal is quantized as a first value 'a', the heart rate signal as a second value 'b', and the eye movement detection signal as a third value 'c', the comprehensive value S of the physiological indicators in the sleep aid mode is S = 50%a + 35%b + 15%c. The larger the comprehensive value S, the closer it is to the adjustment target. Before assessing the user's current mental state, multiple numerical intervals can be set to match different adjustment levels. For example, in terms of interval values, the first interval > the second interval > the third interval. The first interval corresponds to the first level (i.e., if the comprehensive value S corresponding to the current mental state assessment result is in the first interval, then the adjustment scheme of the first level is matched), the second interval corresponds to the second level, and the third interval corresponds to the third level.

[0088] Adjustment Scheme: The sleep aid mode may include three stages. In this embodiment of the invention, the three stages of the sleep aid mode can correspond to three adjustment levels of the adjustment scheme. For example, the first stage corresponds to the third level (i.e., the third-level adjustment scheme is used to adjust the mental state of the user in the first stage), the second stage corresponds to the second level, and the third stage corresponds to the third level. Furthermore, the user's current stage can be determined by the range of values ​​within which the aforementioned comprehensive value S falls.

[0089] The first stage is the sleep latency period, where the goal is to guide the user from wakefulness into a light sleep state. This stage employs a level-three conditioning approach, implemented through visual, audio, and vibrational methods. Further, guided methods may be included, primarily using voice instructions to guide the user through meditation or regulating breathing rhythm. For example, the visual method includes dynamic warm light guidance with a visual pattern of a slowly contracting circle. The audio method includes white noise (e.g., rain or stream sounds) combined with alpha wave-induced audio. The guided method includes voice instructions for the user to perform diaphragmatic breathing (4-12 breaths / minute). The vibrational method includes low-frequency intermittent vibrations to the temples (50Hz, <0.3g), each vibration lasting 2 seconds (intensifying during exhalation to promote relaxation).

[0090] The second stage is the light sleep stage, characterized by a theta wave proportion >30%. The regulatory goal of this stage is to consolidate sleep and avoid disturbances. The second stage employs a second-level regulatory scheme, implemented through visual, audio, and vibration methods. The second level reduces the level of disturbance compared to the third level, primarily by: 1. Disabling guided sleep; 2. Reducing the brightness of warm light and the flashing frequency of visual patterns; 3. Reducing the volume of audio, replacing alpha wave-induced audio with theta wave-induced audio; 4. Reducing the vibration frequency and intensity (e.g., frequency 30Hz, intensity <0.1g).

[0091] The third stage is deep sleep, characterized by a theta wave proportion >50%. The goal of this stage is to maintain sleep. The third stage employs a first-level regulation scheme, implemented through audio-based methods. The first level reduces interference compared to the second level, primarily by: 1. further disabling visual and vibrational methods; 2. retaining only white noise in the audio mode, with the volume further reduced. Furthermore, after maintaining the third stage for a preset duration, the regulation scheme can be activated.

[0092] Abnormal state intervention mechanism: When the main control unit detects an abnormal state event, it stops executing the current adjustment plan and wakes up the user through voice prompts and / or vibration prompts; among which, abnormal state events include: the user's sleep state time exceeds the preset time; Heart rate and / or skin conductance signals exceed safety thresholds. Specifically, when HRV or EDA signals exceed safety thresholds, it indicates that the user's current emotional fluctuations are abnormal and it is not appropriate to continue implementing the adjustment plan.

[0093] The details of the stress relief mode are as follows: Regulation objective: To relieve users' tension and / or high-pressure state.

[0094] Physiological indicators include electroencephalogram (EEG) signals, heart rate signals, and skin conductance signals. Because EEG signals are easily interfered with by noisy environments, different physiological indicators are used in different usage scenarios. Specifically, they can be divided into quiet scenario indicators and noisy scenario indicators; the weighting for quiet scenario indicators is: EEG signal > heart rate signal > skin conductance signal. For example, EEG signals (e.g., EEG) account for 50%, heart rate signals (e.g., HRV) account for 35%, and skin conductance signals (e.g., EDA) account for 15%. The weighting for noisy scenario indicators is: heart rate signal > skin conductance signal > EEG signal. For example, heart rate signals (e.g., HRV) account for 50%, skin conductance signals (e.g., EDA) account for 35%, and EEG signals (e.g., EEG) account for 15%.

[0095] Regulation Levels: The regulation scheme in the stress relief mode includes a first level and a second level. The level of regulation is directly proportional to the degree of intervention of the scheme elements. For example, assuming the user is currently in a quiet environment, this embodiment of the invention can normalize the EEG signal, heart rate signal, and skin conductance signal respectively. Assuming the EEG signal is quantized as a first value 'a', the heart rate signal as a second value 'b', and the skin conductance signal as a third value 'c', the comprehensive value P of the physiological indicators in the stress relief mode is P = 50%a + 35%b + 15%c. The larger the comprehensive value P, the closer it is to the regulation target. Before assessing the user's current mental state, multiple numerical intervals can be set to match different regulation levels. For example, if the first interval > the second interval, the first interval corresponds to the first level (i.e., if the comprehensive value P corresponding to the current mental state assessment result is in the first interval, then the first level regulation scheme is matched), and the second interval corresponds to the second level.

[0096] Adjustment Scheme: The stress relief mode may include three stages. In this embodiment of the invention, the first stage of the stress relief mode corresponds to the second level (i.e., the second-level adjustment scheme is used to adjust the mental state of the user in the first stage), the second stage of the stress relief mode corresponds to the first level, and the third stage of the stress relief mode is the over-relaxation state intervention stage, which requires the use of an over-relaxation intervention mechanism for state adjustment. Specifically, the user's current stage can be determined by the range of values ​​in which the aforementioned comprehensive value P falls.

[0097] The first stage is the intervention phase for tension, where the goal is to quickly alleviate the user's tension and / or high-pressure state. This stage employs a second-level intervention protocol, implemented through visual, audio, and vibration methods. Further, guided methods may be included, primarily using voice instructions to guide the user in meditation, head movements, or regulating breathing rhythm. For example, visual methods may employ a "pulsating green light dynamic pattern" (green light wavelength 520-550nm, which reduces anxiety without inhibiting alertness), with a pulse frequency of 10 times / minute, on for 1 second and off for 5 seconds. Audio methods may include natural sounds (e.g., wind) + alpha wave-induced audio (alpha wave frequency band 10-12Hz). Guided methods may include voice prompts such as "Inhale for 4 seconds, hold for 2 seconds, exhale for 6 seconds," or guiding the user to perform head movements to relieve neck and shoulder muscle tension and neck pain. The vibration methods include: applying medium-frequency, low-intensity vibrations to the temples (approximately 150Hz, intensity <0.5g, higher than the sleep aid mode, maintaining sensory stimulation and alertness), with vibration intervals consistent with the breathing rhythm.

[0098] The second stage is the target state maintenance period, where the adjustment goal is to maintain the user's current relaxed state. The second stage employs the first-level adjustment scheme, which includes visual, audio, and vibration methods. The first level reduces interference compared to the second level, primarily in the following ways: 1. The guided and vibration methods are disabled; 2. The visual method is changed from a "pulsating green light dynamic pattern" to a "slow-flowing waveform pattern" (e.g., 0.5Hz frequency, simulating alpha wave morphology), reducing brightness to avoid visual fatigue; 3. The audio volume is reduced, natural sounds are replaced with white noise, and alpha wave-induced audio is removed to guide the user in self-relaxation.

[0099] The third stage is the intervention stage for excessive relaxation. The goal of this stage is to prevent the user from transitioning from a relaxed state to a sleep state. This stage requires an excessive relaxation intervention mechanism. Specifically, the excessive relaxation intervention mechanism includes: after the second-level adjustment plan has been executed for a preset duration, the user's physiological signals are collected by a data acquisition unit. Based on the collected physiological signals, the user's mental state is assessed. If it is determined that the user is currently in an excessive relaxation state (e.g., the comprehensive value P is within a preset range, or the EEG alpha / beta ratio is >1.2 and the theta wave percentage is >15%), the following adjustment plan is implemented: the visual mode is changed from "pulsating green light dynamic pattern" to "gradual green light dynamic pattern + blue-green light alternating embellishment" to enhance alertness and avoid excessive relaxation. The guidance mode is turned off, the audio volume is reduced to maintain a natural sound, the alpha wave induction audio is reduced from the 10-12Hz frequency band to the 8-10Hz frequency band, and light music fragments (e.g., single piano notes) are superimposed to maintain cognitive arousal. The vibration frequency was reduced to 100Hz, the force to 0.3g, and the rhythm was changed to vibrate for 1 second each during inhalation and exhalation in order to balance the rhythm and reduce dependence.

