A method, system, terminal, and medium for evaluating sleep effects based on a dual threshold verification mechanism.

Abstract

This invention discloses a method, system, terminal, and medium for evaluating sleep effectiveness based on a dual-threshold verification mechanism. The method includes: collecting the user's electroencephalogram (EEG) signals and inertial measurement unit (IMU) signals using a wearable device. The EEG signals reflect electrode contact status, sleep stages, and drowsiness index, while the IMU signals reflect changes in sleep posture and the number of times the user turns over. A dual-threshold verification mechanism is established, including an effective reporting threshold and an effective sleep threshold. The effective reporting threshold and the effective sleep threshold are verified by combining the EEG signals and the IMU signals. After both the effective reporting threshold and the effective sleep threshold pass verification, a final sleep score is calculated, and sleep state evaluation information is determined based on the final sleep score, thus achieving a sleep effectiveness evaluation for the user. This invention, by introducing a dual-threshold verification mechanism of effective reporting threshold and effective sleep threshold, effectively identifies the user's true sleep and achieves sleep effectiveness evaluation, avoiding misjudgments.

Description

Technical Field

[0001] This invention relates to the field of brain-computer interface technology, and in particular to a method, system, terminal, and medium for evaluating sleep effects based on a dual threshold verification mechanism. Background Technology

[0002] Existing sleep quality assessment technologies are mainly based on EEG-based sleep quality determination methods (e.g., determining sleep state and corresponding duration through historical EEG data, and obtaining a sleep score by combining IMU motion data) or sleep quality evaluation methods based on high-frequency EEG.

[0003] However, existing sleep quality assessment technologies primarily rely on gravity sensors and gyroscopes to determine whether a person has fallen asleep, lacking a multi-dimensional quantitative assessment of rest quality. Furthermore, current technologies generally lack the ability to identify and process scenarios where "effective rest has not been achieved," easily misclassifying a user's "eyes closed but not asleep" state as effective rest.

[0004] Therefore, existing technologies still need improvement. Summary of the Invention

[0005] To address the aforementioned shortcomings of existing technologies, this invention provides a method, system, terminal, and medium for evaluating sleep effectiveness based on a dual threshold verification mechanism. The technical solution adopted by this invention is as follows: In a first aspect, the present invention provides a sleep effect evaluation method based on a dual threshold verification mechanism, the method comprising: The system collects the user's electroencephalogram (EEG) signals and inertial measurement unit (IMU) signals using wearable devices. The EEG signals are used to reflect electrode contact status, sleep stages, and drowsiness index, while the IMU signals are used to reflect changes in sleep posture and the number of times the user turns over. A dual threshold verification mechanism is established, including an effective reporting threshold and an effective sleep threshold, and the verification of the effective reporting threshold and the effective sleep threshold is realized based on the EEG signal and the inertial measurement unit signal; After both the valid reporting threshold and the valid sleep threshold are verified and passed, the final sleep score is calculated, and sleep status evaluation information is determined based on the final sleep score to realize the user's sleep effect evaluation.

[0006] In one implementation, a dual threshold verification mechanism is established, including an effective reporting threshold and an effective sleep threshold, comprising: A verification mechanism for an effective reporting threshold is established based on the device usage data, device status, device usage duration, and signal integrity of the wearable device. A verification mechanism for the effective sleep threshold is established based on the percentage of users in an effective rest state and the percentage in a conscious and highly active state.

[0007] In one implementation, the verification of the effective reporting threshold and the effective sleep threshold is based on the electroencephalogram (EEG) signal and the inertial measurement unit (IMU) signal, including: Based on the EEG signal, the ratio between the effective EEG signal segment and the total EEG signal segment is calculated to obtain the signal integrity. Obtain device usage data, device status, and device usage duration of the wearable device; When the device usage data reflects that the wearable device has completed a full use, the device status reflects that the wearable device is functioning normally, the device usage time reaches a preset time, and the signal integrity reaches a first preset threshold, then the valid report threshold verification is determined to be passed.

[0008] In one implementation, the verification of the effective reporting threshold and the effective sleep threshold is based on the electroencephalogram (EEG) signal and the inertial measurement unit (IMU) signal, including: Based on the EEG signals and the inertial measurement unit signals, the percentage of effective resting state and the percentage of awake and highly active state are determined. When the percentage of the effective rest state reaches the second preset threshold and the percentage of the awake and highly active state is less than the third preset threshold, the effective sleep threshold verification is deemed to have passed.

