PEMF-based sleep adaptive adjusting system and device

Abstract

The invention is suitable for the field of sleep health physiotherapy rehabilitation, and provides a PEMF-based sleep self-adaptive adjusting system and a PEMF-based sleep self-adaptive adjusting device. In the embodiment, for a target user needing sleep adjustment, a sleep model for sleep adjustment is determined through a sleep model setting module according to the historical sleep state of the user or the sleep states of other users; the method comprises the following steps: acquiring a target user, monitoring the target user in real time to obtain a sleep state of the target user, comparing the sleep state with simulation data generated by a sleep model to determine a difference between the sleep state and the simulation data, and determining a corresponding PEMF stimulation strategy according to the difference to carry out adaptive stimulation, so that the target user carries out sleep adjustment on the target user according to the sleep model. According to the method and the device, personalized, refined and differentiated adaptive adjustment is implemented aiming at the specific sleep problem of each user, so that the PEMF stimulation adjustment accuracy is improved, and the sleep effect is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of sleep health physiotherapy rehabilitation, in particular to a sleep adaptive adjustment system and device based on PEMF. BACKGROUND

[0002] Pulsed electromagnetic field (PEMF) uses electromagnetic pulses of specific frequency and intensity to act on human cells and tissues through mechanisms such as activating intracellular signal transduction pathways and regulating membrane potential of nerve cells, promoting cell metabolism, regulating the nervous system, and improving blood circulation, which shows significant prospects in treating chronic diseases, relieving pain, improving sleep, and promoting rehabilitation. In particular, for sleep problems caused by physical fatigue, overactive brain, chronic stress, and circadian rhythm disorders, PEMF emits low-frequency pulsed electromagnetic field stimulation, brain wave synchronization, and frequency modulation of brain rhythm from beta wave-alpha wave awake relaxation state to theta wave-delta wave sleep-deep sleep state, which reduces brain tissue metabolism and cortical excitation, reduces stress and anxiety, pacifies the nervous system, reduces the level of the main stress hormone cortisol in the body, and increases the secretion of melatonin, thereby improving sleep quality. However, the precision of sleep adjustment by PEMF stimulation in the prior art is insufficient, resulting in poor sleep improvement effect. SUMMARY

[0003] Therefore, the present application provides a sleep adaptive adjustment system and device based on PEMF, which implements individualized, refined, and differentiated adaptive adjustment for each user's specific sleep problem, thereby improving the precision of PEMF stimulation adjustment and improving the sleep effect.

[0004] The first aspect of the present application provides a sleep adaptive adjustment system based on PEMF, which comprises a sleep model setting module, a sleep real-time monitoring module, a difference analysis module, and a PEMF sleep adjustment module. The sleep model setting module is used to pre-set a plurality of different sleep models, determine model information according to a target user, and then determine a target sleep model for sleep adjustment of the target user through the model information. The sleep real-time monitoring module is used to obtain the sleep state of the target user by real-time monitoring through the pre-set monitoring method of the target user. The difference analysis module is used to generate an analog target sleep state of the target user through the target sleep model when the target user is monitored, and then compare the analog sleep state with the sleep state to obtain the difference information between them. The PEMF sleep regulation module is configured to determine a PEMF stimulation strategy based on the simulated target sleep state, stimulate the target user based on the PEMF stimulation strategy, adjust the PEMF stimulation strategy based on real-time updated difference information during the stimulation, and stimulate the target user based on the adjusted PEMF stimulation strategy until the target user falls asleep.

[0005] Optionally, the pre-set several different sleep models include: a personal sleep model for the target user based on historical sleep monitoring data of the target user; or a general sleep model for all users based on a sleep database containing historical sleep monitoring data of all users; or a corresponding theoretical sleep model based on an authoritative guide of a pre-selected third-party institution.