[0100] The specific details of the incentive model are as follows: Regulation objective: To improve the user's focus and / or level of excitement.

[0101] Physiological indicators include indicators for quiet and noisy scenarios. Quiet scenario indicators include electroencephalogram (EEG) signals, eye movement signals, and skin conductance signals. Noisy scenario indicators include heart rate signals, skin conductance signals, and eye movement signals. Because EEG signals are easily interfered with by noisy environments, different physiological indicators are used for different usage scenarios. The weighting of quiet scenario indicators is: EEG signals > eye movement signals > skin conductance signals. For example, EEG signals (e.g., EEG) account for 50%, eye movement signals (e.g., pupil diameter changes) account for 30%, and skin conductance signals (e.g., EDA) account for 20%. The weighting of noisy scenario indicators is: heart rate signals > skin conductance signals > eye movement signals. For example, heart rate signals (e.g., HRV) account for 50%, skin conductance signals (e.g., EDA) account for 30%, and eye movement signals (e.g., pupil diameter changes) account for 20%.

[0102] Regulation Levels: The regulation scheme in the incentive mode includes a first level and a second level. The level of regulation is directly proportional to the degree of intervention of the scheme elements. For example, assuming the user is currently in a quiet environment, this embodiment of the invention can normalize the EEG signal, heart rate signal, and skin conductance signal respectively. Assuming the EEG signal is quantized as a first value 'a', the eye movement signal as a second value 'b', and the skin conductance signal as a third value 'c', the comprehensive value E of the physiological indicators in the incentive mode is E = 50%a + 30%b + 20%c. The larger the comprehensive value E, the closer it is to the regulation target. Before assessing the user's current mental state, multiple numerical intervals can be set to match different regulation levels. For example, the first interval > the second interval, the first interval corresponds to the first level (i.e., if the comprehensive value E corresponding to the current mental state assessment result is in the first interval, then the first level regulation scheme is matched), and the second interval corresponds to the second level.

[0103] Regulation Scheme: The incentive mode may include three stages. In this embodiment of the invention, the first stage of the incentive mode corresponds to the second level (i.e., the second-level regulation scheme is used to regulate the mental state of the user in the first stage), the second stage of the incentive mode corresponds to the first level, and the third stage of the incentive mode is the over-excitement state intervention stage, which requires the use of an overstimulation intervention mechanism for state regulation. Specifically, the user's current stage can be determined by the range of values ​​within which the aforementioned comprehensive value E falls.

[0104] The first stage is the low-excitation state intervention period, and the regulatory goal of this stage is to quickly activate the user's excited emotions. The first stage employs a second-level regulatory scheme, which includes visual, audio, and electrical stimulation methods, and may further include guided methods. For example, the visual method includes using a "high-frequency blue light pulse pattern" (blue light wavelength 460~480nm, which can inhibit melatonin and enhance alertness), a pulse frequency of 12~15Hz (resonating with the low-frequency band of β waves), a pulse duty cycle of 50% (equal alternation of bright and dark), and a blue light proportion >90% to enhance the arousal effect. The audio method includes using fast-paced audio (such as drumbeats or electronic music) at approximately 150 beats / minute + high-frequency sound waves (frequency band can be 18~25Hz). The guided method includes a voice prompt "rapidly inhale for 2 seconds, exhale for 3 seconds." The electrical stimulation method includes releasing a high-frequency electrical pulse of approximately 300Hz to enhance neural excitability.

[0105] The second stage is the target state maintenance period, where the adjustment goal is to maintain the user's current state of excitement. The second stage employs the first-level adjustment scheme, which includes visual, audio, and electrical stimulation methods. The first level reduces interference compared to the second level, primarily in the following ways: 1. The guidance method is disabled; 2. The visual method adds dynamic patterns (such as rapidly passing meteors to guide attention) to the existing "high-frequency blue light patterns," and reduces brightness to avoid visual fatigue; 3. The audio volume and tempo are reduced (e.g., from 150 beats / minute to 130 beats / minute), and the high-frequency sound wave band is increased from 18-25Hz to 25-30Hz to approach the gamma wave band; 4. The electrical stimulation method reduces pulse frequency and pulse current.

[0106] The third stage is the intervention phase for over-excitement. The goal of this stage is to prevent anxiety caused by over-excitement. This stage requires an overstimulation intervention mechanism for state regulation. Specifically, the over-relaxation intervention mechanism includes: after the second-level regulation program has been implemented for a preset duration, the user's physiological signals are collected by a data acquisition unit. Based on the collected physiological signals, the user's mental state is assessed. If it is determined that the user is currently in a state of over-excitement (e.g., the comprehensive value S is within a preset range, or the skin conductance level is >80% and lasts for a preset duration), the following regulation program is implemented: the electrical stimulation mode is turned off; the visual mode is changed from a "high-frequency blue light pulse pattern" to a "blue-green pulse pattern," and the pulse frequency is reduced to 8-10 Hz (close to alpha waves, slight relaxation); the audio mode removes high-frequency sound waves, reduces the audio volume, and reduces the tempo from 150 beats / minute to 110 beats / minute. Furthermore, low-frequency white noise can also be added.

[0107] The state adjustment system based on a smart wearable device in this invention can monitor the user's physiological signals in multiple dimensions. By comprehensively judging the multi-dimensional physiological signals, the user's mental state can be assessed more accurately. In the above embodiments, the user's mental state can be identified by weighted summation of multiple physiological signals and setting a threshold. However, the feedback of each dimension of physiological signals to the user's mental state is independent, and there is no progressive relationship between the multiple dimensions of physiological signals and the mental state. Furthermore, different users react differently to different dimensions of physiological signals in the same mental state. This makes it difficult to accurately quantify the degree of a mental state using multiple dimensions of physiological signals. For example, if we need to identify the level of stress in users, and simultaneously monitor two dimensions—EEG signals (70% weight) and eye movement signals (30% weight)—we can roughly confirm that users A and B are under high stress if their alpha / beta ratio is less than 0.8. However, we can also assume that user B's stress level is not significantly reflected in their eye movement signals, even if the stress level increases, their eye movement signals remain unchanged; while user A's eye movement signals are significantly reflected. In other words, under roughly the same stress level, user A's weighted summation result is greater than user B's.

[0108] In view of the above, this embodiment of the invention also proposes a state assessment model. This model uses a convolutional neural network model (such as LSTM or Transformer model) as its basic architecture, and uses multi-dimensional physiological parameters corresponding to different mental states of the user as training samples. The output of the model can be the user's mental state recognition result (which can be the user's classification result or quantitative value in a certain mental state). The model can learn the intrinsic relationship between multi-dimensional physiological parameters and related mental states through training, thereby accurately identifying the user's mental state.

[0109] For example, for any regulation mode (e.g., stress relief mode), for the same user, multiple physiological parameters are monitored when the user is under different levels of stress (e.g., relaxed state, moderate stress, and high stress state). The "multiple physiological parameters + stress state level" are then used as training samples to be input into the model for training, so as to obtain a private model that can assess the user's stress state.

[0110] Furthermore, since the adjustment scheme has various execution methods, and each execution method has multiple scheme elements, how to select these scheme elements to form an effective adjustment scheme requires reference to the adjustment effect of each scheme element on different users. The state feedback data in this embodiment of the invention provides an important reference for adjusting the adjustment scheme. Specifically, the state feedback data includes the adjustment effects corresponding to different scheme elements or combinations of scheme elements in the adjustment scheme, and these effects are represented by changes in the user's physiological signals over a period of time. In other words, this embodiment of the invention can quantify the adjustment effects of scheme elements or combinations of scheme elements through state feedback data.