[0009] In one implementation, determining the percentage of effective resting states and the percentage of awake, highly active states based on the electroencephalogram (EEG) signals and the inertial measurement unit (IMU) signals includes: Based on the electroencephalogram (EEG) signals, the electrode contact status, sleep stages, and drowsiness index are determined. Based on the signals from the inertial measurement unit, the number of times the user turns over and the changes in sleep posture are determined; Obtain effective rest signal segments for the sleep stages of light sleep, deep sleep, and rapid eye movement; or, obtain effective rest signal segments for the sleep stage of wakefulness where the drowsiness index reaches a preset range, the number of times the person turns over within a preset time period is less than a first preset number, the number of sleep posture changes is less than a second preset number, and the electrode contact state is stable. The effective rest state percentage is obtained based on the ratio between the effective rest signal segment and the total signal segment. The system acquires a high-activity wakefulness signal segment when the sleep stage is a waking state and the drowsiness index is less than a fourth preset threshold; or, it acquires a high-activity wakefulness signal segment when the sleep stage is a waking state and the number of times the sleeper turns over and the number of sleep posture changes within a preset time period are both greater than a third preset number. The proportion of the awake and highly active state is obtained based on the ratio between the awake and highly active signal segments and the total signal segments.

[0010] In one implementation, after both the valid reporting threshold and the valid sleep threshold have passed verification, the final sleep score is calculated, including: After both the effective reporting threshold and the effective sleep threshold are verified and passed, the rest effectiveness score is calculated based on the effective rest duration coefficient, the effective rest state percentage, and the rest approach coefficient. The rest approach coefficient is calculated based on the drowsiness index. Awakening alertness score is calculated based on wake-up completion coefficient, wake-up timing coefficient, and post-wake stability coefficient. The reliability score is calculated based on signal integrity, contact state stability, low body motion coefficient, and equipment continuous use coefficient. The final sleep score is obtained based on the rest effectiveness score, wakefulness score, and process credibility score.

[0011] In one implementation, the method further includes: The final sleep score is then used for brain charging visual representation to obtain brain charging visual representation information. The final sleep score, the sleep state evaluation information, and the visual expression information of brain charging are visualized.

[0012] Secondly, embodiments of the present invention also provide a sleep effect evaluation system based on a dual threshold verification mechanism, wherein the system is used to implement the steps of the sleep effect evaluation method based on a dual threshold verification mechanism as described in any of the above technical solutions, and the system includes: A multidimensional signal acquisition module is used to acquire the user's electroencephalogram (EEG) signals and inertial measurement unit (IMU) signals based on a wearable device. The EEG signals are used to reflect electrode contact status, sleep stages, and drowsiness index, while the IMU signals are used to reflect changes in sleep posture and the number of times the user turns over. A dual threshold verification module is used to establish a dual threshold verification mechanism including a valid reporting threshold and a valid sleep threshold, and to verify the valid reporting threshold and the valid sleep threshold based on the EEG signal and the inertial measurement unit signal; The sleep status evaluation module is used to calculate the final sleep score after both the valid report threshold and the valid sleep threshold have been verified and passed, and to determine the sleep status evaluation information based on the final sleep score, so as to realize the user's sleep effect evaluation.

[0013] Thirdly, embodiments of the present invention also provide a terminal, wherein the terminal includes a memory, a processor, and a sleep effect evaluation program based on a dual threshold verification mechanism stored in the memory and executable on the processor. When the processor executes the sleep effect evaluation program based on the dual threshold verification mechanism, it implements the steps of the sleep effect evaluation method based on the dual threshold verification mechanism in any of the above-mentioned schemes.

[0014] Fourthly, embodiments of the present invention also provide a computer-readable storage medium, wherein the computer-readable storage medium stores a sleep effect evaluation program based on a dual threshold verification mechanism, the sleep effect evaluation program based on the dual threshold verification mechanism implementing the steps of the sleep effect evaluation method based on the dual threshold verification mechanism as described in any of the above schemes on the computer-readable storage medium.

[0015] Beneficial Effects: Compared with existing technologies, this invention provides a sleep effect evaluation method based on a dual threshold verification mechanism. First, it collects the user's electroencephalogram (EEG) signals and inertial measurement unit (IMU) signals using a wearable device. The EEG signals reflect electrode contact status, sleep stages, and drowsiness index, while the IMU signals reflect changes in sleep posture and the number of times the user turns over. Next, a dual threshold verification mechanism is established, including an effective reporting threshold and an effective sleep threshold. The effective reporting threshold and the effective sleep threshold are verified by combining the EEG signals and the IMU signals. Then, after both the effective reporting threshold and the effective sleep threshold are verified, a final sleep score is calculated, and sleep state evaluation information is determined based on the final sleep score, thus achieving a sleep effect evaluation for the user.