[0006] Optionally, the model information determined based on the target user includes: the model information is determined based on selection parameters input by the target user, the selection parameters including a model ID, a model name, and a model type; or the model information is determined based on basic information of the target user, the basic information including height, weight, gender, and age; or the model information is determined based on treatment and diagnosis information of the target user.

[0007] Optionally, the system further includes a quality evaluation and maintenance module. The quality evaluation and maintenance module is configured to evaluate the sleep quality of the target user after the target user gets up based on a pre-set sleep scale, and store the sleep state, difference information, and corresponding PEMF stimulation strategy when the sleep quality meets pre-set requirements, to obtain a personalized sleep model database of the target user as a personal sleep model candidate in the future.

[0008] Optionally, the PEMF stimulation strategy includes: an alternating cycle stimulation between wave band frequencies of alpha waves, beta waves, theta waves, and delta waves, and a single type wave stimulation.

[0009] The second aspect of the present application provides a PEMF-based sleep self-adaptive regulation device, the device including: a sleep model setting unit configured to pre-set several different sleep models, determine model information based on a target user, and determine a target sleep model for sleep regulation of the target user based on the model information; A real-time sleep monitoring unit, configured to monitor the target user in real time using a preset monitoring method to obtain the target user's sleep state; a difference analysis unit, configured to generate a simulated target sleep state of the target user using the target sleep model when the target user is undergoing sleep monitoring, and then compare the simulated sleep state with the sleep state to obtain difference information between the two; The PEMF sleep adjustment unit is used to determine a PEMF stimulation strategy based on the simulated target sleep state, then stimulate the target user according to 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 according to the adjusted PEMF stimulation strategy until the target user ends sleep.

[0010] Optionally, the sleep model setting unit pre-sets a number of different sleep models including: Setting a personal sleep model for the target user based on the historical sleep monitoring data of the target user; or, Setting a universal sleep model for all users based on a large sleep database containing historical sleep monitoring data of each user; or The corresponding theoretical sleep model is set based on authoritative guidelines from pre-selected third-party organizations.

[0011] Optionally, the step of determining the model information according to the target user in the sleep model setting unit includes: Determine the model information through selection parameters input by the target user, wherein the selection parameters include model ID, model name, and model type; or Determine the model information based on the basic information of the target user, wherein the basic information includes height, weight, gender, and age; or The model information is determined according to the treatment diagnosis information of the target user.

[0012] Optionally, the device further includes: The quality evaluation and maintenance unit is used to evaluate the sleep quality of the target user using a preset sleep scale after the target user gets up, and when the sleep quality meets the preset requirements, extract the sleep state, difference information and corresponding PEMF stimulation strategy for storage to obtain a personalized sleep model database for the target user as a future personal sleep model alternative.

[0013] Optionally, the PEMF stimulation strategy in the PEMF sleep regulation unit includes: alternating cyclic stimulation of the frequency bands of α waves, β waves, θ waves and δ waves, and maintaining stimulation of a single type of wave.

