CN118001546A - Method and device for dynamically intervening sleep, sleep assisting equipment and storage medium - Google Patents

Method and device for dynamically intervening sleep, sleep assisting equipment and storage medium Download PDF

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Publication number
CN118001546A
CN118001546A CN202311867236.0A CN202311867236A CN118001546A CN 118001546 A CN118001546 A CN 118001546A CN 202311867236 A CN202311867236 A CN 202311867236A CN 118001546 A CN118001546 A CN 118001546A
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sleep
user
physiological data
intervention
acquiring
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张勇
魏育林
陆利达
解维东
陆勤
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Shenzhen Tigan Yinyue Technology Co ltd
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Shenzhen Tigan Yinyue Technology Co ltd
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
    • A61M21/02—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis for inducing sleep or relaxation, e.g. by direct nerve stimulation, hypnosis, analgesia
    • A—HUMAN NECESSITIES
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    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A—HUMAN NECESSITIES
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    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A—HUMAN NECESSITIES
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    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02405—Determining heart rate variability
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/372—Analysis of electroencephalograms
    • A61B5/374—Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
    • A61M2021/0005—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus
    • A61M2021/0022—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus by the tactile sense, e.g. vibrations
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
    • A61M2021/0005—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus
    • A61M2021/0027—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus by the hearing sense
    • A—HUMAN NECESSITIES
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    • A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
    • A61M2021/0005—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus
    • A61M2021/0044—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus by the sight sense

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Abstract

The invention relates to the technical field of sleep dynamic management, in particular to a method and a device for dynamically intervening sleep, sleep assisting equipment and a storage medium. The method for dynamically intervening sleep comprises the following steps: acquiring a sleep habit portrait of a user, inputting the sleep habit portrait into a sleep intervention model, acquiring physiological data of the user, acquiring a sleep management strategy corresponding to the physiological data based on the sleep intervention model, and executing sleep intervention menu output operation according to the sleep management strategy; and dynamically acquiring physiological data and/or user feedback information of the user, and adjusting a sleep management strategy and a sleep intervention model according to the physiological data and/or the user feedback information to form closed-loop dynamic sleep management operation. The invention can realize personalized dynamic sleep management and provide more effective sleep support.

Description

Method and device for dynamically intervening sleep, sleep assisting equipment and storage medium
Technical Field
The invention relates to the technical field of sleep management, in particular to a method and a device for dynamically intervening sleep, sleep assisting equipment and a storage medium.
Background
With the general acceleration of the life rhythm of people, the sleep deficiency becomes a common phenomenon of urban people at present, and the harm is very serious. Products with sleep-aiding function are more and more needed.
Current products with sleep-aiding functions generally use devices or applications to collect physiological data of a user, such as brain waves, heart rate, respiratory rate, etc. These data may provide information about the quality of sleep and sleep stages of the user. However, the current sleep-aiding technology mainly presents the data to the user for viewing and analysis, but does not fully utilize the application of the data which is strongly related to the sleep of the user, and cannot really achieve the sleep-aiding effect for the user.
In addition, some products with sleep-aiding functions employ intervention measures, such as playing sleep-aiding music or adjusting ambient light, etc., to help the user fall asleep. However, these interventions are typically unidirectional instruction-type, lacking monitoring and adjustment of the user's real-time physiological data. That is, these interventions are not personalized in terms of optimization and intervention according to the actual situation of the user, and the sleep-aiding effect is poor.
Disclosure of Invention
The technical problem to be solved by the embodiment of the invention is to provide a method for dynamically intervening sleep so as to solve the problem of poor sleep aiding effect in the prior art.
The invention discloses a method for dynamically intervening sleep, which comprises the following steps:
Acquiring a sleep habit portrait of a user, inputting the sleep habit portrait into a sleep intervention model, acquiring physiological data of the user, corresponding to a sleep management strategy based on the physiological data of the sleep intervention model, and executing sleep intervention menu output operation according to the sleep management strategy;
Dynamically collecting the physiological data and/or user feedback information of the user, and adjusting the sleep intervention model according to the physiological data and/or the user feedback information to form a personalized intervention closed-loop management operation.