[0016] This invention designs a dedicated multi-dimensional rest effect evaluation system for short-sleep scenarios (e.g., 10-45 minutes). It differs fundamentally from overnight sleep evaluation in terms of time scale, parameter weights, and scoring logic. Furthermore, by introducing a dual verification mechanism of valid reporting threshold and valid sleep threshold, it effectively distinguishes between two scenarios: wearing wearable devices but not resting and truly entering a resting state, thus avoiding misjudgment. Attached Figure Description

[0017] Figure 1 This is a flowchart of a preferred embodiment of the sleep effect evaluation method based on a dual threshold verification mechanism according to the present invention.

[0018] Figure 2 This is a flowchart illustrating the practical application of the sleep effect evaluation method based on a dual threshold verification mechanism according to an embodiment of the present invention.

[0019] Figure 3 This is a technical framework diagram of a sleep effect evaluation system based on a dual threshold verification mechanism according to an embodiment of the present invention.

[0020] Figure 4 A schematic diagram of a terminal provided in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0022] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content, operations, or steps, nor does it require execution in the described order. For example, some operations or steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0023] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0024] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. For example, the first control information and the second control information are only used to distinguish different control information and do not limit their order.

[0025] Those skilled in the art will understand that the words "first" and "second" do not limit the quantity or the order of execution, and that the words "first" and "second" do not necessarily imply that they are different.

[0026] It should also be understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0027] The method in this embodiment can be applied to a terminal, which can be an intelligent electronic device such as a computer. Figure 1 As shown in the figure, the sleep effect evaluation method based on the dual threshold verification mechanism in this embodiment specifically includes the following steps: Step S100: Collect the user's electroencephalogram (EEG) signals and inertial measurement unit (IMU) signals based on the wearable device, wherein the EEG signals are used to reflect electrode contact status, sleep stages, and drowsiness index, and the IMU signals are used to reflect changes in sleep posture and the number of times the user turns over.

[0028] During their afternoon nap, users wear wearable devices that collect multi-dimensional physiological signals, including electroencephalogram (EEG) signals and inertial measurement unit (IMU) signals. The EEG signals reflect electrode contact status, sleep stages, and drowsiness index. Sleep stages specifically include Light (light sleep), Deep (deep sleep), and REM (rapid eye movement) sleep. The drowsiness index is an objective quantitative indicator derived from EEG signal analysis. It reflects a person's physiological tendency or degree of transition from wakefulness to sleep, and is academically often referred to as the Drowsiness Index or Fatigue Index. The IMU signals reflect changes in sleep posture and the number of times the user turns over during sleep.

[0029] In practical applications, combined with Figure 2 As shown, in this embodiment, after acquiring device data, the acquired EEG signals and inertial measurement unit (IMU) signals are further segmented and preprocessed to determine signal quality. For example, the acquired EEG and IMU signals are segmented into uniform signal segments using a fixed time window (e.g., 1 minute). Then, the intensity of the EEG and IMU signals is filtered to remove weak signals, thereby selecting valid signal segments. Alternatively, this embodiment can also determine the electrode contact state during EEG signal acquisition. If the electrode contact state is stable, the acquired EEG signal is considered valid. In other implementations, the wearable device in this embodiment can also acquire photoplethysmography (PPG) data to assist in verifying the signal quality and contact state of the data.

[0030] Step S200: Establish a dual threshold verification mechanism including a valid reporting threshold and a valid sleep threshold, and verify the valid reporting threshold and the valid sleep threshold based on the EEG signal and the inertial measurement unit signal.

[0031] Next, this embodiment establishes a dual threshold verification mechanism, which includes an effective reporting threshold and an effective sleep threshold. Specifically, this embodiment establishes an effective reporting threshold verification mechanism based on the wearable device's usage data, device status, device usage duration, and signal integrity. The effective reporting threshold is defined as follows: the device usage data reflects that the wearable device has completed a full usage cycle; the device status reflects that the wearable device is functioning normally; the device usage duration reaches a preset duration; and the signal integrity reaches a first preset threshold. This embodiment also establishes an effective sleep threshold verification mechanism based on the user's effective rest state percentage and awake / high-activity state percentage. The effective sleep threshold is defined as follows: the effective rest state percentage reaches a second preset threshold, and the awake / high-activity state percentage is less than a third preset threshold. The effective rest state percentage is the ratio between effective rest signal segments and total signal segments; the awake / high-activity state percentage is the ratio between awake / high-activity signal segments and total signal segments.