[0014] In the embodiments provided herein, for a target user who requires sleep adjustment, a sleep model for sleep adjustment is first determined by a sleep model setting module based on the user's historical sleep state or the sleep state of other users. The target user is then 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 difference between the two. The corresponding PEMF stimulation strategy is then determined based on this difference to perform adaptive stimulation, allowing the target user to adjust their sleep according to the sleep model. This allows for personalized, refined, and differentiated adaptive adjustments tailored to each user's specific sleep problems, thereby improving the accuracy of PEMF stimulation adjustments and improving sleep outcomes. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A system module diagram provided for an embodiment of the present application; Figure 2 A schematic diagram of a sleep model cycle provided in an embodiment of the present application; Figure 3 A schematic diagram of a situation where the sleep model deviates from the embodiment of the present application; Figure 4 A frequency band cycle pattern diagram provided in an embodiment of the present application; Figure 5 A frequency band cycle pattern diagram provided in an embodiment of the present application; Figure 6 A frequency band cycle pattern diagram provided in an embodiment of the present application; Figure 7 A frequency band cycle pattern diagram provided in an embodiment of the present application; Figure 8 A frequency band cycle pattern diagram provided in an embodiment of the present application; Figure 9 A frequency band cycle pattern diagram provided in an embodiment of the present application; Figure 10 A frequency band cycle pattern diagram provided in an embodiment of the present application; Figure 11 A frequency band cycle pattern diagram provided in an embodiment of the present application; Figure 12 A frequency band cycle pattern diagram provided in an embodiment of the present application; Figure 13 A frequency band cycle pattern diagram provided in an embodiment of the present application; Figure 14 Another sleep model cycle diagram provided in an embodiment of the present application; Figure 15 A frequency band cycle pattern diagram provided in an embodiment of the present application; Figure 16A frequency band cycle pattern diagram provided in an embodiment of the present application; Figure 17 A frequency band cycle pattern diagram provided in an embodiment of the present application; Figure 18 This is a schematic diagram of the amount of sleep provided in the embodiment of the present application; Figure 19 A diagram of the device structure provided in an embodiment of the present application; Figure 20 A schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0017] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are 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 encompasses any and all possible combinations of one or more of the associated listed items.

[0018] 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 each other. 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 "at the time of" or "when" or "in response to determining".

[0019] The present 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.

[0020] The following specific embodiments are used to describe the technical solution of the present application in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0021] like Figure 1As shown in FIG, a module diagram of a PEMF-based sleep adaptive regulation system provided by the present application is provided. The functions and effects of each module are described below.

[0022] Module 1, sleep model setting module: This module is used to pre-set several different sleep models, determine model information according to the target user, and then determine the target sleep model for sleep adjustment of the target user based on the model information.

[0023] This module can be considered as a module for preparing before going to sleep. The sleep models set in this module are what the user wants to achieve, representing the sleep state-time cycle chart of their satisfactory and high-quality sleep, such as Figure 2 This sleep state-time cycle chart can be developed by medical professionals and experts in the field based on their experience, or through analysis of clinical data. By pre-storing multiple sleep models, any unsatisfactory data in existing sleep models can be modified, allowing the sleep models to be adjusted and updated.

[0024] Before falling asleep, the user selects a target from the pre-stored sleep models based on their different sleep states and the sleep improvement goals they want to achieve, such as increasing the total sleep time, shortening the time it takes to fall asleep, increasing the time it takes to sleep, solving the problem of grogginess after waking up, etc. The sleep monitoring unit is turned on, PEMF is turned on, and the user begins to fall asleep.

[0025] In another embodiment, a number of different sleep models can be pre-set using a variety of different methods, as follows: Method 1: Develop a theoretical sleep model based on authoritative guidelines from pre-selected third-party organizations. For example, the American Academy of Sleep Medicine (AASM) divides a complete sleep cycle (typically lasting 90-120 minutes) into two major 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 (depending on sleep monitoring technology, this can also be divided into two stages: "light sleep" (N1 / N2) and "deep sleep" (N3)), with a gradual transition from light sleep to deep sleep. An example of a target sleep cycle model is set as falling asleep (N1): 1-15 minutes, light sleep (N2): 10-25 minutes, deep sleep (N3): 20-40 minutes, and rapid eye movement (REM): 10-60 minutes. A typical night's sleep cycle involves four to six sleep cycles. As sleep progresses, the time spent in REM sleep increases, while the time spent in N3 decreases.

[0026] Method 2: A high-quality sleep model is set based on a large sleep database. This method requires collecting a large amount of historical sleep monitoring data from different users, and then performing feature extraction, such as first dividing the sleep stages into: awake, light sleep, deep sleep, REM, etc. Then determine the key indicators: time to fall asleep, time to wake up, sleep efficiency, and then use FFT or peak detection to identify the 90-120 minute sleep cycle. Finally, a universal sleep model is constructed with the horizontal axis as time and the vertical axis as the sleep stage. This universal sleep model can set different standards according to different parameters, so that it can be applied to most users. For users who are adjusting their sleep for the first time, since their sleep monitoring data is relatively small, they cannot generate a personal sleep model. They can use this universal sleep model or the theoretical sleep model determined in method 1.