Optionally, the step of executing the sleep intervention menu output according to the sleep management policy includes:
When the current state is a sleep preparation state according to the physiological data, encouraging a user through voice, acquiring induction same-frequency resonance information output by the sleep intervention model based on the physiological data and/or the sleep habit portrait, playing a sleep-aiding audio file according to the induction same-frequency resonance information, and simultaneously carrying out harmonic vibration on the user by converting sound waves corresponding to the audio file into mechanical vibration waves;
and prompting the user to prepare for falling asleep through voice when the current state of the user is the state to be asleep according to the physiological data.
Optionally, the step of executing the sleep intervention menu output according to the sleep management policy includes:
and when the current state is the deep sleep state according to the physiological data, adjusting the working state to be the silence state.
Optionally, the step of adjusting the sleep management strategy according to the physiological data and/or user feedback information includes:
Acquiring the physiological data and the sleep management policy of a user during a period from the sleep preparation state to the deep sleep state;
and acquiring association information between the physiological data and the sleep management strategy, and adjusting parameters of the sleep intervention model based on the association information.
Optionally, the step of adjusting the sleep intervention model according to the physiological data and/or user feedback information to form a personalized intervention closed-loop management operation includes:
If the physiological data and/or the user feedback information are good, continuing to execute the current sleep management strategy;
if the physiological data and/or the user feedback information do not meet the preset standard, the operation of the current sleep intervention menu output is adjusted so that the physiological data and/or the user feedback information are improved gradually;
acquiring an operation of adjusting sleep intervention menu output for improving the physiological data and/or the user feedback information, and correcting the sleep intervention model based on the operation of adjusting sleep intervention menu output.
Optionally, the operation of executing sleep intervention menu output according to the sleep management policy includes:
and when the current sleep state of the user is the shallow sleep state according to the physiological data, the user gradually enters a deeper sleep state by executing the induced same-frequency resonance operation corresponding to the induced same-frequency resonance information.
Optionally, the sleep intervention model is a gradient lifting tree model and comprises a decision tree, a random forest and XGBoots, lightGBM weak learners,
The decision tree outputs voice prompts or the content of the audio files and the intensity of vibration, the random forest outputs the selection of the audio file playing, XGBoost and LightGBM can output specific sleep intervention menus.
The invention also discloses a device for dynamically intervening sleep, which comprises:
The sleep aiding module is used for acquiring sleep habit portraits of the user, inputting the sleep habit portraits into a sleep intervention model, acquiring physiological data of the user, corresponding to a sleep management strategy based on the physiological data of the sleep intervention model, and executing sleep intervention menu output operation according to the sleep management strategy;
the closed loop module is used for dynamically collecting the physiological data and/or user feedback information of the user, and adjusting the sleep intervention model according to the physiological data and/or the user feedback information so as to form individual intervention closed loop management operation.
The invention also discloses a sleep-aiding device comprising a memory and a processor, wherein the memory stores a computer program which, when executed by the processor, causes the processor to perform the steps as described above.
The invention also discloses a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps as described above.
Compared with the prior art, the method for dynamically intervening sleep provided by the embodiment of the invention has the beneficial effects that: the physiological data and/or user feedback information of the user are dynamically collected, the sleep intervention model is adjusted according to the physiological data and/or user feedback information, the effect of a sleep intervention menu is continuously evaluated, and self-learning adjustment is carried out on the sleep intervention model according to an evaluation result, so that personalized sleep assistance can be realized, more effective sleep assistance support is provided, and the closed-loop dynamic sleep management operation can help the user to obtain better sleep experience and sleep quality.
Drawings
The invention will now be described in further detail with reference to the drawings and examples, in which:
FIG. 1 is a flow chart of an embodiment of a method for dynamically intervening sleep provided by the present invention;
FIG. 2 is a schematic diagram of an embodiment of a device for dynamically intervening sleep according to the present invention;
figure 3 is an internal block diagram of a sleep aid device in one embodiment.