[0032] When performing dual threshold verification, the valid report threshold is verified first. Only after the valid report threshold verification is passed is the valid sleep threshold verification performed. Only after both threshold verifications are passed is the user's valid sleep identified, and then sleep effect evaluation is conducted. Specifically, when verifying the valid report threshold, this embodiment first filters the EEG signal for signal quality and, in conjunction with the stability of electrode contact, filters out valid EEG signal segments. Then, it calculates the ratio between valid EEG signal segments and total EEG signal segments to obtain the signal integrity. Next, the device usage data, device status, and device usage duration of the wearable device are analyzed. When the device usage data reflects that the wearable device has completed a full use, the device status reflects that the wearable device is functioning normally, the device usage duration reaches a preset duration, and the signal integrity reaches a first preset threshold (e.g., signal integrity ≥ 60%), then the valid report threshold verification is considered to have passed. In other implementations, if the signal integrity is below a first preset threshold, device usage data, device status, and device usage duration are not analyzed. In this case, subsequent verification of the effective sleep threshold and sleep effect evaluation are not performed; only the collected data is recorded. This means that even if the user is clearly in a resting state, if there are insufficient effective EEG segments, the subsequent sleep effect evaluation process will not be triggered.

[0033] Furthermore, in verifying the effective sleep threshold, this embodiment first determines the proportion of effective rest state and the proportion of awake and highly active state based on the EEG signal and the inertial measurement unit signal. Then, when the proportion of effective rest state reaches a second preset threshold and the proportion of awake and highly active state is less than a third preset threshold, the effective sleep threshold verification is deemed successful. In this embodiment, the proportion of effective rest state is the ratio between effective rest signal segments and total signal segments. Effective rest segments are signal segments in sleep stages of light sleep, deep sleep, and REM sleep, or, when the sleep stage is awake, a drowsiness index reaches a preset range and the individual is in a low-movement state. The proportion of awake and highly active state is the ratio between awake and highly active signal segments and total signal segments. Awakening and highly active signal segments are awake and highly active signal segments in sleep stages of awake state where the drowsiness index is less than a fourth preset threshold, or, when the sleep stage is awake state, a individual is in a state of significant movement.

[0034] In verifying the effective sleep threshold, this embodiment first determines the electrode contact state, sleep stage, and drowsiness index based on the EEG signals. When calculating the drowsiness index, this embodiment extracts key EEG features related to drowsiness and fatigue from the collected EEG signals, including the EEG power spectrum and specific frequency band power wall ratios, such as the β / (θ+α) ratio, (θ+α) / β ratio, and θ / α ratio. β waves are associated with wakefulness and alertness, while α / θ waves are associated with relaxation and drowsiness. Therefore, an increased β / (θ+α) ratio may indicate greater alertness, while a decreased ratio may indicate greater fatigue. An increased (θ+α) / β ratio is generally associated with increased fatigue; furthermore, enhanced θ wave activity and decreased α wave activity are also considered indicators of drowsiness. Therefore, one or more of the extracted features are fused using a specific algorithm model (such as optimization algorithms, neural networks, etc.) to finally calculate a drowsiness index that changes over time. Next, this embodiment determines the number of times the user turns over and changes in sleep posture based on the inertial measurement unit signals. Then, this embodiment acquires effective rest signal segments for the sleep stages of light sleep, deep sleep, and REM sleep, or, when the sleep stage is awake, the drowsiness index reaches a preset range (e.g., 700-100), the number of times the user turns over within a preset time period is less than a first preset number (e.g., ≤1 time), the number of sleep posture changes is less than a second preset number (e.g., ≤2 times), and the electrode contact state is stable. Therefore, the proportion of the effective rest state can be obtained based on the ratio between the effective rest signal segments and the total signal segments. When determining the highly active awake signal segments, this embodiment can acquire highly active awake signal segments when the sleep stage is awake and the drowsiness index is less than a fourth preset threshold (e.g., <50), or, when the sleep stage is awake and the number of times the user turns over and the number of sleep posture changes within a preset time period are both greater than a third preset number (e.g., both >2 times), indicating that the user has not entered a rest state. Therefore, the proportion of the highly active awake state can be obtained based on the ratio between the highly active awake signal segments and the total signal segments. Furthermore, in this embodiment, when the percentage of effective rest reaches a second preset threshold and the percentage of awake and highly active states is less than a third preset threshold, the effective sleep threshold verification is considered successful. For example, when the effective rest percentage is ≥20% and the percentage of awake and highly active states is less than 80%, the effective sleep threshold verification is successful, and the user's effective sleep is identified. If the percentage of effective rest does not reach the second preset threshold, or the percentage of awake and highly active states is greater than the third preset threshold, it indicates that no effective rest was detected. In this case, the subsequent calculation process for the final sleep score will not proceed; only the relevant data will be retained.