[0111] For example, in one implementation, please refer to Figure 3 and Figure 4 By weighted summing of multiple physiological signals, a comprehensive value corresponding to the user's mental state at a given time (the time of collection of multi-dimensional physiological signals) can be obtained. The difference between the comprehensive values ​​at two time points (e.g., t1 and t2) can reflect the regulatory effect of the program elements or combinations of program elements during this period. Furthermore, since the degree of change in mental state may differ at different stages during the regulation process, the regulatory effects of different program elements at a unified stage can be compared using historical data (e.g., ...). Figure 3 and Figure 4 The corresponding status feedback data can then be used to more accurately quantify the adjustment effect of scheme elements or combinations of scheme elements.

[0112] For example, in another implementation, state feedback data can be used as training samples. The state feedback data is input into the state evaluation model for training, and subsequently, during the execution of the adjustment scheme, at every stage (e.g. Figure 3 During the t1 to t2 phase, state feedback data is collected once and input into the state assessment model for identification. The output is a quantitative value of the user in a certain mental state. This quantitative value can also be used as the adjustment effect of a certain scheme element or a combination of scheme elements.

[0113] Furthermore, in one implementation, after collecting sufficient data on the adjustment effects of various scheme elements or combinations of scheme elements for the same user, this data can be input into a model (e.g., a Transformer model) for training to obtain a scheme generation model, which can serve as a private model for a particular user. In another implementation, historical data collected during the adjustment process for a certain type of user (e.g., a user requiring stress relief) can also be input into a model for training to obtain a general model for that type of user.

[0114] In this embodiment of the invention, there are various types of physiological signals related to mental state, and the physiological logic reflected by these signals is not entirely the same. It is difficult to accurately identify the degree of a user's mental state using a weighted summation method. Therefore, this embodiment of the invention obtains a state assessment model capable of identifying a user's mental state through model training. The state assessment model can learn the intrinsic correlation between multiple dimensions of physiological signals and their corresponding mental states, as well as the intrinsic correlation between these multiple dimensions of physiological signals, thereby accurately identifying the user's mental state. Furthermore, in practical applications, there are numerous elements forming a regulation plan, and each element has different effects on different users. It is difficult to form an effective conditional plan based solely on subjective design. This embodiment of the invention uses state feedback data as training samples to train a plan generation model. This model can accurately understand the regulatory effects of various plan elements or combinations of plan elements on the user, thereby generating a regulation plan suitable for the corresponding user.

[0115] Please see Figure 2 In this embodiment of the invention, the execution unit includes at least one of the following: A visual display module is configured to provide users with visual hypnotic signals of specific patterns; for example, a lens display module in smart glasses can enable VR functionality.

[0116] An audio output module is configured to provide audio hypnotic signals to the user; for example, an audio speaker module or bone conduction speaker is installed in the temple of smart glasses.

[0117] A vibration module configured to provide a vibratory massage to the user's temples; for example, a vibration motor installed in the temples of smart glasses.

[0118] Furthermore, in this embodiment of the invention, the data acquisition unit includes at least one of the following: An eye detection module is configured to detect changes in a user's eye movements, including at least one of blink frequency, eye movement frequency, and pupil diameter. For example, the eye detection module can be an infrared optical eye-tracking device (including an infrared light emitting module and an infrared light receiving module) mounted on the frame of glasses.

[0119] The brainwave interface module is configured to collect the user's brainwave signals, which include at least one of alpha, beta, theta, and gamma wave signals. For example, the brainwave interface module may include flexible electrodes that can fit against the forehead region of the head, or dry electrodes disposed on the inner side of the temple (i.e., the side facing the scalp when wearing the smart glasses). Specifically, the dry electrodes may have four electrodes symmetrically distributed on both sides of the temple, with two dry electrodes on one temple positioned near the temple and at the end of the temple, respectively, when worn.

[0120] Furthermore, considering that users have different head sizes, in order to adapt to the temple positions of different users when wearing the smart glasses, a moving device is provided on the temples. One of the dry electrodes and the vibration module on one temple are set on the moving device. After wearing the smart glasses, the user can adjust the position of the moving device so that the dry electrode and the vibration module can be adjusted to be aligned with the user's temple.

[0121] A heart rate detection module is configured to collect the user's heart rate signal. For example, a PPG sensor installed on a smartwatch or smart band can detect the user's heart rate variability.

[0122] A conductivity detection module is configured to collect the user's skin conductivity signals. For example, an EDA sensor installed in a smartwatch or smart bracelet can detect the user's skin conductivity response and skin conductivity level.

[0123] The posture detection module is configured to detect the user's head posture using an accelerometer and a gyroscope. Under the guidance mode, it monitors the user's head posture in real time to detect whether the user is making the correct head movements according to the voice instructions.

[0124] Furthermore, in this embodiment of the invention, the execution unit may further include a current output module, configured to apply current stimulation to the user's contact area to perform the required function of the aforementioned current stimulation method. Specifically, the current output module may be a dry electrode disposed on the temple of the mirror; that is, the dry electrode of the EEG interface module disposed on the temple near the temple can serve as the dry electrode of the current output module. Specifically, the dry electrode may integrate an input mode and an output mode. The input mode is used to acquire brainwave signals; the output mode is used to release a current with adjustable intensity and frequency to stimulate the brain into an active state.

[0125] Furthermore, the state adjustment system in this embodiment of the invention also includes an audio input module (not shown in the figure) and a camera module (not shown in the figure). When an emergency occurs (e.g., a fire), the state adjustment system analyzes the sound and video information collected by the audio input module and the camera module. If it is determined to be an emergency recorded in the system, it coordinates with the audio output module and the vibration module to issue a voice alarm and a vibration alarm, causing the user to exit the adjustment scheme and prompting relevant safety measures. Furthermore, the information collected by the audio input module and / or the camera module can also be used to form a trigger mechanism for a certain adjustment mode. For example, in the scenario of fatigued driving, when the physiological signals collected by the data acquisition unit identify that the user is currently in a fatigued state, and the camera module identifies that the user is currently in the driver's seat, an incentive mode can be triggered. It should be noted that there can be multiple preset incentive modes in this embodiment of the invention. Among them, the incentive mode for fatigued driving does not include the above-mentioned visual method to avoid visual images affecting the user's driving vision. Furthermore, in the incentive mode, the user can be reminded by voice that they are in driving mode. If no response to the voice reminder is detected within a preset time, the user will be stimulated with a larger current intensity through electrical stimulation.

[0126] In one embodiment of the state adjustment system based on a smart wearable device of the present invention, please refer to... Figure 5 The state adjustment system may include smart glasses. The aforementioned visual display module, audio output module, vibration module, eye detection module, and brainwave interface module are integrated into the smart glasses.

[0127] Specifically, the smart glasses provided in this embodiment of the invention are used to execute an adjustment scheme for regulating the user's mental state. The adjustment scheme includes at least two execution modes: visual mode, audio mode, and vibration mode; wherein each execution mode has at least one adjustable scheme element. The smart glasses specifically include: The visual display module is configured to provide users with visual hypnotic signals of specific patterns to achieve the visual mode in the adjustment scheme.

[0128] The audio output module is configured to provide users with audio hypnotic signals to achieve the audio mode in the adjustment scheme.

[0129] The vibration module is configured to provide vibration massage to the user's temples, thus achieving the vibration mode in the adjustment scheme.

[0130] An eye detection module is configured to detect changes in the user's eyeballs, including at least one of blink frequency, eye movement frequency, and pupil diameter.

[0131] An EEG interface module is configured to acquire the user's brainwave signals; the brainwave signals include at least one of alpha wave signals, beta wave signals, theta wave signals, and gamma wave signals.

[0132] The main control unit is configured to call the eye detection module and the EEG interface module before and during the execution of the adjustment plan to collect the user's physiological signals and obtain state feedback data. The state feedback data is the change of the user's physiological signals over a period of time during the execution of the adjustment plan. The physiological signals include: eye movement signals and EEG signals. The plan elements in the adjustment plan are adjusted based on the state feedback data.