The reference numerals in the drawings are as follows:
10. A device for dynamically intervening sleep; 11. a sleep aiding module; 12. a closed loop module.
Detailed Description
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. Preferred embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for dynamically intervening sleep according to an embodiment of the present invention. The method for dynamically intervening sleep is applied to sleep-aiding equipment, wherein the sleep-aiding equipment can be devices such as a mattress, a deck chair, a sofa, a pillow, a bed, a blanket and the like, and comprises the following steps of:
s101: and acquiring a sleep habit portrait of the user, inputting the sleep habit portrait into a sleep intervention model, acquiring physiological data of the user, corresponding to a sleep management strategy based on the physiological data of the sleep intervention model, and executing sleep intervention menu output according to the sleep management strategy.
In a specific implementation scenario, a sleep habit portrait of a user is obtained, corresponding information can be filled in by the user in a questionnaire manner, and the sleep habit portrait is generated according to the information provided by the user. The sleep related data of the user can be collected, including sleep time, wake-up time, sleep duration, sleep quality assessment, sleep stage and the like, and the sleeping habit of the user can be also included, including habits in aspects of light, sound, diet, time and the like. In addition, other physiological data such as pulse condition, HRV (HEART RATE Variability ), brain waves, heart rate, respiratory rate, etc. can also be collected. Data analysis techniques are used to explore the sleep patterns, habits, and trends of the user based on the sleep related data. Based on the results of the data analysis, construction of a representation of the user's sleep habits may begin.
The representation of the user's sleep habits is provided as input to a sleep intervention model. The sleep intervention model may be trained based on a representation of the user's sleep habits. During training, the sleep intervention model learns how to predict sleep states, analyze sleep quality and provide corresponding sleep management strategies based on physiological data of the user (provided by sleep habit portraits or later acquired through real-time acquisition). Parameter adjustment and optimization may be required during the training process to improve the performance and accuracy of the model.
In one implementation scenario, the physiological data includes: at least one of pulse condition, heart Rate Variability (HRV), brain waves, heart rate, and respiratory rate. Heart rate variability refers to the variation in the interval between heart beats. The pulse condition can reflect the balance of yin and yang, qi and blood. When yin and yang of human body are unbalanced, sleep quality is affected, and the relationship between the human body and sleep is very strong. HRV may reflect the activity level of the autonomic nervous system, including the balance of sympathetic and parasympathetic nerves. In sleep monitoring, HRV may be used to evaluate the autonomic neuromodulation status of a user, thereby knowing the user's degree of relaxation and sleep quality. Brain waves are electrical signals of brain neuron activity. Information about the sleep state and sleep stage of the user can be acquired by monitoring brain waves through electroencephalography (EEG). Brain waves can be divided into bands of different frequencies, such as delta, theta, alpha, beta, etc., each band corresponding to a different sleep state and brain activity level. Heart rate refers to the number of beats per minute of the heart. In sleep monitoring, heart rate may be used to assess a user's mental state and sleep quality. Heart rate generally decreases gradually while falling asleep, and there is a tendency for fluctuations to increase during the dream phase (rapid eye movement sleep stage). The respiratory rate refers to the number of breaths per minute. By monitoring the breathing frequency, the breathing state and breathing regularity of the user can be known.
Pulse condition, heart rate variability, wrist band which can be installed on flexible material through sensor, etc. can acquire, brain wave can acquire through electroencephalogram (EEG) instrument, and electroencephalogram instrument uses electrode to place on the scalp, measures and records the signal of telecommunication that brain activity produced. The breathing frequency may be acquired by vibration or a laser sensor.
S102: and executing sleep intervention menu output operation according to the sleep management strategy.
In one particular implementation scenario, the operation of the sleep intervention menu output includes a voice prompt or audio file and a harmonic vibration. The user may be provided with instructions for relaxation, breathing exercises or meditation, etc. by voice prompts. The voice prompts may include mild sounds, relaxed audio files, or meditation guides that help the user to gradually relieve stress, relax mind and body, and gradually go to sleep.