[0027] Method 3: Determine a personalized sleep model based on the target user's historical sleep monitoring data. For users who have repeatedly used this system to adjust their sleep, a large amount of historical sleep monitoring data has been collected, allowing the user to determine their personalized sleep model. The determination method is the same as that used in Method 2 and will not be further described here. Compared to the sleep models set in Methods 1 and 2, the sleep model set in this method is more closely matched to the user, providing greater precision in PEMF stimulation adjustments and ultimately improving sleep outcomes.

[0028] In another embodiment, there are many methods for determining model information according to the target user.

[0029] Exemplarily, the system determines the model through user-selected parameters. The system provides the user with a model selection interface that includes controls such as a drop-down box, icons, and input boxes. The user selects the corresponding model information through these controls, such as entering a model ID, model name, or model type; or clicks an icon to determine the corresponding model ID, model name, model type, etc., thereby determining the corresponding sleep model.

[0030] Exemplarily, the model information is determined based on the target user's basic information. This method requires pre-matching various basic information to corresponding model information. For example, {age 20-22, gender female, height 150-160 cm, weight 90-110 matches model information A}, {age 55-60... model information B}, and so on. Model information A and model information B are then used to match the corresponding sleep model.

[0031] Furthermore, you can also set it for a single basic information, such as different age ranges corresponding to model information A, B, C; different heights and weights corresponding to model information A, B, C, D, etc. Finally, the sleep model that matches the user is determined based on multiple model information.

[0032] For example, the model information can also be determined based on the target user's treatment diagnosis information. This method also requires pre-matching corresponding model information for different treatment diagnosis information. For example, if the treatment diagnosis information determines that the target user has mild snoring, model information A is matched accordingly; if the target user has sleep apnea syndrome, model information B is matched accordingly, and so on. Similarly, if the treatment diagnosis information of the target user matches multiple different model information, the sleep model that matches the user is then determined based on these multiple model information.

[0033] Module 2, real-time sleep monitoring module: This module is used to monitor the target user in real time using a preset monitoring method to obtain the target user's sleep status.

[0034] In this embodiment, the monitoring method can be one or a combination of EEG monitoring, heart rate / ECG monitoring, myoelectric 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, and sleep monitoring mattresses to obtain the target user's sleep state, such as light sleep, deep sleep, REM sleep, etc.

[0035] Module 3, Difference Analysis Module: This module is used to generate a simulated target sleep state of the target user using the target sleep model when the target user is undergoing sleep monitoring, and then compare the simulated sleep state with the sleep state to obtain difference information between the two.

[0036] In this embodiment, the difference information can be used to determine whether the user's sleep state deviates from the simulated target sleep state determined by the model, and if a deviation occurs, adjustments are made according to the deviation direction. 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. Real-time comparison reveals the difference between the two. The PEMF sleep regulation module then adjusts stimulation based on this difference.

[0037] Module 4, PEMF Sleep Adjustment Module. This module is used to determine a 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 the real-time updated difference information during the stimulation process. Stimulation is then continued using the adjusted PEMF stimulation strategy until the target user ends sleep.

[0038] In this embodiment, PEMF is used to emit low-frequency pulsed electromagnetic field stimulation, synchronize brain waves, and modulate the EEG rhythm from the beta-alpha wave-like awake relaxation state to the theta-delta wave-sleep-deep sleep state, thereby reducing brain tissue metabolism and cerebral cortical excitement, reducing stress and anxiety, soothing the nervous system, lowering the level of cortisol, the main stress hormone in the body, and increasing the secretion of melatonin, thereby improving sleep quality.

[0039] The specific process of stimulating the target user through PEMF stimulation strategy is as follows: 1. When the user is falling asleep, the alpha wave (8-13Hz) and the theta wave (4-8Hz) frequency bands are alternately cycled. The alpha wave / theta wave is a pair of N, N = 1 / 2 / 3 adjustable, the mode is as follows Figure 4 shown.