[0035] This embodiment sets up a dual threshold verification mechanism. First, the reliability of the collected data is determined by verifying the effective reporting threshold. Then, the effective sleep threshold is verified to confirm the real existence of the sleep state. This effectively distinguishes between two scenarios: wearing wearable devices but not resting and truly entering a resting state, thus avoiding misjudgment of effective sleep.

[0036] Step S300: After both the valid report threshold and the valid sleep threshold are verified and passed, the final sleep score is calculated, and the sleep status evaluation information is determined based on the final sleep score to realize the user's sleep effect evaluation.

[0037] Combination Figure 2 As shown, this embodiment evaluates sleep effectiveness after both the valid reporting threshold and the valid sleep threshold have passed verification. This embodiment determines sleep state evaluation information by calculating a final sleep score, which is based on rest effectiveness score, wakefulness score, and process credibility score.

[0038] Specifically, this embodiment first calculates the rest effectiveness score based on the effective rest duration coefficient, the percentage of effective rest states, the rest proximity coefficient, and their respective weights. In practical applications, the effective rest duration coefficient is determined based on the user's specific effective rest minutes. For example, if the effective rest minutes are less than 10 minutes, the effective rest duration coefficient is 0; if the effective rest minutes are 10-20 minutes, the effective rest duration coefficient = (effective rest minutes - 10) / 10; if the effective rest minutes are 20-30 minutes, the effective rest duration coefficient is 1; if the effective rest minutes are 30-45 minutes, the effective rest duration coefficient = 1 - (effective rest minutes - 30) / 30; and if the effective rest minutes are ≥45 minutes, the effective rest duration coefficient is 0.5. The rest proximity coefficient is calculated as: (average drowsiness index in the second half - average drowsiness index in the first 5 minutes + 20) / 40. Preferably, the rest proximity coefficient in this embodiment does not exceed 0.1. After calculating the effective rest duration coefficient, the effective rest state percentage, and the rest approach coefficient, this embodiment can obtain the weights corresponding to the effective rest duration coefficient, the effective rest state percentage, and the rest approach coefficient, and perform a weighted summation to obtain the rest effectiveness score. For example, the calculation formula is: Rest effectiveness score = 0.45 × effective rest duration coefficient + 0.40 × rest state percentage + 0.15 × rest approach coefficient.

[0039] Next, this embodiment calculates the wake-up alertness score based on the wake-up completion coefficient, wake-up timing coefficient, and post-wake stability coefficient. Specifically, the wake-up completion coefficient is related to whether the user is awake. When the user has been woken up, or the wake-up function of the wearable device (such as an alarm clock or music playback) is triggered, and the user actively removes the wearable device after the wake-up function has been completed, the wake-up completion coefficient is 1. If the wake-up function of the wearable device is triggered but prematurely terminated, and the user is already awake, the wake-up completion coefficient is 0.8. If the wake-up function is executed, but the user is not woken up, the wake-up completion coefficient is 0.5. If the wake-up function of the wearable device is not triggered, the wake-up completion coefficient is 0. The wake-up timing coefficient in this embodiment is related to the proportion of wake-up risk segments in the three minutes before wake-up. The three minutes before wake-up refers to the three-minute time window before the wake-up function is triggered. The proportion of wake-up risk segments = the number of segments identified as "wake-up risk segments" in the three minutes before wake-up ÷ the total number of segments in the three minutes before wake-up (calculated in a 1-minute window, a total of 3 segments). In this embodiment, the wake-up risk segments are derived from deep sleep segments, segments with significant jumps in drowsiness index, and segments with obvious body movement. In practical applications, the wake-up timing coefficient can be set as shown in Table 1.

[0040] Table 1

[0041] In this embodiment, the post-awakening stability coefficient is calculated as: (Number of stable post-awakening segments within 2 minutes of awakening) ÷ (Number of effective post-awakening signal segments). The core logic of this coefficient is that within 2 minutes of awakening, the higher the proportion of "stable" segments among the available effective signals, the closer the coefficient is to 1, indicating a smoother transition from rest to wakefulness. The number of effective post-awakening signal segments represents the number of segments within these 2 minutes where the EEG signal quality is normal when the EEG electrodes are in contact with the skin. The number of stable post-awakening segments must simultaneously meet the following three conditions: the sleep stage is awake, the drowsiness index is less than 50 or the drowsiness index decreases by ≥20 compared to before awakening, and there is no continuous significant body movement. "No continuous significant body movement" means that no sustained body movement occurs within this 1-minute segment; occasional single turning over or minor posture adjustments do not constitute continuous significant body movement. Therefore, the post-awakening stability coefficient can be obtained based on the ratio between the number of stable post-awakening segments and the number of effective post-awakening signal segments within 2 minutes of awakening. In practical applications, the value of the post-awakening stability coefficient can be shown in Table 2.

