A PEMF-based sleep adaptive conditioning system and device

Abstract

The application is suitable for the field of sleep health physiotherapy rehabilitation, and provides a sleep self-adaptive adjustment system and device based on PEMF. In the embodiment, for a target user who needs sleep adjustment, a sleep model for sleep adjustment is determined by a sleep model setting module according to the historical sleep state of the user or the sleep state of other users; then the target user is monitored in real time to obtain the sleep state, and the sleep state is compared with the simulation data generated by the sleep model to determine the difference between the two, and the corresponding PEMF stimulation strategy is determined through the difference to perform adaptive stimulation, so that the target user is adjusted according to the sleep model. This realizes the implementation of individualized, refined and differentiated adaptive adjustment for each user's specific sleep problem, thereby improving the accuracy of PEMF stimulation adjustment and improving the sleep effect.

Description

Technical Field

[0001] This application relates to the field of sleep health physiotherapy and rehabilitation, and in particular to a sleep adaptive regulation system and device based on PEMF. Background Technology

[0002] Pulsed Electromagnetic Field (PEMF) uses electromagnetic pulses of specific frequencies and intensities to act on human cells and tissues through various mechanisms, such as activating intracellular signal transduction pathways and regulating the membrane potential of nerve cells. This promotes cell metabolism, regulates the nervous system, and improves blood circulation, showing significant promise in treating chronic diseases, relieving pain, improving sleep, and promoting rehabilitation. In particular, for sleep problems caused by physical fatigue, overactive brain nerves, chronic stress, and circadian rhythm disorders, PEMF utilizes the effect of gradually matching the frequency or pattern of brain waves with external stimuli. It emits low-frequency pulsed electromagnetic fields to synchronize brain waves, modulating the brain's circadian rhythm from a β-wave-α-wave state of wakefulness and relaxation to a theta-delta-wave state of sleep and deep sleep. This reduces brain tissue metabolism and cortical excitation, reduces stress and anxiety, soothes the nervous system, lowers the level of the major stress hormone cortisol, and increases melatonin secretion, thereby improving sleep quality. However, current technologies lack the precision required for PEMF stimulation to regulate sleep, resulting in relatively poor sleep improvement effects. Summary of the Invention

[0003] In view of this, this application provides a sleep adaptive regulation system and device based on PEMF, which implements personalized, refined and differentiated adaptive regulation for each user's specific sleep problems, thereby improving the accuracy of PEMF stimulation regulation and improving sleep effects.

[0004] The first aspect of this application provides a PEMF-based sleep adaptive regulation system, the system including a sleep model setting module, a real-time sleep monitoring module, a difference analysis module, and a PEMF sleep regulation module;

[0005] The sleep model setting module is used to pre-set several different sleep models, determine model information based on the target user, and then determine the target sleep model for sleep regulation of the target user based on the model information.

[0006] The real-time sleep monitoring module is used to monitor the target user's sleep status in real time by using a preset monitoring method.

[0007] The difference analysis module is used to generate a simulated target sleep state of the target user through the target sleep model when the target user is monitored for sleep, and then compare the simulated sleep state with the actual sleep state to obtain the difference information between the two.

[0008] The PEMF sleep regulation module is used to determine the PEMF stimulation strategy based on the simulated target sleep state, then stimulate the target user using the PEMF stimulation strategy, and adjust the PEMF stimulation strategy according to the real-time updated difference information during the stimulation process, and then stimulate the target user again using the adjusted PEMF stimulation strategy until the target user's sleep ends.

[0009] Optionally, the pre-set several different sleep models include:

[0010] A personalized sleep model is set up for the target user based on the target user's historical sleep monitoring data; or,

[0011] A general sleep model is set up for all users based on a large sleep database containing historical sleep monitoring data of each user; or,

[0012] The corresponding theoretical sleep model is established based on authoritative guidelines from a pre-selected third-party organization.

[0013] Optionally, determining the model information based on the target user includes:

[0014] The model information is determined by the selection parameters input by the target user, including model ID, model name, and model type; or...

[0015] The model information is determined based on the target user's basic information, including height, weight, gender, and age; or...

[0016] The model information is determined using the treatment and diagnostic information of the target user.