[0040] 2. When the user is in the light sleep stage N1, the frequency of the θ wave (4-8Hz) and the δ wave (0.3-4Hz) bands are alternately cycled. The θ wave / δ wave is a pair of N, N=1 / 2 adjustable, the mode is as follows Figure 5 shown.

[0041] 3. In the light sleep stage N2, the frequency of theta wave (4-8Hz) and delta wave (0.3-4Hz) is alternating, theta wave / delta wave is a pair of N, N = 3 / 4 / 5 adjustable, the pattern is as follows Figure 6 shown.

[0042] 4. In deep sleep stage N3, maintain the frequency of the delta wave (0.3-4Hz), the pattern is as follows Figure 7 shown.

[0043] 5. Rapid eye movement sleep stage REM, maintaining the frequency of theta wave (4-8Hz), the pattern is as follows Figure 8 shown.

[0044] During sleep, if the real-time sleep state monitoring deviates from the target sleep cycle model, adaptive PEMF stimulation adjustment is performed. Different stimulation adjustment strategies are used for different deviation situations.

[0045] by Figure 3 For example, the scenario corresponding to the figure is "natural waking mode". In this mode, there is no limit on the total sleep time and wake-up time. The PEMF stimulation adjustment strategies corresponding to the five offsets in the figure are as follows: Deviation situation ①: Difficulty falling asleep or taking too long to fall asleep. This deviation does not help improve sleep quality and is therefore considered a negative deviation. PEMF regulation adopts a "correction" strategy. The specific adjustment method is to alternate the frequencies of α waves (8-13Hz) and θ waves (4-8Hz). α waves / θ waves are a pair of N, starting with N=1. At regular intervals (such as every 5 minutes), N=2, 3, 4, 5, 6, ..., and automatically increase gradually until falling asleep. The pattern is as follows:Figure 9 shown.

[0046] Deviation situation ②: The sleep is not deep, the deep sleep time is short, and it is easy to fall into the light sleep state from the deep sleep state. This deviation does not help improve the sleep quality, so it is considered a negative deviation. PEMF regulation adopts the strategy of "correction"; the specific adjustment method is to alternate the frequencies of the θ wave (4-8Hz) and the δ wave (0.3-4Hz) bands. The θ wave / δ wave is a pair of N, N starts from 5, and every certain time (such as every 5 minutes), N = 5, 6, 7, 8, 9, 10..., automatically and gradually increases until entering the deep sleep stage again. The pattern is as follows Figure 10 shown.

[0047] Deviation situation ③: It is easy to wake up briefly during sleep. This deviation does not help improve sleep quality and is therefore considered a negative deviation. PEMF regulation adopts a "correction" strategy; the specific adjustment method is to adopt a "sleep-promoting" adjustment strategy, with alpha waves (8-13Hz) and theta waves (4-8Hz) alternating in frequency. There are N pairs of alpha waves / theta waves, but N starts from 8. At regular intervals (such as every 5 minutes), N = 8, 9, 10..., and it automatically increases gradually until you fall asleep again. The pattern is as follows Figure 11 shown.

[0048] Deviation situation ④: The deep sleep time is more than the target model. This deviation helps to increase the deep sleep time and improve the sleep quality. Therefore, it is considered a positive deviation. The PEMF regulation adopts the strategy of "maintaining, not correcting the deviation"; the specific adjustment method is that the PEMF regulation stage does not adopt the target sleep model but adopts real-time monitoring of the sleep state, which is the "deep sleep N3" stage. The PEMF stimulation is also the "deep sleep N3" stage stimulation strategy in Table 1 (that is, "maintaining the delta wave (0.3-4Hz) band frequency"), and the pattern is as follows Figure 12 shown.