[0042] Table 2

[0043] After calculating the wake-up completion coefficient, wake-up timing coefficient, and post-wake stability coefficient, this embodiment can perform a weighted sum based on the wake-up completion coefficient, wake-up timing coefficient, and post-wake stability coefficient to obtain the wake-up alertness score. The calculation formula for the wake-up alertness score is: Wake-up alertness score = 0.40 × Wake-up completion coefficient + 0.35 × Wake-up timing coefficient + 0.25 × Post-wake stability coefficient.

[0044] Furthermore, this embodiment can calculate the reliability score based on signal integrity, contact stability, low body movement coefficient, and continuous equipment use coefficient. Specifically, signal integrity = effective EEG signal segments ÷ total EEG signal segments. Effective EEG signal segments refer to signal segments where the EEG electrodes are in contact with the skin and the signal quality is normal. Contact stability = 1 Number of failed contact segments ÷ Total number of EEG signal segments. The number of failed contact segments refers to EEG electrodes not making contact with the skin or continuous abnormal EEG signals, such as consecutive excessively large or small EEG signals. Low body movement coefficient = 1 The formula `clamp(obvious body movement segment percentage / 20%, 0, 1)` means that `x` is limited to between 0 and 1, with values ​​less than 0 counted as 0 and values ​​greater than 1 counted as 1. In practical applications, low body movement refers to turning over ≤1 times and changing posture ≤2 times per minute. Obvious body movement refers to turning over ≥2 times or changing posture ≥3 times per minute. Obvious body movement segment percentage - number of signal segments with obvious body movement ÷ total number of EEG signal segments. Device continuous use coefficient = 1 - `clamp((number of device pauses × 2 + number of minutes the device was actively removed) ÷ total number of minutes the device was used, 0, 1)`. After calculating the signal integrity, contact stability, low body movement coefficient, and device continuous use coefficient, this embodiment can perform a weighted sum based on the signal integrity, contact stability, low body movement coefficient, and device continuous use coefficient to obtain the process reliability score. In practical applications, the calculation formula for the process reliability score can be: Process Reliability Score = 0.40 × Signal Integrity + 0.30 × Contact Stability + 0.20 × Low Body Motion Coefficient + 0.10 × Continuous Conversation Coefficient. In other implementations, this embodiment can also collect photoplethysmography (PPG) signals, which reflect the user's blood oxygenation and heart rate during sleep. Then, the EEG signal, inertial unit signal, and PPG signal are combined to comprehensively analyze the wearing status of the wearable device, accurately identify the electrode connection status, reduce the misjudgment rate of contact stability, and improve the accuracy of process reliability calculation. Further, after calculating the rest effectiveness score, wakefulness score, and process reliability score, this embodiment can perform a weighted sum to obtain the final sleep score. The weight of the rest effectiveness score is 50, the weight of the wakefulness score is 25, and the weight of the process reliability score is 25. The calculation formula for the final sleep score is: Final Sleep Score = 50 × Rest Effectiveness Score + 25 × Wakefulness Score + 25 × Process Reliability Score.

[0045] Furthermore, in this embodiment, after obtaining the final sleep score, the final sleep score is mapped to sleep state evaluation information. In practical applications, this embodiment can obtain the numerical range of the final sleep score, and then determine the sleep state evaluation information based on this numerical range. For example, when the final sleep score is in the range of 80-100, the sleep state evaluation information is "very good sleep." When the final sleep score is in the range of 70-80, the sleep state evaluation information is "good sleep." When the final sleep score is in the range of 60-70, the sleep state evaluation information is "average sleep." When the final sleep score is in the range of 50-60, the sleep state evaluation information is "sleep state needs improvement." When the final sleep score is in the range of 0-50, the sleep state evaluation information is "no effective rest."

[0046] Furthermore, in other implementations, this embodiment can also perform a brain-charging visual representation of the final sleep score to obtain brain-charging visual representation information. Similarly, the brain-charging visual representation information can be determined based on the numerical range of the final sleep score. The higher the final sleep score, the better the sleep state evaluation information, and the more fully the brain rests; therefore, the more positive the brain-charging visual representation information will be. Finally, this embodiment can also visualize the final sleep score, the sleep state evaluation information, and the brain-charging visual representation information to provide an intuitive display of the user's sleep state.