The sleep intervention menu output operation is generated according to the sleep habit portrait of the user, so that the personalized requirements of the user can be met.
S103: and dynamically acquiring the physiological data and/or user feedback information of the user, and adjusting the sleep management strategy and the sleep intervention model according to the physiological data and/or the user feedback information to form closed-loop dynamic sleep management operation.
In a specific implementation scenario, the physiological data and/or user feedback information of the user are dynamically collected, an effect of the sleep intervention menu output operation can be obtained according to the collected physiological data and/or user feedback information, and the sleep management strategy is adjusted according to whether the sleep assisting effect reaches the expected level. By comparing the data of the different time periods, the influence of the operation of the sleep intervention menu output on the physiological index can be evaluated. For example, if the operation of the sleep intervention menu output is successful, a change in the heart rate trend, the spectral characteristics of brain waves, or the like, which is related to the improvement of sleep quality, may be observed. The user may provide feedback regarding the time to sleep, quality of sleep, depth of sleep, sleep disruption, etc. By comparing the user feedback before and after, it is possible to know whether the operation of the sleep intervention menu output has a positive effect on the sleep of the user.
If the effect of the sleep intervention menu output operation is expected, i.e., both physiological data and user feedback show a trend toward improvement, then the current sleep management strategy may continue to be maintained. However, if the effect of the operation of the sleep intervention output menu is not as expected, i.e., the physiological data and user feedback are not significantly improved, then adjustments to the operation of the sleep intervention output menu are required. The manner in which the operation of the sleep intervention menu output is adjusted may be dependent on the particular situation, such as changing the intensity, duration, frequency, or manner in which the operation of the sleep intervention menu output is performed. According to the physiological data and feedback information of the user, the sleep management strategy can be adjusted in a personalized way so as to better meet the requirements of the user and improve the sleep effect.
In the implementation scene, a new sleep management strategy is acquired based on the operation effect of the modified sleep intervention menu output, the sleep intervention model can be modified based on the new sleep management strategy, and after the operation of the sleep intervention menu output is adjusted, the adjustment can be applied to the sleep intervention model to update the parameters and the hypothesis of the model, so that the sleep feature of a user can be better adapted. Modifying the sleep intervention model may include adjusting parameters of the model, updating assumptions of the model, or adding new features. The goal of the correction is to make the model more accurate to predict the sleep state and needs of the user. And verifying the new sleep data by using the corrected sleep intervention model. And (3) evaluating the accuracy and reliability of the corrected model in predicting the sleep state and effect through comparison with the actual data.
In one implementation scenario, the sleep intervention model is a gradient lift tree (Gradient Boosting Tree) model that includes multiple weak learners of different types that can be used to obtain diversified predictive capabilities, thereby improving overall model performance. Different weak learners may have different bias and variance characteristics, so they may exhibit optimal performance over different data subsets or feature subspaces.
For example, the weak learners are decision trees, random forests, XGBoots, lightGBM, respectively. After receiving the physiological data, feature information is extracted from the physiological data, for example, frequency domain features or time domain features may be extracted from the pulse condition, frequency domain features or time domain features may be extracted from the heart rate variability data, frequency spectrum features may be extracted from the brain wave data, and statistical features may be extracted from the heart rate and respiratory frequency data. The extracted features are used as input and are respectively input into weak learners such as decision trees, random forests, XGBoost, lightGBM and the like. Each weak learner learns and predicts based on the input features. Based on the design and training of the model, these weak learners will output corresponding sleep management strategies. For example, the decision tree may output the content of the voice prompt or the intensity of the vibration, the random forest may output a selection of music play, XGBoost and LightGBM may output the operational advice output by a specific sleep intervention menu. And integrating sleep management strategies obtained from the weak learners. The outputs of the different weak learners can be integrated according to a certain rule or weight to obtain a final sleep management strategy.