[0133] like Figure 5 As shown, the EEG interface module includes four dry electrodes. These four dry electrodes are symmetrically distributed on both sides of the temples of the smart glasses. Two dry electrodes on each temple are positioned near the temple when worn, and the other is positioned at the end of the temple. Exemplarily, the dry electrodes can be attached to the scalp using flexible electronic materials (requiring no conductive gel, making them comfortable to wear and suitable for long-term use) to collect EEG signals from the prefrontal cortex. Furthermore, the dry electrodes have an input mode and an output mode. The input mode is used to collect EEG signals; the output mode is used to release a stimulation current with adjustable intensity and frequency to achieve the function of the aforementioned current stimulation method.

[0134] Furthermore, considering that users have different head sizes, the smart glasses are equipped with a moving device on the temples to adapt to different users' temple positions when worn. One of the dry electrodes and the vibration module on one temple are mounted on the moving device, allowing the dry electrode and vibration module to move along the temple with the moving device. After wearing the smart glasses, users can adjust the position of the moving device to align the dry electrode and vibration module with their temples.

[0135] Furthermore, the aforementioned smart glasses can also perform actions through a guided mode. This guided mode is used to guide users to meditate, regulate their breathing rhythm, or perform head movements via voice instructions. Specifically, the audio output module is also configured to implement the guided mode function.

[0136] Furthermore, the aforementioned smart glasses also include a posture detection module, configured to use an accelerometer and a gyroscope to detect the user's head posture. In guided mode, it monitors the user's head posture in real time to detect whether the user is making the correct head movements according to the voice instructions.

[0137] In this embodiment of the invention, all the functions of the main control unit and the execution unit, as well as some of the functions of the data acquisition unit, can be achieved through a single pair of smart glasses. In other words, the smart glasses in this embodiment can realize the main functions of the aforementioned state adjustment system. Users, simply by wearing these smart glasses, can conveniently adjust their mental state using the aforementioned sleep-aid mode, stress-relieving mode, or motivational mode in various life scenarios, including study, lunch break, work, and sleep.

[0138] Furthermore, to more accurately assess the user's mental state, the state adjustment system in this embodiment may also include a smartwatch or smart bracelet, with the aforementioned heart rate detection module and conductivity detection module integrated into the smartwatch or smart bracelet. Since the smartwatch or smart bracelet is also a device that users can conveniently wear daily, it can connect to the smart glasses via wireless communication functions such as Bluetooth or Wi-Fi to transmit the physiological signals collected by the heart rate detection module and conductivity detection module to the smart glasses for processing.

[0139] In another embodiment of the state adjustment system based on a smart wearable device of the present invention, please refer to [link / reference]. Figure 6 The state adjustment system may include: smart glasses 801, a smart bracelet 802, and an electroencephalogram (EEG) detection device 803. The aforementioned visual display module, audio output module, vibration module, and eye detection module are integrated into the smart glasses. The aforementioned heart rate detection module and conductance detection module are integrated into the smartwatch or smart bracelet. The EEG detection device implements the functions of the aforementioned EEG interface module.

[0140] The state regulation system in this embodiment of the invention can be applied to professional psychotherapy settings. Since these settings offer dedicated treatment environments, more specialized hardware testing equipment (e.g., independent EEG detection devices) can be installed to obtain more diverse (e.g., more abundant protocol elements for testing to select those with higher patient suitability) and refined data, providing more accurate data support for the stress therapy process. Furthermore, for different patients, the state regulation system in this embodiment can establish a dedicated database, a private state assessment model, and a protocol generation model for each patient, enabling targeted treatment. The private protocol generation model can generate specific regulation plans for particular patients, achieving better regulatory effects.

[0141] This invention also provides a state adjustment method based on a smart wearable device. Please refer to [link / reference]. Figure 7 , Figure 7 This diagram illustrates a flow chart of a state adjustment method based on a smart wearable device according to an embodiment of the present invention. The state adjustment method according to the embodiment of the present invention includes: S1. Implement the adjustment plan for the user; This adjustment scheme is used to regulate the user's mental state through the execution unit in the state adjustment system, including at least two execution modes: visual mode, audio mode and vibration mode, wherein each execution mode has at least one adjustable scheme element.

[0142] S2. Collect the user's physiological signals to obtain status feedback data; Before and during the implementation of the regulation program, the data acquisition unit in the state regulation system collects the user's physiological signals to obtain state feedback data. These physiological signals may include at least one of the following: eye movement signals, electroencephalogram (EEG) signals, heart rate signals, and skin conductance signals. This state feedback data reflects the changes in the user's physiological signals over a period of time during the implementation of the regulation program. These changes may include the direction of change (e.g., deterioration or improvement of indicators) and the degree of change (e.g., the magnitude of improvement in indicators). Specifically, the state feedback data may reflect the overall physiological signal changes corresponding to the regulation program, or it may reflect the physiological signal changes corresponding to different program elements or combinations of different program elements within the regulation program.

[0143] Furthermore, after acquiring physiological signals, preprocessing operations can be performed on the acquired signals. These preprocessing operations include signal amplification, signal filtering, and algorithmic averaging. Specifically, the algorithmic averaging can be a sliding window incremental averaging algorithm. Window division: An overlapping window sequence is generated with a time window of 60 seconds (N=60) and a step size of 1 second. For example, the first group is 1-60 seconds, the second group is 2-61 seconds, the third group is 3-62 seconds, and so on. The arithmetic mean Avg is calculated for the raw data (e.g., eye movement frequency) within each window. The calculation expression is as follows:

[0144] in, Let be the signal value at time t. Output (M-N+1) average window sequences (M is the total duration, i+N-1≤M), for example, 301 sets of averages are generated from 360 seconds of data.

[0145] It should be noted that all physiological signals collected can undergo the above preprocessing operations, whether before or during the implementation of the regulation program. These will not be elaborated upon in subsequent steps.

[0146] S3. Adjust the scheme elements in the adjustment scheme based on the state feedback data.

[0147] The state adjustment system adjusts the scheme elements in the adjustment plan based on state feedback data. In this invention, the adjustment of scheme elements can include two methods: one is to adjust at different stages of the same execution plan, and the other is to adjust the scheme elements in the next adjustment plan for the same user.

[0148] In this embodiment of the invention, the adjustment scheme includes at least two execution modes (e.g., visual mode, audio mode, and vibration mode). Regardless of the execution mode, it has at least one adjustable scheme element. During the execution of the adjustment scheme on the user, the data acquisition unit can collect the user's physiological signals in real time to obtain state feedback data that reflects the changes in the user's physiological signals over a period of time. This allows the main control unit to determine the adjustment effect of the current adjustment scheme on the user based on the state feedback data, and to adjust the scheme elements in the adjustment scheme based on the adjustment effect to optimize the current adjustment scheme.

[0149] For adjustment schemes with the same level of intervention, different users have different sensitivities, making it difficult to match the appropriate level of intervention to the current user during the initial execution phase. Therefore, this invention proposes a method for feedback adjustment during the execution of the adjustment scheme. Please refer to [link to relevant documentation]. Figure 8 , Figure 8 This illustrates another flowchart of a state adjustment method based on a smart wearable device according to an embodiment of the present invention. The state adjustment method according to an embodiment of the present invention includes: H1. Obtain the adjustment mode selected by the user; The state regulation system in this embodiment of the invention can collect physiological signals from multiple dimensions and has multiple execution modes, enabling various regulation modes such as sleep aid mode, stress relief mode, and incentive mode. The sleep aid mode is used to induce sleep in users who need it, helping them enter a sleep state; its application scenarios include: afternoon naps, difficulty falling asleep, and frequent awakenings at night. The stress relief mode is used to relieve stress for users with high levels of mental stress; its application scenarios include: breaks between work / study sessions, feeling stressed before speeches / exams, and recovery after exercise. The incentive mode is used to provide mental stimulation for users who need to improve focus and / or alertness; its application scenarios include: before exercise, and boosting spirits before meetings / driving. Different regulation modes correspond to different physiological indicators. Therefore, users need to select the desired regulation mode before implementing the regulation plan. The specific details of each regulation mode can be found in the above embodiments and will not be repeated here.