[0017] Optionally, the system may also include a quality assessment and maintenance module;

[0018] The quality assessment and maintenance module is used to assess the sleep quality of the target user after the target user wakes up using a preset sleep scale. When the sleep quality meets the preset requirements, the module extracts and stores the sleep state, difference information, and corresponding PEMF stimulation strategy to obtain a personalized sleep model database for the target user, which can be used as a candidate for future personal sleep models.

[0019] Optionally, the PEMF stimulation strategy includes: alternating cyclic stimulation of waveband frequencies between alpha, beta, theta, and delta waves to maintain stimulation of a single wave type.

[0020] A second aspect of this application provides a PEMF-based sleep adaptive regulation device, the device comprising:

[0021] The sleep model setting unit is used to pre-set several different sleep models, determine model information based on the target user, and then determine the target sleep model for sleep regulation of the target user based on the model information.

[0022] A real-time sleep monitoring unit is used to monitor the sleep state of the target user in real time using a preset monitoring method.

[0023] The difference analysis unit is used to generate a simulated target sleep state of the target user through the target sleep model when the target user is undergoing sleep monitoring, and then compare the simulated sleep state with the actual sleep state to obtain the difference information between the two.

[0024] The PEMF sleep regulation unit is used to determine the PEMF stimulation strategy based on the simulated target sleep state, then stimulate the target user using the PEMF stimulation strategy, and adjust the PEMF stimulation strategy according to the real-time updated difference information during the stimulation process, and then stimulate the target user using the adjusted PEMF stimulation strategy until the target user's sleep ends.

[0025] Optionally, the sleep model setting unit may pre-set several different sleep models, including:

[0026] A personalized sleep model is set up for the target user based on the target user's historical sleep monitoring data; or,

[0027] A general sleep model is set up for all users based on a large sleep database containing historical sleep monitoring data of each user; or,

[0028] The corresponding theoretical sleep model is established based on authoritative guidelines from a pre-selected third-party organization.

[0029] Optionally, the sleep model setting unit may determine model information based on the target user, including:

[0030] The model information is determined by the selection parameters input by the target user, including model ID, model name, and model type; or...

[0031] The model information is determined based on the target user's basic information, including height, weight, gender, and age; or...

[0032] The model information is determined using the treatment and diagnostic information of the target user.

[0033] Optionally, the device further includes:

[0034] The quality assessment and maintenance unit is used to assess the sleep quality of the target user after the target user wakes up using a preset sleep scale, and when the sleep quality meets the preset requirements, to extract and store the sleep state, difference information and corresponding PEMF stimulation strategy to obtain a personalized sleep model database for the target user as a candidate for future personal sleep models.

[0035] Optionally, the PEMF stimulation strategy in the PEMF sleep regulation unit includes: alternating cyclic stimulation of waveband frequencies between alpha waves, beta waves, theta waves, and delta waves, while maintaining stimulation of a single wave type.

[0036] In the embodiments provided in this application, for a target user requiring sleep regulation, a sleep model setting module first determines a sleep model for sleep regulation based on the user's historical sleep state or the sleep state of other users. Then, the target user's sleep state is monitored in real time to obtain their sleep state, which is then compared with the simulated data generated by the sleep model to determine the differences. Based on these differences, a corresponding PEMF stimulation strategy is determined for adaptive stimulation, allowing the target user to adjust their sleep according to the sleep model. This achieves personalized, refined, and differentiated adaptive regulation for each user's specific sleep problem, thereby improving the accuracy of PEMF stimulation regulation and enhancing sleep outcomes. Attached Figure Description

[0037] Figure 1 A system block diagram provided for embodiments of this application;

[0038] Figure 2 A schematic diagram of the sleep model cycle provided in the embodiments of this application;

[0039] Figure 3 A schematic diagram illustrating a deviation from the sleep model provided in an embodiment of this application;

[0040] Figure 4 This is a band frequency cycling mode diagram provided in the embodiments of this application;

[0041] Figure 5 This is a band frequency cycling mode diagram provided in the embodiments of this application;

[0042] Figure 6 This is a band frequency cycling mode diagram provided in the embodiments of this application;

[0043] Figure 7 This is a band frequency cycling mode diagram provided in the embodiments of this application;

[0044] Figure 8 This is a band frequency cycling mode diagram provided in the embodiments of this application;

[0045] Figure 9 This is a band frequency cycling mode diagram provided in the embodiments of this application;