In one implementation scenario, the coefficient of the key of the decision tree is:
Where w i is the weight of sample class i, p i is the relative frequency of sample class i in the node, k represents the number of classes, and j represents the index of the class. This custom coefficient of kunity introduces a sample weight consideration into the calculation process, as compared to the conventional kunity coefficient. The conventional kunit only considers the relative frequencies of the categories in the nodes, and does not consider the importance differences of the samples. In the custom-made coefficient, we multiply the relative frequency of each class by the weight value of the corresponding sample and divide it by the sum of the weights of all samples. The coefficient of the kunit can thus be adjusted according to the importance of the sample.
In a specific implementation scenario, a sleeping preparation posture of the user may be preset, a current posture of the user is detected through a sensor, and if the current posture is the sleeping preparation posture, the method is started. When physiological data of the user is acquired, and the user is in an accurate state of falling asleep, namely the user is in a sleeping preparation posture based on the physiological data, but not falling asleep, instructions of relaxation and falling asleep are transmitted to the user through voice prompt or encouragement. This may include mild speech, meditation guidance, or relaxation skills to help the user relax mood and prepare to fall asleep. And according to the sleep intervention model, the same-frequency resonance induction information is output based on the physiological data and the sleep habit portrait, so that the effect of same-frequency resonance induction is realized. Inducing on-channel resonance is a technique whereby the physiological system of the human body is kept synchronized or resonated with a specific frequency by interacting with an external stimulus of that frequency. This technique is based on the principle of resonance, i.e. when the external stimulus is matched to the physiological vibration frequency inside the human body, an enhancing effect is produced. In this implementation scenario, inducing on-channel resonance may be used to adjust sleep states. By using specific frequencies of sound, light or other stimuli, synchronization with the brain waves or other physiological vibration frequencies of the human body is performed to promote a specific sleep state.
For example, if a person is in a light sleep state, the method of inducing on-channel resonance may use a specific frequency of sound, vibration or light stimulus, and keep synchronization with the brain wave frequency of the person to help the person gradually enter a deeper sleep state. This resonance phenomenon can produce a better sleep effect by stimulating and adjusting the physiological vibration frequency of the human body.
Music suitable for aiding sleep can be selected for playing. The sleep-aiding music generally adopts slow, soft and comfortable melodies, and is helpful for relaxing mind and body and promoting sleep. The sound wave corresponding to the selected sleep-aiding music is converted into mechanical vibration wave, and the user is subjected to harmonic vibration through a mattress, a pillow or other carriers. The harmonic rhythm vibration can provide comfortable physical stimulus, promote deep relaxation of muscles, tendons and nerves, further help a user to gradually enter a sleep state, and obtain a better sleep effect.
In one implementation scenario, a low-frequency signal of 15 Hz-150 Hz in the audio file may be extracted, amplified and converted into a corresponding vibration waveform, and the vibration assembly driven to vibrate according to the vibration waveform. The low frequency vibrations may help relax muscles, relieve mental stress and pressure, promote relaxation and relaxation of the body. Through converting the sound waves of the audio file into mechanical vibration waveforms, the perception of the body is enhanced, the user is more focused on the perception of the body to promote the relaxation effect, and the sleeping quality is improved.
The purpose of converting sound waves into mechanical vibration waves is to transmit harmonious tones and rhythms to the body of a user in a physical vibration mode, so that the user is helped to relax the mind and body and promote to fall asleep. Such stimulation of harmonic vibrations may be accomplished by a mattress, chair or other similar device that may generate mechanical vibration waves that match the audio file to provide a physical comfort and relaxation effect.
In other implementations, the sleep intervention model receives physiological data of the user, analyzes the physical characteristic data and generates corresponding environmental control information to provide an environment more suitable for the user to fall asleep. At least one factor in the environment may be controlled based on the output environmental control information of the sleep intervention model. For example, air conditioning or heating equipment may be adjusted to provide a comfortable temperature. The temperature may be adjusted to a range more suitable for falling asleep, as suggested by the sleep intervention model. The ambient humidity may be regulated by a humidifier or dehumidifier. Humidity may be adjusted to a level more appropriate for the user to fall asleep, as suggested by the sleep intervention model. The brightness of the light can be controlled or the window curtain, the window shade and the like can be used for adjusting the brightness of the indoor light. The light brightness may be adjusted to a level more suitable for falling asleep according to the advice of the sleep intervention model. Since the environmental control information is generated from the physical characteristic data of the user, the environmental control information is personalized to meet the needs and preferences of each user.