[0150] H2. Collect users' physiological signals based on the modulation mode; After determining the adjustment mode, the state adjustment system invokes the corresponding data acquisition unit to collect the user's physiological signals. For example, if the user selects the sleep aid mode, the eye detection module needs to be invoked to monitor the user's eye movements and pupil changes in real time. Changes in the user's eye state can reflect the user's level of concentration and stress. For example, when the user is under stress, the pupils may dilate, and eye movements may become more frequent and irregular. Furthermore, the system can also invoke the EEG interface module, heart rate detection module, and conductance detection module to collect the user's physiological signals, using multi-dimensional physiological signals to comprehensively assess the user's mental state and improve the accuracy of the assessment.

[0151] H3. Assess the user's mental state based on the collected physiological signals; In one implementation, the collected physiological signals can be input into a state assessment model to obtain the mental state assessment results output by the state assessment model.

[0152] The training samples for the state assessment model include: state monitoring data and / or state feedback data; the state monitoring data includes multiple dimensions of physiological parameters monitored when the user is in different mental states, and the state feedback data includes the regulatory effects corresponding to different elements or combinations of elements in the regulation plan. The regulatory effects are represented by changes in the user's physiological signals over a period of time. The state assessment model can learn the correlation between the user's different mental states and physiological signals through the training samples.

[0153] H4. Determine the adjustment level of the adjustment mode based on the assessment results; For any adjustment mode, the state adjustment system has multiple preset adjustment schemes. Different adjustment levels correspond to different levels of intervention (the adjustment level includes at least two levels, and the level of adjustment is proportional to the degree of intervention of the adjustment scheme). If the user's current mental state is closer to the adjustment target, a lower level adjustment scheme is used; if the user's current mental state is further away from the adjustment target, a higher level adjustment scheme is used.

[0154] H5, implement adjustment plans for users; The specific content of step H5 is similar to that of step S1 above, and will not be repeated here.

[0155] H6. Collect the user's physiological signals to obtain status feedback data; The state feedback data in this embodiment of the invention reflects the changes in physiological signals corresponding to the overall adjustment scheme.

[0156] H7. Adjust the adjustment level of the adjustment scheme based on status feedback data; Specifically, the user's adjustment feedback state can be identified based on the state feedback data. The adjustment feedback state includes: state improvement, state maintenance, and state deterioration. If the adjustment feedback state is state improvement, then step H8 is executed; if the adjustment feedback state is state deterioration, then step H9 is executed; if the adjustment feedback state is state maintenance, then step H10 is executed.

[0157] H8. Lower the adjustment level or maintain the lowest level; If the adjustment feedback status is "state improvement" and the current adjustment level is not the lowest level, it means that the user's current mental state has been improved after adjustment, and the intervention level of the adjustment plan can be reduced. Therefore, the adjustment level of the adjustment plan can be reduced.

[0158] If the adjustment feedback status is "state improvement" and the current adjustment level is the lowest level, it means that the user's current mental state has been improved after adjustment, and the user is currently implementing the adjustment plan with the lowest level of intervention. The user's adjustment goal has been achieved, and the adjustment plan can be stopped.

[0159] H9. Increase the adjustment level or maintain the current adjustment level; If the adjustment feedback status indicates a worsening situation, and the current adjustment level is not the highest level, it means that the current level of intervention is not effective for the user, and the adjustment level of the adjustment plan needs to be increased to enhance the level of intervention.

[0160] If the adjustment feedback status is deteriorating and the current adjustment level is the highest level, it means that the current level of intervention has no effect on the user. However, the system has no higher level of intervention at present. The current adjustment plan can be kept unchanged, and the adjustment effect can be observed through long-term adjustment. If the current status is maintained or continues to deteriorate for a preset time, the adjustment plan should be stopped.

[0161] H10. Maintain the current adjustment level or increase the adjustment level.

[0162] If the adjustment feedback status is "status maintained" and the current adjustment level is not the highest level, it means that the current level of intervention has no significant effect on the user. The current adjustment plan can be kept unchanged or the adjustment level of the plan can be increased.

[0163] If the adjustment feedback status is "status maintained" and the current adjustment level is the highest level, it means that the current level of intervention has no significant effect on the user. However, the system currently has no higher level of intervention, so the current adjustment plan can be kept unchanged, and the adjustment effect can be observed through long-term adjustment.

[0164] Because different users have varying sensitivities to the same adjustment scheme, the initial intervention level of the scheme may be insufficient to meet users' adjustment needs. Therefore, this embodiment of the invention sets multiple adjustment levels for each adjustment mode, with different levels corresponding to different intervention levels. If the initial adjustment level is effective, the intervention level is gradually reduced; if the initial adjustment level is ineffective, the intervention level is increased. This embodiment of the invention can dynamically adjust the adjustment scheme based on the actual adjustment situation, adapting to different users' sensitivities to different schemes, thereby optimizing the adjustment effect of the scheme.

[0165] Since different users have varying sensitivities to different types of scheme elements, this invention proposes a method for adjusting subsequent schemes based on historical data of prior adjustment effects. This method can configure adjustment schemes tailored to individual users. Please refer to [link to relevant documentation]. Figure 9 , Figure 9 This illustrates another flowchart of a state adjustment method based on a smart wearable device according to an embodiment of the present invention. The state adjustment method according to an embodiment of the present invention includes: L1. Collect the user's physiological signals; The state adjustment system in this embodiment of the invention can have multiple adjustment modes, or only one adjustment mode. When only one adjustment mode is available, the user does not need to select an adjustment mode; after wearing the smart glasses, the system directly enters the physiological signal acquisition process.

[0166] L2. Determine the user's adjustment plan based on the currently collected physiological signals; In one implementation, the collected physiological signals can be input into a protocol generation model to generate a regulation protocol that matches the user's current state.

[0167] In practical applications, there are many elements that form a regulation plan, and the impact of each element on different users is also different. Therefore, by using state feedback data as training samples through model training, the model can learn the relationship between different elements, the relationship between the regulation effect of different elements on different users, and the relationship between different elements and users in different mental states. In this way, a regulation plan that is suitable for the user can be generated.

[0168] L3. Implement adjustment plans for users; The specific content of step L3 is similar to that of step S1 above, and will not be repeated here.

[0169] L4. Collect the user's physiological signals to obtain status feedback data; Specifically, the status feedback data includes the adjustment effects corresponding to different scheme elements or combinations of scheme elements in the adjustment scheme, and the adjustment effects are represented by changes in the user's physiological signals over a period of time.

[0170] In one implementation, a private scheme generation model can be established for individual users. This scheme generation model can be trained to generate adjustment schemes using the state feedback data after each execution of the adjustment scheme as training samples.

[0171] L5. Update the scheme elements in the adjustment scheme to which the user belongs based on the status feedback data.

[0172] In one implementation, the user's identity identifier is obtained, and the following steps are performed on the adjustment scheme corresponding to the identity identifier: based on the status feedback data of the completed adjustment scheme, the adjustment effect corresponding to the scheme element or combination of scheme elements in the adjustment scheme is recorded; the weights corresponding to different scheme elements in the adjustment scheme are updated based on the adjustment effect.

[0173] In another implementation, a private solution generation model can be suggested to the user. All state feedback data from the user's execution of all adjustment plans can be used as training samples for this solution generation model. The state adjustment system can maintain and update the historical data generated by the user's executed adjustment plans in real time, and train the user's private model periodically based on this historical data, so that the adjustment plans generated by the private model become increasingly adapted to the user. This approach can be applied to some professional treatment scenarios for mental illnesses. For example, for patients with excessive mental stress, doctors can establish a dedicated database, a private state assessment model, and a solution generation model for the patient to cope with long-term treatment.

[0174] This invention also provides an electronic device, comprising a memory and a processor. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The memory stores executable code for executing the state adjustment method based on a smart wearable device described in the above method embodiments. When the executable code is processed by the processor, the processor can execute part or all of the methods described above.

[0175] Furthermore, embodiments of the present invention also provide a computer-readable storage medium, such as a chip or optical disc, storing the above-described state adjustment method based on a smart wearable device. The computer-readable storage medium stores an executable program, which, when executed, implements the state adjustment method as described in any of the above embodiments.

[0176] It should be noted that the computer-readable storage medium described in the embodiments of this disclosure is not limited to the embodiments given above. For example, it can also be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the embodiments of this disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0177] It will be understood by those skilled in the art that the above-described preferred solutions can be freely combined and superimposed without conflict. The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings; for example, two consecutively indicated blocks may actually be executed substantially in parallel, or sometimes in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions. The numbering of each step in this document is for ease of explanation and reference only and is not intended to limit the order of execution. The specific execution order is determined by the technology itself, and those skilled in the art can determine various permissible and reasonable orders based on the technology itself.