[0046] Figure 10 This is a band frequency cycling mode diagram provided in the embodiments of this application;

[0047] Figure 11 This is a band frequency cycling mode diagram provided in the embodiments of this application;

[0048] Figure 12 This is a band frequency cycling mode diagram provided in the embodiments of this application;

[0049] Figure 13 This is a band frequency cycling mode diagram provided in the embodiments of this application;

[0050] Figure 14 This is another schematic diagram of a sleep model cycle provided in an embodiment of this application;

[0051] Figure 15 This is a band frequency cycling mode diagram provided in the embodiments of this application;

[0052] Figure 16 This is a band frequency cycling mode diagram provided in the embodiments of this application;

[0053] Figure 17 This is a band frequency cycling mode diagram provided in the embodiments of this application;

[0054] Figure 18 This is a schematic representation of sleep volume provided in the embodiments of this application;

[0055] Figure 19 This is a structural diagram of the device provided in the embodiments of this application;

[0056] Figure 20 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0057] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0058] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0059] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0060] This application provides a PEMF-based sleep adaptive adjustment system to implement personalized, refined, and differentiated adaptive adjustments for each user's specific sleep problems, thereby more effectively improving sleep quality.

[0061] The technical solutions of this application will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0062] like Figure 1 The diagram shown is a block diagram of a PEMF-based sleep adaptive regulation system provided in this application. The functions and effects of each module are described below.

[0063] Module 1, Sleep Model Setting Module. This module is used to pre-set several different sleep models, determine model information based on the target user, and then determine the target sleep model for sleep regulation of the target user based on the model information.

[0064] This module can be seen as a preparation module for falling asleep. The sleep models set in this module are what users want to achieve, representing their satisfactory, high-quality sleep through sleep state-time cycle charts, such as... Figure 2 As shown. This sleep state-time cycle chart can be set by medical professionals and related experts in this field based on experience, or it can be set through some clinical data analysis. After pre-saving multiple sleep models, local unsatisfactory data in existing sleep models can be modified, and the sleep model can be corrected, adjusted, and updated.

[0065] Before falling asleep, users can select a pre-stored sleep model as their goal based on their different sleep states and desired sleep improvement goals, such as increasing total sleep duration, shortening the time to fall asleep, increasing deep sleep duration, and solving the problem of grogginess after waking up. The sleep monitoring unit is then activated, PEMF is enabled, and the user begins to fall asleep.

[0066] In another embodiment, several different sleep models can be pre-defined using various methods, as detailed below:

[0067] Method 1 establishes a theoretical sleep model based on authoritative guidelines from a pre-selected third-party organization. Taking the American Academy of Sleep Medicine (AASM) as an example, a complete sleep cycle (usually lasting 90-120 minutes) is divided into two main stages: non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep. NREM sleep is further divided into three stages: N1, N2, and N3 (for different sleep monitoring technologies, it can also be divided into two stages: "light sleep" (N1 / N2) and "deep sleep" (N3)), gradually transitioning from light sleep to deep sleep. An example of the target sleep cycle pattern is set as follows: sleep onset stage (N1): 1-15 minutes, light sleep stage (N2): 10-25 minutes, deep sleep stage (N3): 20-40 minutes, and rapid eye movement (REM) sleep: 10-60 minutes. During a night's sleep, there are generally 4-6 sleep cycles. As sleep time progresses, the time spent in the REM sleep stage gradually increases, while the time spent in the N3 stage gradually decreases.

[0068] Method 2, a high-quality sleep model based on a large sleep database. This method requires collecting a large amount of historical sleep monitoring data from different users, followed by feature extraction, such as first classifying sleep stages: wakefulness, light sleep, deep sleep, REM sleep, etc. Then, key indicators are determined: sleep onset time, wake-up time, and sleep efficiency. Next, 90-120 minute sleep cycles are identified using FFT or peak detection. Finally, a general sleep model is constructed with time on the horizontal axis and sleep stages on the vertical axis. This general sleep model can be set with different standards based on different parameters, making it applicable to most users. For users undergoing sleep adjustment for the first time, due to limited sleep monitoring data, a personal sleep model cannot be generated; in this case, the general sleep model or the theoretical sleep model determined in Method 1 can be used.