In one specific implementation scenario, the operating state is adjusted to a silence state when it is determined from the collected physiological data that the user has been in a deep sleep state. This is because deep sleep is one of the most important stages in the sleep cycle, and is very important for recovery and health of the body and brain. The audio file is not played and harmonic rhythm vibration is not performed, so that any stimulus possibly disturbing deep sleep can be avoided, the user can fully enjoy the benefit of deep sleep, and a quiet and comfortable environment is provided for the user so as to support the deep sleep.
In one implementation scenario, when the user is detected to be in a deep sleep state, the sleep management strategy and the physiological data of the user from the state to be asleep to the state to be asleep are obtained, information association between the physiological data and the induced on-channel resonance information is obtained, and parameters of the sleep intervention model are adjusted based on the association information. The relationship between physiological data and sleep management strategies is studied using appropriate statistical analysis methods (e.g., correlation analysis, regression analysis, factor analysis, etc.). And adjusting parameters of the sleep intervention model according to the result of the relation analysis. This may include weights, biases, learning rates, etc. of the model. Optimization algorithms (e.g., gradient descent, genetic algorithms, etc.) can be used to automatically adjust parameters to maximize model performance and prediction accuracy.
That is, in the invention, except for correcting the sleep intervention model according to the collected physiological data in each period, the sleep intervention model is corrected according to all the physiological data collected by the user in each sleep, so that a closed loop with one smaller part and two larger parts is formed. Dynamic correction is to correct model parameters according to the change trend of physiological data in one period so as to adapt to sleeping habits and changes of users. Such modifications may help the model better predict the current sleep state of the user and provide the corresponding sleep intervention menu output operations. Through all physiological data acquired every time of sleep, more detailed and comprehensive sleep information can be acquired, and more subtle patterns and relations can be found by correlating and analyzing the data with a sleep intervention model. Incremental learning techniques may be used to add data for each sleep to the model and update model parameters based on the new data. Thus, the sleep intervention model can gradually learn individual differences and changes of the user, so that more accurate and personalized prediction and suggestion are provided, the accuracy and adaptability of the sleep intervention model can be improved in subsequent application, and more personalized and effective sleep-aiding suggestion is provided for the user.
And prompting the user to prepare for falling asleep through voice when the current state of the user is the state to be asleep according to the physiological data. For example, it is detected that the user is lying in the bed, but is not in a ready-to-fall-sleep posture, and if the current time is already a time at which the user can fall to sleep, the user is prompted by voice to make ready to fall to sleep. For example, the user is prompted to adjust to a sleep preparation posture and guided to perform deep breathing exercises to relax the mind and body by slow and deep breathing.
As can be seen from the above description, in this embodiment, the physiological data and/or the user feedback information of the user are dynamically collected, the sleep intervention model is adjusted according to the physiological data and/or the user feedback information, and the sleep intervention model is adjusted according to the evaluation result by continuously evaluating the effect of the operation output by the sleep intervention menu, so that personalized sleep assistance can be realized and more effective sleep support is provided. Such closed loop dynamic sleep management operations may help the user to obtain a better sleep experience and sleep quality.
Referring to fig. 2, fig. 2 is a schematic structural diagram of an apparatus for dynamically intervening sleep according to an embodiment of the present invention. The device 10 for dynamically intervening sleep comprises a sleep aiding module 11 and a closed loop module 12.
The sleep aiding module 11 is used for acquiring sleep habit portraits of users, inputting the sleep habit portraits into a sleep intervention model, acquiring physiological data of the users, corresponding to a sleep management strategy based on the physiological data of the sleep intervention model, and executing sleep intervention menu output operation according to the sleep management strategy; the closed loop module 12 is configured to dynamically collect the physiological data and/or user feedback information of the user, and adjust the sleep intervention model according to the physiological data and/or user feedback information to form a closed loop dynamic sleep management operation.