[0178] It should be noted that the use of step numbers (letters or numbers) to refer to certain specific method steps in this invention is merely for the purpose of convenience and brevity in description, and is by no means intended to restrict the order of these method steps. Those skilled in the art will understand that the order of the relevant method steps should be determined by the technology itself and should not be unduly restricted by the existence of step numbers. Those skilled in the art can determine various permissible and reasonable orderings of steps based on the technology itself.

[0179] Those skilled in the art will understand that, without conflict, the above-mentioned preferred solutions can be freely combined and superimposed.

[0180] It should be understood that the above embodiments are merely exemplary and not restrictive. Various obvious or equivalent modifications or substitutions that can be made by those skilled in the art regarding the above details without departing from the basic principles of the present invention will be included within the scope of the claims of the present invention.

Claims

1. A state adjustment system based on a smart wearable device, characterized in that, include: An execution unit is configured to execute an adjustment scheme on a user, the adjustment scheme including at least two of the following execution modes: visual mode, audio mode, and vibration mode; wherein each of the execution modes has at least one adjustable scheme element; The data acquisition unit is configured to collect the user's physiological signals before and during the execution of the adjustment scheme to obtain state feedback data; the state feedback data is the change of the user's physiological signals over a period of time during the execution of the adjustment scheme; the physiological signals include at least one of the following: eye movement signals, electroencephalogram (EEG) signals, heart rate signals, and skin conductance signals; The main control unit is configured to adjust the scheme elements in the adjustment scheme based on the status feedback data.

2. The state regulation system according to claim 1, characterized in that, The main control unit is also configured to: The adjustment mode to be executed is confirmed, which includes: sleep aid mode, stress relief mode, and incentive mode; wherein, the sleep aid mode is used to hypnotize users who need sleep to help them enter a sleep state; the stress relief mode is used to relieve stress for users with high mental stress; the incentive mode is used to provide mental stimulation for users who need to improve their concentration and / or excitement; wherein, different adjustment modes correspond to different physiological indicators.

3. The state regulation system according to claim 2, characterized in that, The main control unit is also configured to: Based on the adjustment mode, the corresponding data acquisition unit is invoked to collect the user's physiological signals; The user's mental state is assessed based on the physiological signals, and the adjustment level of the adjustment mode is determined based on the assessment results; wherein the adjustment level includes at least two levels, and the level of adjustment is proportional to the degree of intervention formed by the elements of the adjustment plan; Based on the adjustment mode, the corresponding execution unit is invoked to perform an adjustment scheme that matches the adjustment level on the user.

4. The state regulation system according to claim 3, characterized in that, The main control unit is also configured to: After acquiring the status feedback data, the user's adjustment feedback status is identified based on the status feedback data. The adjustment feedback status includes: status improvement, status maintenance, and status deterioration. If the adjustment feedback state is an improvement and the current adjustment level is not the lowest level, then the adjustment level of the adjustment scheme is reduced; if the adjustment feedback state is an improvement and the current adjustment level is the lowest level, then the adjustment scheme is stopped. If the adjustment feedback state is "state maintained" and the current adjustment level is not the highest level, then the currently executing adjustment scheme remains unchanged or the adjustment level of the adjustment scheme is increased; if the adjustment feedback state is "state maintained" and the current adjustment level is the highest level, then the currently executing adjustment scheme remains unchanged. If the adjustment feedback state indicates a deterioration and the current adjustment level is not the highest level, then the adjustment level of the adjustment scheme is increased; if the adjustment feedback state indicates a deterioration and the current adjustment level is the highest level, then the currently executing adjustment scheme remains unchanged.

5. The state regulation system according to claim 1, characterized in that, The main control unit is also configured to: Obtain the user's identity identifier, and perform the following steps for the adjustment scheme corresponding to the identity identifier: Based on the status feedback data of the completed adjustment scheme, record the adjustment effect corresponding to the scheme element or combination of scheme elements in the adjustment scheme; The status feedback data includes: the adjustment effect corresponding to different scheme elements or combinations of scheme elements in the adjustment scheme, and the adjustment effect is represented by the changes in the user's physiological signals over a period of time; The weights corresponding to different scheme elements in the adjustment scheme are updated based on the adjustment effect.

6. The state regulation system according to claim 1, characterized in that, The main control unit includes a state assessment model, which can output the user's mental state assessment result based on the input physiological signals; wherein, the training samples of the state assessment model include state monitoring data and / or state feedback data; the state monitoring data includes physiological parameters monitored when the user is in different mental states, and the state feedback data includes the adjustment effects corresponding to different scheme elements or combinations of scheme elements in the adjustment scheme, and the adjustment effects are represented by the changes in the user's physiological signals over a period of time; the state assessment model can learn the correlation between the user's different mental states and physiological signals through the training samples.

7. The state regulation system according to claim 6, characterized in that, The main control unit includes: a scheme generation model, which can output an adjustment scheme adapted to the user's current state based on the physiological signals collected from the user; wherein, the training samples of the scheme generation model include: state feedback data; the scheme generation model can learn the adjustment effects of various scheme elements or combinations of scheme elements on the user, as well as the correlation between various scheme elements, through the training samples.

8. The state regulation system according to claim 1, characterized in that, The main control unit is also configured to perform preprocessing operations on the acquired physiological signals, including signal amplification, signal filtering, and algorithm averaging.

9. The state regulation system according to claim 1, characterized in that, The execution method also includes: guidance method and / or electrical stimulation method; The guidance method is used to guide users to meditate, regulate their breathing rhythm, or perform head movements through voice instructions. The current stimulation method is used to stimulate the user through pulsed current.

10. The state regulation system according to claim 2, characterized in that, The physiological indicators of the sleep aid mode include: electroencephalogram (EEG) signals, heart rate signals, and eye detection signals; the weighting order of the physiological signals is: EEG signals > heart rate signals > eye detection signals. The adjustment levels of the sleep aid mode include: Level 1, Level 2, and Level 3; the intervention levels corresponding to Level 1, Level 2, and Level 3 increase sequentially; the scheme elements corresponding to Level 1 only include: white noise at a first volume; the scheme elements corresponding to Level 2 include: "white noise + theta wave induced audio" at a second volume, and a warm-colored visual pattern with a first brightness and a first flashing frequency; the scheme elements corresponding to Level 3 include: "white noise + alpha wave induced audio" at a third volume, wherein a warm-colored visual pattern with a second brightness and a second flashing frequency; wherein, Level 1 < Level 2 < Level 3, Level 1 < Brightness, and First Flashing Frequency < Second Flashing Frequency.

11. The state regulation system according to claim 10, characterized in that, The sleep aid mode also includes: an abnormal state intervention mechanism; The abnormal state intervention mechanism includes: when the main control unit detects an abnormal state event, it stops executing the adjustment scheme and wakes up the user through voice prompts and / or vibration prompts; the abnormal state event includes: The user entered sleep mode for a longer than preset duration; The heart rate signal and / or the skin conductance signal exceed the safety threshold.

12. The state regulation system according to claim 2, characterized in that, The physiological indicators of the stress relief mode include: electroencephalogram (EEG) signals, heart rate signals, and skin conductance signals; the physiological indicators include quiet scene indicators and noisy scene indicators; the weighting of the quiet scene indicators is: EEG signals > heart rate signals > skin conductance signals; the weighting of the noisy scene indicators is: heart rate signals > skin conductance signals > EEG signals. The stress relief mode adjustment levels include: a first level and a second level; the intervention levels corresponding to the first level and the second level increase sequentially; the scheme elements corresponding to the second level include: "natural sound + alpha wave induced audio" at a second volume, and a green visual pattern at a second brightness; the scheme elements corresponding to the first level include: "white noise" at a first volume, and a green visual pattern at a first brightness; wherein, the first volume < the second volume, and the first brightness < the second brightness.