[0069] Method 3 involves establishing a personal sleep model based on the target user's historical sleep monitoring data. For users who have repeatedly adjusted their sleep through this system, a personal sleep model can be determined using this data, as the method is the same as in Method 2 and will not be repeated here. Compared to the sleep models established in Methods 1 and 2, the sleep model established by this method has a higher degree of matching with the user, higher accuracy in PEMF stimulation regulation, and can better improve sleep performance.

[0070] In another embodiment, there are many methods for determining model information based on the target user.

[0071] For example, the model is determined by user-selected parameters. This system provides a model selection interface with controls such as dropdown menus, icons, and input boxes. Users select the corresponding model information using these controls, such as entering the model ID, model name, and model type; or they click on an icon to determine the model ID, model name, and model type corresponding to that icon. This determines the corresponding sleep model.

[0072] For example, the model information is determined based on the target user's basic information. This method requires pre-matching various different basic information with corresponding model information. For example, {age 20-22, gender female, height 150-160, weight 90-110 match model information A}, {age 55-60... model information B}, etc. Then, model information A and model information B are used to match the corresponding sleep model.

[0073] Furthermore, individual basic information can be configured, such as different age ranges corresponding to model information A, B, and C; different heights and weights corresponding to model information A, B, C, and D, etc. Finally, the sleep model matched to the user is determined by using multiple model information.

[0074] For example, the model information can also be determined using the target user's treatment and diagnostic information. This method also requires pre-matching corresponding model information for different treatment and diagnostic information. For instance, if the treatment and diagnostic information determines that the target user has mild snoring, model information A is matched; if the target user has sleep apnea syndrome, model information B is matched, and so on. Similarly, if the target user's treatment and diagnostic information matches multiple different model information, the sleep model matched to the user is then determined using these multiple model information.

[0075] Module 2, Real-time Sleep Monitoring Module. This module is used to monitor the sleep status of the target user in real time using a preset monitoring method.

[0076] In this embodiment, the monitoring method described above can be one or more combinations of electroencephalogram (EEG) monitoring, heart rate / ECG monitoring, electromyography (EMG) monitoring, respiratory monitoring, blood oxygen saturation detection technology, motion monitoring, and cardiopulmonary coupling. Real-time monitoring can be achieved through medical polysomnography, medical or consumer health wearable watches or bracelets, or sleep monitoring mattresses to obtain the sleep state of the target user, such as light sleep, deep sleep, REM sleep, etc.

[0077] Module 3, Difference Analysis Module. This module is used to generate a simulated target sleep state for the target user using the target sleep model during sleep monitoring, and then compare the simulated sleep state with the actual sleep state to obtain difference information between the two.

[0078] In this embodiment, the difference information can be used to determine whether the user's sleep state has deviated from the simulated target sleep state determined by the model, and adjustments can be made according to the direction of the deviation when a deviation occurs. Figure 3 As shown, the green line represents the simulated target sleep state determined by the target sleep model, while the blue line represents the real-time monitored sleep state. The difference between the two can be determined through real-time comparison. The PEMF sleep regulation module then adjusts the user's stimulation based on this difference information.

[0079] Module 4, PEMF Sleep Regulation Module. This module is used to determine the PEMF stimulation strategy based on the simulated target sleep state, then stimulate the target user using the PEMF stimulation strategy, and adjust the PEMF stimulation strategy based on real-time updated difference information during the stimulation process, and then stimulate the target user again using the adjusted PEMF stimulation strategy until the target user's sleep ends.

[0080] In this embodiment, low-frequency pulsed electromagnetic field stimulation is emitted by PEMF to synchronize brain waves and modulate the brain electrical rhythm from the β-wave-α-wave wakefulness and relaxation state to the θ-δ-wave sleep-deep sleep state. This reduces brain tissue metabolism and cerebral cortex excitation, reduces stress and anxiety, soothes the nervous system, lowers the level of the main stress hormone cortisol in the body, and increases the secretion of melatonin, thereby improving sleep quality.

[0081] The specific process of stimulating target users through the PEMF stimulus strategy is as follows:

[0082] 1. During the sleep stage, the user experiences alternating cycles of alpha wave (8-13Hz) and theta wave (4-8Hz) frequencies. The alpha / theta wave pair is N, where N=1 / 2 / 3 is adjustable. The mode is as follows: Figure 4 As shown.