[0047] The method in this embodiment can be applied to long-term sleep scenarios, nap scenarios, or other short-term rest scenarios.

[0048] Based on the above embodiments, the present invention also provides a sleep effect evaluation system based on a dual threshold verification mechanism, the system being used to implement the steps of the above method embodiments. Figure 3 As shown, the system includes a multi-dimensional signal acquisition module 10, a dual threshold verification module 20, and a sleep state evaluation module 30. Specifically, the multi-dimensional signal acquisition module 10 is used to acquire the user's electroencephalogram (EEG) signals and inertial measurement unit (IMU) signals based on a wearable device. The EEG signals reflect electrode contact status, sleep stages, and drowsiness index, while the IMU signals reflect changes in sleep posture and the number of times the user turns over. The dual threshold verification module 20 is used to establish a dual threshold verification mechanism including a valid reporting threshold and a valid sleep threshold, verifying the valid reporting threshold and the valid sleep threshold based on the EEG signals and the IMU signals. The sleep state evaluation module 30 is used to calculate a final sleep score after both the valid reporting threshold and the valid sleep threshold have passed verification, and to determine sleep state evaluation information based on the final sleep score, thereby evaluating the user's sleep effectiveness.

[0049] The principle of each module in the sleep effect evaluation system based on the dual threshold verification mechanism in this embodiment is the same as the implementation process of each step in the above method embodiment, and will not be elaborated further here.

[0050] Based on the above embodiments, the present invention also provides a terminal, the principle block diagram of which can be as follows: Figure 4 As shown. The terminal may include one or more processors 100 ( Figure 4(Only one is shown in the image), memory 101, and computer program 102 stored in memory 101 and executable on one or more processors 100. For example, a sleep effect evaluation program based on a dual threshold verification mechanism. When one or more processors 100 execute computer program 102, they can implement the various steps in the embodiment of the sleep effect evaluation method based on a dual threshold verification mechanism. Alternatively, when one or more processors 100 execute computer program 102, they can implement the functions of each module / unit in the embodiment of the sleep effect evaluation device based on a dual threshold verification mechanism, which is not limited here.

[0051] In one embodiment, the processor 100 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.

[0052] In one embodiment, memory 101 can be an internal storage unit of the terminal, such as a hard disk or RAM. Memory 101 can also be an external storage terminal of the terminal, such as a plug-in hard disk, smart media card (SM), secure digital card (SD), flash card, etc., all equipped on the terminal. Furthermore, memory 101 can include both internal and external storage units. Memory 101 is used to store computer programs and other programs and data required by the terminal. Memory 101 can also be used to temporarily store data that has been output or will be output.

[0053] Those skilled in the art will understand that Figure 4 The block diagram shown is merely a partial structural diagram related to the present invention and does not constitute a limitation on the terminal to which the present invention is applied. A specific terminal may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0054] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), direct memory bus RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A sleep effect evaluation method based on a dual threshold verification mechanism, characterized in that, The method includes: The system collects the user's electroencephalogram (EEG) signals and inertial measurement unit (IMU) signals using wearable devices. The EEG signals are used to reflect electrode contact status, sleep stages, and drowsiness index, while the IMU signals are used to reflect changes in sleep posture and the number of times the user turns over. A dual threshold verification mechanism is established, including an effective reporting threshold and an effective sleep threshold, and the verification of the effective reporting threshold and the effective sleep threshold is realized based on the EEG signal and the inertial measurement unit signal; After both the valid reporting threshold and the valid sleep threshold are verified and passed, the final sleep score is calculated, and sleep status evaluation information is determined based on the final sleep score to realize the user's sleep effect evaluation.

2. The sleep effect evaluation method based on a dual threshold verification mechanism according to claim 1, characterized in that, Establish a dual threshold verification mechanism that includes both an effective reporting threshold and an effective sleep threshold, including: A verification mechanism for an effective reporting threshold is established based on the device usage data, device status, device usage duration, and signal integrity of the wearable device. A verification mechanism for the effective sleep threshold is established based on the percentage of users in an effective rest state and the percentage in a conscious and highly active state.

3. The sleep effect evaluation method based on a dual threshold verification mechanism according to claim 2, characterized in that, The verification of the effective reporting threshold and the effective sleep threshold based on the EEG signal and the inertial measurement unit signal includes: Based on the EEG signal, the ratio between the effective EEG signal segment and the total EEG signal segment is calculated to obtain the signal integrity. Obtain device usage data, device status, and device usage duration of the wearable device; When the device usage data reflects that the wearable device has completed a full use, the device status reflects that the wearable device is functioning normally, the device usage time reaches a preset time, and the signal integrity reaches a first preset threshold, then the valid report threshold verification is determined to be passed.