13. The state regulation system according to claim 12, characterized in that, The stress relief mode also includes: an over-relaxation intervention mechanism; The over-relaxation intervention mechanism includes: after the second-level adjustment scheme has been implemented for a preset duration, the user's physiological signals are collected by the data acquisition unit, and the user's mental state is assessed based on the currently collected physiological signals. If it is determined that the user is currently in an over-relaxed state, the following adjustment scheme is implemented: the guidance mode is turned off, the visual mode is changed from a green visual pattern to a blue-green alternating pattern; the audio mode reduces the audio volume, maintains a natural sound, adds light music segments, and reduces the frequency band of alpha wave induced audio.

14. The state regulation system according to claim 2, characterized in that, The physiological indicators of the stimulation mode include: quiet scene indicators and noisy scene indicators; the quiet scene indicators include: electroencephalogram (EEG) signals, eye movement signals, and skin conductance signals; the weights of the quiet scene indicators are: EEG signals > eye movement signals > skin conductance signals; the noisy scene indicators include: heart rate signals, skin conductance signals, and eye movement signals; the weights of the noisy scene indicators are: heart rate signals > skin conductance signals > eye movement signals. The adjustment levels of the excitation mode include: a first level and a second level; the intervention levels corresponding to the first level and the second level increase sequentially; the scheme elements corresponding to the second level include: a fast-paced audio with a second volume and a second beat frequency, and a blue light pulse visual pattern with a second brightness; the scheme elements corresponding to the first level include: a fast-paced audio with a first volume and a first beat frequency, and a blue light pulse visual pattern with a first brightness; wherein, the first volume < the second volume, the first beat frequency < the second beat frequency, and the first brightness < the second brightness.

15. The state regulation system according to claim 14, characterized in that, The incentive model also includes: an overstimulation intervention mechanism; The overstimulation intervention mechanism includes: after the second-level regulation scheme has been executed for a preset duration, the user's physiological signals are collected by the data acquisition unit, and the user's mental state is assessed based on the currently collected physiological signals. If it is determined that the user is currently in a state of over-excitement, the following regulation scheme is executed: the current stimulation mode is turned off, the visual mode is changed from a blue light pulse pattern to a blue-green pulse pattern, and the pulse frequency is reduced; the audio mode reduces the audio volume, replaces high-frequency sound waves with low-frequency white noise, and reduces the beat frequency of fast-paced audio.

16. The state regulation system according to claim 1, characterized in that, The execution unit includes at least one of the following: A visual display module configured to provide users with visual hypnotic signals of specific patterns; An audio output module configured to provide audio hypnotic signals to the user; Vibration module, configured to provide vibration massage to the user's temples; The data acquisition unit includes at least one of the following: An eye detection module is configured to detect changes in a user's eye movements, wherein the changes in eye movements include at least one of blink frequency, eye movement frequency, and pupil diameter. An electroencephalogram (EEG) interface module is configured to acquire a user's brainwave signals; the brainwave signals include at least one of alpha wave signals, beta wave signals, theta wave signals, and gamma wave signals. Heart rate detection module, configured to collect the user's heart rate signal; The conductivity detection module is configured to collect the user's skin conductivity signal.

17. The state regulation system according to claim 9, characterized in that, The data acquisition unit further includes: The posture detection module is configured to detect the user's head posture using an accelerometer and a gyroscope. Under the guidance mode, it monitors the user's head posture in real time to detect whether the user is making the correct head movements according to the voice instructions.

18. The state adjustment system according to claim 16, characterized in that, The visual display module, the audio output module, the vibration module, the eye detection module, and the EEG interface module are integrated into the same smart glasses.

19. The state regulation system according to claim 16, characterized in that, The heart rate detection module and the conductivity detection module are integrated into the same smart bracelet or smartwatch.

20. The state regulation system according to claim 18, characterized in that, The EEG interface module includes four dry electrodes. The four dry electrodes are symmetrically distributed on both sides of the temples of the smart glasses, with two dry electrodes on one temple located near the temple and at the end of the temple, respectively.

21. The state regulation system according to claim 20, characterized in that, The smart glasses have a moving device on the temple. One of the dry electrodes on one temple and the vibration module are mounted on the moving device, so that the dry electrode and the vibration module can move on the temple with the moving device.

22. The state regulation system according to claim 20, characterized in that, The dry electrode has an input mode and an output mode. The input mode is used to acquire the EEG signal, and the output mode is used to release a stimulation current with adjustable intensity and frequency.

23. A type of smart glasses, characterized in that, A modulation scheme for regulating a user's mental state, the modulation scheme including at least two of the following execution modes: visual mode, audio mode, and vibration mode; wherein each of the execution modes has at least one adjustable scheme element; the smart glasses include: A visual display module is configured to provide the user with visual hypnotic signals of specific patterns, thereby realizing the visual mode in the adjustment scheme; An audio output module is configured to provide audio hypnotic signals to the user, thereby implementing the audio mode in the aforementioned adjustment scheme; The vibration module is configured to provide vibration massage to the user's temples, thereby implementing the vibration mode in the adjustment scheme. An eye detection module is configured to detect changes in a user's eye movements, wherein the changes in eye movements include at least one of blink frequency, eye movement frequency, and pupil diameter. An electroencephalogram (EEG) interface module is configured to acquire a user's brainwave signals; the brainwave signals include at least one of alpha wave signals, beta wave signals, theta wave signals, and gamma wave signals. The main control unit is configured to, before and during the execution of the adjustment scheme, call the eye detection module and the EEG interface module to collect the user's physiological signals and obtain state feedback data; adjust the scheme elements in the adjustment scheme based on the state feedback data; the state feedback data is the change of the user's physiological signals over a period of time during the execution of the adjustment scheme; the physiological signals include: eye movement signals and EEG signals.

24. The smart glasses according to claim 23, characterized in that, The EEG interface module includes four dry electrodes. The four dry electrodes are symmetrically distributed on both sides of the temples of the smart glasses, with two dry electrodes on one temple located near the temple and at the end of the temple, respectively.

25. The smart glasses according to claim 24, characterized in that, The smart glasses have a moving device on the temple. One of the dry electrodes on one temple and the vibration module are mounted on the moving device, so that the dry electrode and the vibration module can move on the temple with the moving device.

26. The smart glasses according to claim 24, characterized in that, The dry electrode has an input mode and an output mode. The input mode is used to acquire the EEG signal, and the output mode is used to release a stimulation current with adjustable intensity and frequency.

27. The smart glasses according to claim 23, characterized in that, The execution method also includes a guidance method; the guidance method is used to guide the user to meditate, adjust breathing rhythm or perform head movements through voice instructions; the audio output module is also configured to implement the function of the guidance method.

28. The smart glasses according to claim 27, characterized in that, The smart glasses also include: The posture detection module is configured to detect the user's head posture using an accelerometer and a gyroscope. Under the guidance mode, it monitors the user's head posture in real time to detect whether the user is making the correct head movements according to the voice instructions.

29. A state adjustment method based on a smart wearable device, characterized in that, include: An adjustment scheme is executed on the user, the adjustment scheme including at least two of the following execution modes: visual mode, audio mode, and vibration mode; wherein, each of the execution modes has at least one adjustable scheme element; Before and during the implementation of the regulation program, the user's physiological signals are collected to obtain state feedback data; the state feedback data is the change of the user's physiological signals over a period of time during the implementation of the regulation program; the physiological signals include at least one of the following: eye movement signals, electroencephalogram (EEG) signals, heart rate signals, and skin conductance signals; The scheme elements in the adjustment scheme are adjusted based on the state feedback data.

30. The state adjustment method according to claim 29, characterized in that, Before implementing the adjustment plan on the user, the following steps are included: The adjustment mode to be executed is confirmed, which includes: sleep aid mode, stress relief mode, and incentive mode; wherein, the sleep aid mode is used to hypnotize users who need sleep to help them enter a sleep state; the stress relief mode is used to relieve stress for users with high mental stress; the incentive mode is used to provide mental stimulation for users who need to improve their concentration and / or excitement; wherein, different adjustment modes correspond to different physiological indicators.

31. The state adjustment method according to claim 30, characterized in that, After confirming the adjustment mode to be executed, the process also includes: The user's physiological signals are collected based on the aforementioned adjustment mode; The user's mental state is assessed based on the physiological signals; The adjustment level of the adjustment mode is determined based on the results of the assessment; wherein the adjustment level includes at least two levels, and the level of adjustment is proportional to the degree of intervention formed by the elements of the adjustment scheme. Based on the adjustment mode, an adjustment scheme matching the adjustment level is executed on the user.