[0083] 2. During the light sleep stage N1, the user experiences alternating cycles of theta wave (4-8Hz) and delta wave (0.3-4Hz) frequencies. Theta wave / delta wave is paired with N, and N=1 / 2 is adjustable. The mode is as follows: Figure 5 As shown.

[0084] 3. During the light sleep stage (N2), the theta wave (4-8Hz) and delta wave (0.3-4Hz) frequencies alternate in a cycle. Theta wave / delta wave is paired with N, and N=3 / 4 / 5 is adjustable, as shown in the pattern. Figure 6 As shown.

[0085] 4. During the N3 deep sleep stage, maintain the delta wave frequency (0.3-4Hz), as shown in the pattern. Figure 7 As shown.

[0086] 5. During REM sleep, the theta wave frequency (4-8Hz) is maintained, and the pattern is as follows: Figure 8 As shown.

[0087] If, during sleep, real-time monitoring reveals a deviation between the sleep state and the target sleep cycle model, adaptive PEMF stimulation adjustment is implemented. Different stimulation adjustment strategies are employed for different deviation scenarios.

[0088] by Figure 3 Taking the example of the diagram, the scenario corresponding to this diagram is the "natural wake-up mode," in which there is no limit to the total sleep time and wake-up time. The PEMF stimulation adjustment strategies corresponding to the five shifts in this diagram are as follows:

[0089] Deviation ①: Difficulty falling asleep or excessively long sleep onset time. This deviation does not help improve sleep quality and is therefore considered a negative deviation. PEMF regulation employs a "correction" strategy. The specific adjustment method involves alternating frequencies of alpha waves (8-13Hz) and theta waves (4-8Hz), with an alpha / theta wave pair corresponding to N, starting with N=1. At regular intervals (e.g., every 5 minutes), N=2, 3, 4, 5, 6, ..., automatically and gradually increasing until sleep occurs. The pattern is as follows: Figure 9 As shown.

[0090] Deviation Scenario ②: Sleep is shallow, deep sleep time is short, and it's easy to transition from deep sleep to light sleep. This deviation does not improve sleep quality and is therefore considered a negative deviation. PEMF regulation employs a "correction" strategy. The specific adjustment method involves alternating frequencies of theta waves (4-8Hz) and delta waves (0.3-4Hz), with a theta / delta wave pair corresponding to N. N starts at 5 and gradually increases automatically at regular intervals (e.g., every 5 minutes) until deep sleep is re-entered. The pattern is as follows: Figure 10 As shown.

[0091] Deviation 3: Frequent brief awakenings during sleep. This deviation does not improve sleep quality and is therefore considered a negative deviation. PEMF regulation employs a "correction" strategy. Specifically, it uses a "sleep-inducing" strategy, alternating the frequencies of alpha waves (8-13Hz) and theta waves (4-8Hz). The alpha / theta wave pair is N, starting at 8 and gradually increasing automatically at regular intervals (e.g., every 5 minutes) until the sleeper falls asleep again. The pattern is as follows: Figure 11 As shown.

[0092] Deviation Scenario 4: Deep sleep time exceeds the target model. This deviation helps increase deep sleep time and improve sleep quality, and is therefore considered a positive deviation. PEMF regulation adopts a "maintain, no correction" strategy. Specifically, the PEMF regulation phase does not use the target sleep model but instead uses real-time monitoring of sleep state, i.e., the "deep sleep N3" stage. PEMF stimulation also follows the "deep sleep N3" stage stimulation strategy in Table 1 (i.e., "maintaining the delta wave (0.3-4Hz) frequency band"), as shown in the pattern. Figure 12 As shown.

[0093] Deviation 5: Waking up prematurely and being unable to fall back asleep. This deviation may lead to insufficient total sleep duration and does not help improve sleep quality; therefore, it is considered a negative deviation. PEMF regulation employs a "correction" strategy. Specifically, a "sleep-inducing" strategy is used, with alternating frequencies of alpha waves (8-13Hz) and theta waves (4-8Hz). The alpha / theta wave pair is N, starting at 5 and gradually increasing automatically at regular intervals (e.g., every 5 minutes) from N=5, 6, 7, 8, 9, 10... until sleep resumes. The pattern is as follows: Figure 13 As shown.