[0049] Deviation situation ⑤: Waking up early and unable to fall asleep again. This deviation may lead to insufficient total sleep time and does not help improve sleep quality. Therefore, it is considered a negative deviation. PEMF regulation adopts a "correction" strategy; the specific adjustment method is to adopt an "advance sleep" adjustment strategy, with alpha wave (8-13Hz) and theta wave (4-8Hz) band frequencies alternating in cycles, with alpha wave / theta wave pairs N, but N starts from 5. At regular intervals (such as every 5 minutes), N = 5, 6, 7, 8, 9, 10..., and automatically increases until falling asleep again. The pattern is as follows Figure 13 shown.

[0050] In the other scenario, "Alarm Clock Mode," there's a clear wake-up time limit. Unlike "Natural Wake-Up Mode," which doesn't have a clear wake-up time limit, the controls for falling asleep, light sleep, and deep sleep are identical. Please refer to the corresponding content in Scenario 1 and will not be further elaborated. The main difference between this scenario and "Natural Wake-Up Mode" is the addition of a "wake-up" function: This function gradually applies PEMF stimulation to activate the brain / nerves before the scheduled wake-up time (the "alarm clock" moment), allowing the user to wake smoothly from sleep and avoid being suddenly awakened by the alarm and feeling drowsy or tired.

[0051] Set the wake-up duration ∆t (the system defaults to ∆t = 30 minutes; it can be adjusted from 15 minutes to 60 minutes, which can be set by the user), and the user sets the wake-up alarm time (such as 7 o'clock / 8 o'clock in the morning or any other time set by the user). The PEMF adaptive debugging system starts the PEMF wake-up stimulation adjustment according to the alarm time ∆t in advance, such as Figure 14 shown.

[0052] The "wake-up function" PEMF modulation stimulation strategy is to gradually transition from the theta wave (4-8Hz) of the sleep stage to the alpha wave (8-13Hz) of the relaxation stage, and finally to the low beta wave (13-15Hz) of the wake-up stage. The entire wake-up process is divided into three sub-stages: The first 1 / 3 of the awakening duration: Theta wave (4-8Hz) and alpha wave (8-13Hz) frequency bands alternate and cycle, theta wave / alpha wave pair N, N = 1 start, every certain time (such as every 1 minute), N = 2, 3, 4, 5, 6, ..., automatically increase step by step, the pattern is as follows Figure 15 shown.

[0053] In the middle 1 / 3 of the wake-up duration, the alpha wave (8-13Hz) and low beta wave (13-15Hz) bands alternate in frequency, with N alpha waves / low beta waves in pairs, starting with N=1, and increasing automatically and gradually at regular intervals (such as every 1 minute), N=2, 3, 4, 5, 6, ..., in a pattern such as Figure 16 shown.

[0054] The last 1 / 3 of the wake-up period: Continue to stimulate with low beta wave (13-15 Hz) frequency, the pattern is as follows Figure 17 shown.

[0055] There are many specific scenarios, which will not be described one by one here, but it can be seen from the above embodiments that by adjusting the frequency of the bands between α waves, β waves, θ waves and δ waves for alternating cyclic stimulation, it is possible to stimulate users according to different scenarios, so that the present application can realize the adaptive adjustment of PEMF stimulation methods according to the specific sleep problems of each user, thereby improving the accuracy of PEMF stimulation adjustment and improving sleep effects.

[0056] So far, completed Figure 1 Functional description of each module in .

[0057] In an embodiment of the present application, for a target user requiring sleep adjustment, a sleep model is first determined using the sleep model setting module based on the user's historical sleep state or the sleep state of other users. The target user is then 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 difference between the two. This difference is then used to determine a corresponding PEMF stimulation strategy for adaptive stimulation, allowing the target user to adjust their sleep according to the sleep model. This allows for personalized, refined, and differentiated adaptive adjustments tailored to each user's specific sleep issues, thereby improving the accuracy of PEMF stimulation adjustments and enhancing sleep outcomes.