32. The state adjustment method according to claim 31, characterized in that, The adjustment of scheme elements in the adjustment scheme based on the state feedback data includes: After acquiring the status feedback data, the user's adjustment feedback status is identified based on the status feedback data. The adjustment feedback status includes: status improvement, status maintenance, and status deterioration. If the adjustment feedback state is an improvement and the current adjustment level is not the lowest level, then the adjustment level of the adjustment scheme is reduced; if the adjustment feedback state is an improvement and the current adjustment level is the lowest level, then the adjustment scheme is stopped. If the adjustment feedback state is "state maintained" and the current adjustment level is not the highest level, then the currently executing adjustment scheme remains unchanged or the adjustment level of the adjustment scheme is increased; if the adjustment feedback state is "state maintained" and the current adjustment level is the highest level, then the currently executing adjustment scheme remains unchanged. If the adjustment feedback state indicates a deterioration and the current adjustment level is not the highest level, then the adjustment level of the adjustment scheme is increased; if the adjustment feedback state indicates a deterioration and the current adjustment level is the highest level, then the currently executing adjustment scheme remains unchanged.

33. The state adjustment method according to claim 29, characterized in that, The adjustment of scheme elements in the adjustment scheme based on the state feedback data includes: Obtain the user's identity identifier, and perform the following steps for the adjustment scheme corresponding to the identity identifier: based on the status feedback data of the completed adjustment scheme, record the adjustment effect corresponding to the scheme element or combination of scheme elements in the adjustment scheme; update the weights corresponding to different scheme elements in the adjustment scheme based on the adjustment effect; the status feedback data includes: the adjustment effect corresponding to different scheme elements or combinations of scheme elements in the adjustment scheme, and the adjustment effect is represented by the changes in the user's physiological signals over a period of time.

34. The state adjustment method according to claim 31, characterized in that, The assessment of the user's mental state based on the physiological signals includes: The collected physiological signals are input into the state assessment model to obtain the mental state assessment results output by the state assessment model; The training samples of the state assessment model include: state monitoring data and / or state feedback data; the state monitoring data includes physiological parameters monitored when the user is in different mental states, and the state feedback data includes the regulatory effects corresponding to different scheme elements or combinations of scheme elements in the regulation scheme, and the regulatory effects are represented by the changes in the user's physiological signals over a period of time; the state assessment model can learn the correlation between the user's different mental states and physiological signals through the training samples.

35. The state adjustment method according to claim 34, characterized in that, After collecting the user's physiological signals based on the adjustment mode, the method further includes: The collected physiological signals are input into the scheme generation model to obtain the adjustment scheme output by the scheme generation model that is adapted to the user's current state; The training samples of the scheme generation model include the state feedback data; the scheme generation model can learn the adjustment effect of various scheme elements or combinations of scheme elements on the user, as well as the correlation between various scheme elements, through the training samples.

36. The state adjustment method according to claim 31, characterized in that, After collecting the user's physiological signals based on the adjustment mode, the method further includes: The acquired physiological signals are subjected to preprocessing operations, including signal amplification, signal filtering, and algorithm averaging.

37. The state adjustment method according to claim 29, characterized in that, The execution methods also include: guided methods and / or electrical stimulation methods; The guidance method is used to guide users to meditate, regulate their breathing rhythm, or perform head movements through voice instructions. The current stimulation method is used to stimulate the user through pulsed current.

38. The state adjustment method according to claim 30, characterized in that, The physiological indicators of the sleep aid mode include: electroencephalogram (EEG) signals, heart rate signals, and eye detection signals; the weighting order of the physiological signals is: EEG signals > heart rate signals > eye detection signals. The adjustment levels of the sleep aid mode include: Level 1, Level 2, and Level 3; the intervention levels corresponding to Level 1, Level 2, and Level 3 increase sequentially; the scheme elements corresponding to Level 1 only include: white noise at a first volume; the scheme elements corresponding to Level 2 include: "white noise + theta wave induced audio" at a second volume, and a warm-colored visual pattern with a first brightness and a first flashing frequency; the scheme elements corresponding to Level 3 include: "white noise + alpha wave induced audio" at a third volume, and a warm-colored visual pattern with a second brightness and a second flashing frequency; wherein, the first volume < the second volume < the third volume, the first brightness < the second brightness, and the first flashing frequency < the second flashing frequency.

39. The state adjustment method according to claim 38, characterized in that, The sleep aid mode also includes: an abnormal state intervention mechanism; The abnormal state intervention mechanism includes: when an abnormal state event is detected, stopping the execution of the adjustment scheme and waking the user through voice prompts and / or vibration prompts; the abnormal state event includes: The user entered sleep mode for a longer than preset duration; The heart rate signal and / or the skin conductance signal exceed the safety threshold.

40. The state adjustment method according to claim 30, characterized in that, The physiological indicators of the stress relief mode include: electroencephalogram (EEG) signals, heart rate signals, and skin conductance signals; the physiological indicators include quiet scene indicators and noisy scene indicators; the weighting of the quiet scene indicators is: EEG signals > heart rate signals > skin conductance signals; the weighting of the noisy scene indicators is: heart rate signals > skin conductance signals > EEG signals. The stress relief mode adjustment levels include: a first level and a second level; the intervention levels corresponding to the first level and the second level increase sequentially; the scheme elements corresponding to the second level include: "natural sound + alpha wave induced audio" at a second volume, and a green visual pattern at a second brightness; the scheme elements corresponding to the first level include: "white noise" at a first volume, and a green visual pattern at a first brightness; wherein, the first volume < the second volume, and the first brightness < the second brightness.

41. The state adjustment method according to claim 40, characterized in that, The stress relief mode also includes: an over-relaxation intervention mechanism; The over-relaxation intervention mechanism includes: after the second-level adjustment scheme has been executed for a preset duration, collecting the user's physiological signals, assessing the user's mental state based on the currently collected physiological signals, and if it is determined that the user is currently in an over-relaxed state, then executing the following adjustment scheme: turning off the guidance mode, changing the visual mode from a green visual pattern to a blue-green alternating pattern; reducing the audio volume of the audio mode, maintaining a natural sound, adding light music segments, and reducing the frequency band of the alpha wave induced audio.

42. The state adjustment method according to claim 30, characterized in that, The physiological indicators of the stimulation mode include: quiet scene indicators and noisy scene indicators; the quiet scene indicators include: electroencephalogram (EEG) signals, eye movement signals, and skin conductance signals; the weights of the quiet scene indicators are: EEG signals > eye movement signals > skin conductance signals; the noisy scene indicators include: heart rate signals, skin conductance signals, and eye movement signals; the weights of the noisy scene indicators are: heart rate signals > skin conductance signals > eye movement signals. The adjustment levels of the excitation mode include: a first level and a second level; the intervention levels corresponding to the first level and the second level increase sequentially; the scheme elements corresponding to the second level include: a fast-paced audio with a second volume and a second beat frequency, and a blue light pulse visual pattern with a second brightness; the scheme elements corresponding to the first level include: a fast-paced audio with a first volume and a first beat frequency, and a blue light pulse visual pattern with a first brightness; wherein, the first volume < the second volume, the first beat frequency < the second beat frequency, and the first brightness < the second brightness.

43. The state adjustment method according to claim 42, characterized in that, The incentive model also includes: an overstimulation intervention mechanism; The overstimulation intervention mechanism includes: after executing the second-level regulation scheme for a preset duration, collecting the user's physiological signals, assessing the user's mental state based on the currently collected physiological signals, and if it is determined that the user is currently in a state of over-excitement, then executing the following regulation scheme: turning off the electrical stimulation mode, changing the visual mode from a blue light pulse pattern to a blue-green pulse pattern, and reducing the pulse frequency; reducing the audio volume in the audio mode, replacing high-frequency sound waves with low-frequency white noise, and reducing the beat frequency of fast-paced audio.

44. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the state adjustment method as described in any one of claims 29 to 43.

45. An electronic device comprising a storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the state adjustment method as described in any one of claims 29 to 43.

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