[0094] In the other scenario, the "Alarm Clock Mode," there is a specific wake-up time limit. Compared to the "Natural Wake-up Mode," which has no specific wake-up time limit, the regulation of the sleep onset, light sleep, and deep sleep stages is the same, and will not be repeated here. The main difference between this mode and the "Natural Wake-up Mode" in Scenario 1 is that this mode adds a "wake-up" function: that is, before the scheduled wake-up time (the "alarm clock" time), PEMF stimulation to activate the brain / nerves is gradually applied in advance to smoothly wake the user from sleep, avoiding the user being suddenly awakened by the alarm clock and feeling drowsy or tired.

[0095] Set the wake-up duration ∆t (system default ∆t = 30 minutes; adjustable from 15 to 60 minutes, user-defined). The user sets the wake-up alarm time (e.g., 7:00 AM / 8:00 AM or any other time set by the user). The PEMF adaptive adjustment system initiates PEMF wake-up stimulus adjustment based on the alarm time preceded by ∆t. Figure 14 As shown.

[0096] The PEMF (Peak-Modulated Motion) stimulation strategy for "awakening function" involves a gradual transition from theta waves (4-8 Hz) during sleep to alpha waves (8-13 Hz) during relaxation, and finally to low beta waves (13-15 Hz) during wakefulness. The entire awakening process is divided into three sub-stages:

[0097] The first third of the wake-up duration: alternating frequencies of theta waves (4-8Hz) and alpha waves (8-13Hz), with a theta / alpha wave pair corresponding to N, starting with N=1. At regular intervals (e.g., every minute), N=2, 3, 4, 5, 6, ..., automatically and gradually increasing, as shown in the pattern. Figure 15 As shown.

[0098] The middle third of the wake-up duration consists of alternating frequencies of alpha waves (8-13Hz) and low beta waves (13-15Hz), with an alpha / low beta pair corresponding to N. N starts at 1 and increases automatically at regular intervals (e.g., every minute) to 2, 3, 4, 5, 6, ... , as shown in the pattern. Figure 16 As shown.

[0099] The second third of the awakening duration: Continuous stimulation with low beta wave (13-15Hz) frequencies, as shown in the pattern. Figure 17 As shown.

[0100] There are many other specific scenarios, which will not be elaborated here. However, as can be seen from the above embodiments, by adjusting the frequency of the alpha, beta, theta, and delta waves in an alternating cycle, it is possible to stimulate the user according to different scenarios. This enables the present application to achieve adaptive adjustment of the PEMF stimulation method according to each user's specific sleep problems, thereby improving the accuracy of PEMF stimulation regulation and improving sleep effects.

[0101] This concludes the process. Figure 1 Functional descriptions of each module.

[0102] In this embodiment, for a target user requiring sleep regulation, a sleep model setting module first determines a sleep model for sleep regulation based on the user's historical sleep state or the sleep state of other users. Then, the target user's sleep state is monitored in real time to obtain their sleep state, which is then compared with the simulated data generated by the sleep model to determine the differences. Based on these differences, a corresponding PEMF stimulation strategy is determined for adaptive stimulation, allowing the target user to adjust their sleep according to the sleep model. This achieves personalized, refined, and differentiated adaptive regulation for each user's specific sleep problem, thereby improving the accuracy of PEMF stimulation regulation and enhancing sleep outcomes.

[0103] In another embodiment, the system further includes a quality assessment and maintenance module. This module assesses the sleep quality of the target user after they wake up using a preset sleep scale, such as the Pittsburgh Sleep Quality Index (PSQI). When the sleep quality meets preset requirements, the module extracts and stores the sleep state, difference information, and corresponding PEMF stimulation strategy to obtain a personalized sleep model database for the target user, serving as a candidate for future personal sleep models. The sleep scale is as follows: Figure 18 As shown.

[0104] like Figure 19 As shown, this application also provides a sleep adaptive regulation device based on PEMF, the device comprising:

[0105] The sleep model setting unit 1901 is used to pre-set several different sleep models, determine model information based on the target user, and then determine the target sleep model for sleep regulation of the target user based on the model information.

[0106] The real-time sleep monitoring unit 1902 is used to monitor the sleep state of the target user in real time by using a monitoring method preset by the target user.