[0058] In another embodiment, the above system further includes a quality assessment and maintenance module; the module is used to assess the target user's sleep quality after the target user wakes up using a preset sleep scale, such as the Pittsburgh Sleep Quality Index (PSQI), and when the sleep quality meets the preset requirements, extract the sleep state, difference information and corresponding PEMF stimulation strategy for storage to obtain a personalized sleep model database for the target user as a future personal sleep model alternative. Figure 18 shown.

[0059] like Figure 19 As shown, the present application also provides a PEMF-based sleep adaptive regulation device, the device comprising: The sleep model setting unit 1901 is used to pre-set a number of different sleep models, determine model information according to a target user, and then determine a target sleep model for sleep adjustment of the target user based on the model information; The sleep real-time monitoring unit 1902 is configured to monitor the target user in real time using a preset monitoring method to obtain the target user's sleep state; a difference analysis unit 1903 for generating a simulated target sleep state of the target user using the target sleep model when the target user is undergoing sleep monitoring, and then comparing the simulated sleep state with the sleep state to obtain difference information between the two; The PEMF sleep adjustment unit 1904 is used to determine a 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 using the adjusted PEMF stimulation strategy until the target user ends sleep.

[0060] In another embodiment, the sleep model setting unit pre-setting a plurality of different sleep models includes: Setting a personal sleep model for the target user based on the historical sleep monitoring data of the target user; or, Setting a universal sleep model for all users based on a large sleep database containing historical sleep monitoring data of each user; or The corresponding theoretical sleep model is set based on authoritative guidelines from pre-selected third-party organizations.

[0061] In another embodiment, the sleep model setting unit determines the model information according to the target user, including: Determine the model information through selection parameters input by the target user, wherein the selection parameters include model ID, model name, and model type; or Determine the model information based on the basic information of the target user, wherein the basic information includes height, weight, gender, and age; or The model information is determined according to the treatment diagnosis information of the target user.

[0062] In another embodiment, the apparatus further comprises: The quality evaluation and maintenance unit 1905 is used to evaluate the sleep quality of the target user using a preset sleep scale after the target user gets up, and when the sleep quality meets the preset requirements, extract the sleep state, difference information and corresponding PEMF stimulation strategy for storage to obtain a personalized sleep model database for the target user as a future personal sleep model alternative.

[0063] In another embodiment, the PEMF stimulation strategy in the PEMF sleep regulation unit includes: alternating cyclic stimulation of the frequency bands of alpha waves, beta waves, theta waves, and delta waves, and maintaining stimulation of a single type of wave.

[0064] The above embodiments of the present invention provide a PEMF-based adaptive sleep adjustment system, and based on this system, provide a PEMF-based adaptive sleep adjustment device. Through the above system and device, personalized, refined and differentiated adaptive adjustment can be implemented for each user's specific sleep problems, thereby improving the accuracy of PEMF stimulation adjustment and improving sleep effects.

[0065] This embodiment also discloses a computer device, such as Figure 20As shown, the computer device includes a processor and a memory, the memory storing therein at least one instruction, the at least one instruction being loaded and executed by the processor to implement the method on the above-described any one of the PEMF-based sleep adaptive adjustment systems.

[0066] In addition, in the above-described embodiments of the PEMF-based sleep adaptive adjustment device, the logical division of each program module is only illustrative. In actual applications, the above-described functions can be completed by different program modules according to needs, for example, for the configuration requirements of corresponding hardware or the convenience of software implementation. That is, the internal structure of the PEMF-based sleep adaptive adjustment device is divided into different program modules to complete all or part of the above-described functions.

[0067] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of protection of the present application.