4. The sleep effect evaluation method based on a dual threshold verification mechanism according to claim 3, characterized in that, The verification of the effective reporting threshold and the effective sleep threshold based on the EEG signal and the inertial measurement unit signal includes: Based on the EEG signals and the inertial measurement unit signals, the percentage of effective resting state and the percentage of awake and highly active state are determined. When the percentage of the effective rest state reaches the second preset threshold and the percentage of the awake and highly active state is less than the third preset threshold, the effective sleep threshold verification is deemed to have passed.

5. The sleep effect evaluation method based on a dual threshold verification mechanism according to claim 4, characterized in that, Based on the EEG signals and the inertial measurement unit signals, the percentage of effective resting states and the percentage of awake, highly active states are determined, including: Based on the electroencephalogram (EEG) signals, the electrode contact status, sleep stages, and drowsiness index are determined. Based on the signals from the inertial measurement unit, the number of times the user turns over and the changes in sleep posture are determined; Obtain effective rest signal segments for the sleep stages of light sleep, deep sleep, and rapid eye movement; or, obtain effective rest signal segments for the sleep stage of wakefulness where the drowsiness index reaches a preset range, the number of times the person turns over within a preset time period is less than a first preset number, the number of sleep posture changes is less than a second preset number, and the electrode contact state is stable. The effective rest state percentage is obtained based on the ratio between the effective rest signal segment and the total signal segment. The system acquires a high-activity wakefulness signal segment when the sleep stage is a waking state and the drowsiness index is less than a fourth preset threshold; or, it acquires a high-activity wakefulness signal segment when the sleep stage is a waking state and the number of times the sleeper turns over and the number of sleep posture changes within a preset time period are both greater than a third preset number. The proportion of the awake and highly active state is obtained based on the ratio between the awake and highly active signal segments and the total signal segments.

6. The sleep effect evaluation method based on a dual threshold verification mechanism according to claim 1, characterized in that, After both the valid reporting threshold and the valid sleep threshold are verified and passed, the final sleep score is calculated, including: After both the effective reporting threshold and the effective sleep threshold are verified and passed, the rest effectiveness score is calculated based on the effective rest duration coefficient, the effective rest state percentage, and the rest approach coefficient. The rest approach coefficient is calculated based on the drowsiness index. Awakening alertness score is calculated based on wake-up completion coefficient, wake-up timing coefficient, and post-wake stability coefficient. The reliability score is calculated based on signal integrity, contact state stability, low body motion coefficient, and equipment continuous use coefficient. The final sleep score is obtained based on the rest effectiveness score, wakefulness score, and process credibility score.

7. The sleep effect evaluation method based on a dual threshold verification mechanism according to claim 1, characterized in that, The method further includes: The final sleep score is then used for brain charging visual representation to obtain brain charging visual representation information. The final sleep score, the sleep state evaluation information, and the visual expression information of brain charging are visualized.

8. A sleep effect evaluation system based on a dual threshold verification mechanism, characterized in that, The system is used to implement the steps of the sleep effect evaluation method based on a dual threshold verification mechanism as described in any one of claims 1-7, and the system includes: A multidimensional signal acquisition module is used to acquire the user's electroencephalogram (EEG) signals and inertial measurement unit (IMU) signals based on a wearable device. The EEG signals are used to reflect electrode contact status, sleep stages, and drowsiness index, while the IMU signals are used to reflect changes in sleep posture and the number of times the user turns over. A dual threshold verification module is used to establish a dual threshold verification mechanism including a valid reporting threshold and a valid sleep threshold, and to verify the valid reporting threshold and the valid sleep threshold based on the EEG signal and the inertial measurement unit signal; The sleep status evaluation module is used to calculate the final sleep score after both the valid report threshold and the valid sleep threshold have been verified and passed, and to determine the sleep status evaluation information based on the final sleep score, so as to realize the user's sleep effect evaluation.

9. A terminal, characterized in that, The terminal includes a memory, a processor, and a sleep effect evaluation program based on a dual threshold verification mechanism stored in the memory and executable on the processor. When the processor executes the sleep effect evaluation program based on the dual threshold verification mechanism, it implements the steps of the sleep effect evaluation method based on the dual threshold verification mechanism as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a sleep effect evaluation program based on a dual threshold verification mechanism, and the sleep effect evaluation program based on the dual threshold verification mechanism implements the steps of the sleep effect evaluation method based on the dual threshold verification mechanism as described in any one of claims 1-7 on the computer-readable storage medium.

Images