[0107] The difference analysis unit 1903 is used to generate a simulated target sleep state of the target user through the target sleep model when the target user is undergoing sleep monitoring, and then compare the simulated sleep state with the actual sleep state to obtain the difference information between the two.

[0108] The PEMF sleep regulation unit 1904 is used to determine the PEMF stimulation strategy based on the simulated target sleep state, then stimulate the target user using the PEMF stimulation strategy, and adjust the PEMF stimulation strategy according to the real-time updated difference information during the stimulation process, and then stimulate the target user using the adjusted PEMF stimulation strategy until the target user's sleep ends.

[0109] In another embodiment, the sleep model setting unit includes several pre-set different sleep models, including:

[0110] A personalized sleep model is set up for the target user based on the target user's historical sleep monitoring data; or,

[0111] A general sleep model is set up for all users based on a large sleep database containing historical sleep monitoring data of each user; or,

[0112] The corresponding theoretical sleep model is established based on authoritative guidelines from a pre-selected third-party organization.

[0113] In another embodiment, the sleep model setting unit determines the model information based on the target user, including:

[0114] The model information is determined by the selection parameters input by the target user, including model ID, model name, and model type; or...

[0115] The model information is determined based on the target user's basic information, including height, weight, gender, and age; or...

[0116] The model information is determined using the treatment and diagnostic information of the target user.

[0117] In another embodiment, the device further includes:

[0118] The quality assessment and maintenance unit 1905 is used to assess the sleep quality of the target user after the target user wakes up using a preset sleep scale, and when the sleep quality meets the preset requirements, to extract and store the sleep state, difference information and corresponding PEMF stimulation strategy to obtain a personalized sleep model database for the target user as a candidate for future personal sleep models.

[0119] In another embodiment, the PEMF stimulation strategy in the PEMF sleep regulation unit includes: alternating cyclic stimulation of waveband frequencies between alpha waves, beta waves, theta waves, and delta waves, while maintaining stimulation of a single wave type.

[0120] The above embodiments of the present invention provide a PEMF-based sleep adaptive regulation system and a PEMF-based sleep adaptive regulation device. Through the above system and device, personalized, refined and differentiated adaptive regulation can be implemented for each user's specific sleep problems, thereby improving the accuracy of PEMF stimulation regulation and improving sleep effects.

[0121] This embodiment also discloses a computer device, such as... Figure 20 As shown, the computer device includes a processor and a memory, the memory storing at least one instruction, which is loaded and executed by the processor to implement the method on the PEMF-based sleep adaptive regulation system described above.

[0122] Furthermore, in the above-described implementation of the PEMF-based sleep adaptive adjustment device, the logical division of each program module is merely illustrative. In practical applications, the functions described above can be assigned to different program modules as needed, for example, for the sake of hardware configuration requirements or software implementation convenience. That is, the internal structure of the PEMF-based sleep adaptive adjustment device can be divided into different program modules to complete all or part of the functions described above.

[0123] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A sleep adaptive regulation system based on PEMF, characterized in that, The system includes a sleep model setting module, a real-time sleep monitoring module, a difference analysis module, and a PEMF sleep regulation module. The sleep model setting module is used to pre-set several different sleep models, determine model information based on the target user, and then determine the target sleep model for sleep regulation of the target user based on the model information. The real-time sleep monitoring module is used to monitor the target user's sleep status in real time by using a preset monitoring method. The difference analysis module is used to generate a simulated target sleep state of the target user through the target sleep model when the target user is undergoing sleep monitoring, and then compare the simulated target sleep state with the actual sleep state to obtain the difference information between the two. The PEMF sleep regulation module is used to determine the PEMF stimulation strategy through the simulated target sleep state, wherein the PEMF stimulation strategy includes: alternating cyclic stimulation of waveband frequencies between alpha waves, beta waves, theta waves and delta waves, and maintaining stimulation of a single type of wave. The target user is then stimulated using the PEMF stimulation strategy, and the strategy is adjusted based on real-time updated difference information during stimulation. Specifically, this includes: determining the direction of deviation of the target user's sleep state relative to the simulated target sleep state based on the difference information; when the deviation is negative, a correction strategy is adopted, which involves alternating the frequencies of two wavebands (alpha and theta waves, or theta and delta waves) and gradually increasing the number of cycles N of the latter waveband relative to the former waveband in the alternation cycle until the target user's sleep state returns to the simulated target sleep state; when the deviation is positive, a maintenance strategy is adopted to maintain the frequency stimulation corresponding to the current real-time sleep state; wherein, negative deviation does not help improve sleep quality, while positive deviation helps increase sleep time and improve sleep quality. Then, stimulation is performed using the adjusted PEMF stimulation strategy until the target user falls asleep.