Claims

1. A PEMF-based sleep adaptive regulation system, 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 a number of different sleep models, determine model information according to a target user, and then determine a target sleep model for sleep adjustment of the target user based on the model information; The sleep real-time monitoring module is used to monitor the target user in real time using a preset monitoring method to obtain the target user's sleep state; The difference analysis module is used to generate a simulated target sleep state of the target user by using the target sleep model when the target user is undergoing sleep monitoring, and then compare the simulated sleep state with the sleep state to obtain difference information between the two; The PEMF sleep adjustment module is used to determine a PEMF stimulation strategy based on the simulated target sleep state, and then stimulate the target user using the PEMF stimulation strategy. During the stimulation process, the PEMF stimulation strategy is adjusted according to the real-time updated difference information, and then stimulation is performed using the adjusted PEMF stimulation strategy until the target user ends sleep.

2. The system according to claim 1, wherein: The pre-set number of different sleep models include: Setting a personal sleep model for the target user based on the historical sleep monitoring data of the target user; or, Setting a universal sleep model for all users based on a large sleep database containing historical sleep monitoring data of each user; or The corresponding theoretical sleep model is set based on authoritative guidelines from pre-selected third-party organizations.

3. The system according to claim 1, wherein: Determining model information according to the target user includes: Determine the model information through selection parameters input by the target user, wherein the selection parameters include model ID, model name, and model type; or Determine the model information based on the basic information of the target user, wherein the basic information includes height, weight, gender, and age; or The model information is determined according to the treatment diagnosis information of the target user.

4. The system according to claim 1, wherein: The system also includes a quality assessment and maintenance module; The quality assessment and maintenance module is used to assess the target user's sleep quality using a preset sleep scale after the target user gets up, and when the sleep quality meets the preset requirements, extract the sleep state, difference information and corresponding PEMF stimulation strategy for storage to obtain a personalized sleep model database for the target user as a future personal sleep model alternative.

5. The system according to claim 1, wherein: The PEMF stimulation strategy includes: cyclic stimulation of the frequency bands of α waves, β waves, θ waves and δ waves, and maintaining stimulation of a single type of wave.

6. A PEMF-based sleep adaptive regulation device, characterized in that: The device comprises: a sleep model setting unit, configured to pre-set a number of different sleep models, determine model information according to a target user, and then determine a target sleep model for sleep adjustment of the target user based on the model information; A real-time sleep monitoring unit, configured to monitor the target user in real time using a preset monitoring method to obtain the target user's sleep state; a difference analysis unit, configured to generate a simulated target sleep state of the target user using the target sleep model when the target user is undergoing sleep monitoring, and then compare the simulated sleep state with the sleep state to obtain difference information between the two; The PEMF sleep adjustment unit is used to determine a PEMF stimulation strategy based on the simulated target sleep state, then stimulate the target user according to 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 according to the adjusted PEMF stimulation strategy until the target user ends sleep.

7. The device according to claim 6, characterized in that The sleep model setting unit pre-sets a number of different sleep models including: Setting a personal sleep model for the target user based on the historical sleep monitoring data of the target user; or, Setting a universal sleep model for all users based on a large sleep database containing historical sleep monitoring data of each user; or The corresponding theoretical sleep model is set based on authoritative guidelines from pre-selected third-party organizations.

8. The device according to claim 6, characterized in that The sleep model setting unit determines the model information according to the target user, including: Determine the model information through selection parameters input by the target user, wherein the selection parameters include model ID, model name, and model type; or Determine the model information based on the basic information of the target user, wherein the basic information includes height, weight, gender, and age; or The model information is determined according to the treatment diagnosis information of the target user.

9. The device according to claim 6, characterized in that The device further comprises: The quality evaluation and maintenance unit is used to evaluate the sleep quality of the target user using a preset sleep scale after the target user gets up, and when the sleep quality meets the preset requirements, extract the sleep state, difference information and corresponding PEMF stimulation strategy for storage to obtain a personalized sleep model database for the target user as a future personal sleep model alternative.

10. The device according to claim 6, characterized in that The PEMF stimulation strategy in the PEMF sleep regulation unit includes: alternating cyclic stimulation of the frequency bands of α waves, β waves, θ waves and δ waves, and maintaining stimulation of a single type of wave.

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