2. The system according to claim 1, characterized in that, The pre-defined sleep models include: A personalized sleep model is set up for the target user based on the target user's historical sleep monitoring data; or, A general sleep model is established for each user based on a large sleep database containing historical sleep monitoring data; or, The corresponding theoretical sleep model is established based on authoritative guidelines from a pre-selected third-party organization.

3. The system according to claim 1, characterized in that, The step of determining model information based on the target user includes: The model information is determined by the selection parameters input by the target user, including model ID, model name, and model type; or... The model information is determined based on the target user's basic information, including height, weight, gender, and age; or... The model information is determined using the treatment and diagnostic information of the target user.

4. The system according to claim 1, characterized in that, The system also includes a quality assessment and maintenance module; The quality assessment and maintenance module is used to assess the sleep quality of the target user after the target user wakes up using a preset sleep scale. When the sleep quality meets the preset requirements, the module extracts and stores the sleep state, difference information, and corresponding PEMF stimulation strategy to obtain a personalized sleep model database for the target user, which can be used as a candidate for future personal sleep models.

5. A sleep adaptive regulation device based on PEMF, characterized in that, The device includes: The sleep model setting unit is used to pre-set several different sleep models, determine model information based on the target user, and then determine the target sleep model for sleep regulation of the target user based on the model information. A real-time sleep monitoring unit is used to monitor the sleep status of the target user in real time using a preset monitoring method. The difference analysis unit is used to generate a simulated target sleep state of the target user through the target sleep model when the target user is undergoing sleep monitoring, and then compare the simulated target sleep state with the actual sleep state to obtain the difference information between the two. The PEMF sleep regulation unit is used to determine the PEMF stimulation strategy based on the simulated target sleep state, wherein the PEMF stimulation strategy includes: alternating cyclic stimulation of waveband frequencies between alpha waves, beta waves, theta waves and delta waves, and maintaining stimulation of a single type of wave. The target user is then stimulated using the PEMF stimulation strategy, and the strategy is adjusted based on real-time updated difference information during stimulation. Specifically, this includes: determining the direction of deviation of the target user's sleep state relative to the simulated target sleep state based on the difference information; when the deviation is negative, a correction strategy is adopted, which involves alternating the frequencies of two wavebands (alpha and theta waves, or theta and delta waves) and gradually increasing the number of cycles N of the latter waveband relative to the former waveband in the alternation cycle until the target user's sleep state returns to the simulated target sleep state; when the deviation is positive, a maintenance strategy is adopted to maintain the frequency stimulation corresponding to the current real-time sleep state; wherein, negative deviation does not help improve sleep quality, while positive deviation helps increase sleep time and improve sleep quality. Then, stimulation is performed using the adjusted PEMF stimulation strategy until the target user falls asleep.

6. The apparatus according to claim 5, characterized in that, The sleep model setting unit includes several different pre-set sleep models, including: A personalized sleep model is set up for the target user based on the target user's historical sleep monitoring data; or, A general sleep model is established for each user based on a large sleep database containing historical sleep monitoring data; or, The corresponding theoretical sleep model is established based on authoritative guidelines from a pre-selected third-party organization.

7. The apparatus according to claim 5, characterized in that, The sleep model setting unit determines the model information based on the target user, including: The model information is determined by the selection parameters input by the target user, including model ID, model name, and model type; or... The model information is determined based on the target user's basic information, including height, weight, gender, and age; or... The model information is determined using the treatment and diagnostic information of the target user.

8. The apparatus according to claim 5, characterized in that, The device further includes: The quality assessment and maintenance unit is used to assess the sleep quality of the target user after the target user wakes up using a preset sleep scale, and when the sleep quality meets the preset requirements, to extract and store the sleep state, difference information and corresponding PEMF stimulation strategy to obtain a personalized sleep model database for the target user as a candidate for future personal sleep models.

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