The sleep aiding module 11 is configured to encourage the user by voice when the current state is the sleep preparation state according to the physiological data, obtain the induced same-frequency resonance information output by the sleep intervention model based on the physiological data and/or the sleep habit portrait, play a sleep aiding audio file according to the induced same-frequency resonance information, and simultaneously perform physical and psychological resonance on the user by converting sound waves corresponding to the audio file into mechanical vibration waves.
The sleep aiding module 11 is configured to adjust a working state to a silence state when the current state is a deep sleep state according to the physiological data.
A closed loop module 12 for acquiring the physiological data and the sleep management strategy of the user during the period from the to-be-asleep state to the deep sleep state; and acquiring relation information between the physiological data and the sleep management strategy, and adjusting parameters of the sleep intervention model based on the relation information.
The sleep aiding module 11 is used for extracting low-frequency signals of 15 Hz-150 Hz in the audio file, converting the low-frequency signals into corresponding mechanical vibration waveforms, and driving the vibration assembly to carry out physical and psychological resonance according to the vibration waveforms.
The sleep aiding module 11 is used for prompting the user to prepare for falling asleep through voice when the current state of the user is the state to be asleep according to the physiological data.
Figure 3 illustrates an internal block diagram of a sleep aid device in one embodiment. The sleep-aiding device can be a terminal or a server. As shown in fig. 3, the sleep aid device includes a processor, memory, and a network interface connected by a system bus. The memory includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium of the sleep aid device stores an operating system and may also store a computer program which, when executed by a processor, causes the processor to implement a method of dynamically intervening in sleep. The internal memory may also have stored therein a computer program which, when executed by the processor, causes the processor to perform the age identification method. It will be appreciated by persons skilled in the art that the structure shown in fig. 3 is merely a block diagram of a portion of the structure associated with the present inventive arrangements and is not limiting of the sleep aid device to which the present inventive arrangements are applied, and that a particular sleep aid device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a sleep aid device is provided comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps as described above.
In one embodiment, a computer-readable storage medium is provided, storing a computer program which, when executed by a processor, causes the processor to perform the steps as described above. The mobile hard disk or other tools capable of reading, writing and storing such as a USB flash disk and an optical disk can also be a server and the like.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile 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 a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
It should be understood that the foregoing embodiments are merely illustrative of the technical solutions of the present invention, and not limiting thereof, and that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art; all such modifications and substitutions are intended to be included within the scope of this disclosure as defined in the following claims.

Claims (10)

1. A method of dynamically intervening in sleep, comprising:
Acquiring a sleep habit portrait of a user, inputting the sleep habit portrait into a sleep intervention model, acquiring physiological data of the user, and executing sleep intervention menu output operation according to the sleep management strategy based on the sleep intervention model and a sleep management strategy corresponding to the physiological data;
and dynamically acquiring the physiological data and/or user feedback information of the user, and adjusting the sleep management strategy and the sleep intervention model according to the physiological data and/or the user feedback information to form a personalized intervention closed-loop management operation.
2. The method of dynamically intervening in sleep of claim 1, wherein the step of performing a sleep intervention menu output operation in accordance with the sleep management policy comprises:
When the current state is a sleep preparation state according to the physiological data, encouraging a user through voice, acquiring induction same-frequency resonance information output by the sleep intervention model based on the physiological data and/or the sleep habit portrait, playing a sleep-aiding audio file according to the induction same-frequency resonance information, and simultaneously converting sound waves corresponding to the audio file into mechanical vibration waves to perform harmonic vibration on the body and mind of the user;
and prompting the user to prepare for falling asleep through voice when the current state of the user is the state to be asleep according to the physiological data.
3. The method of dynamically intervening in sleep of claim 2, wherein the step of performing the operation of sleep intervention menu output in accordance with the sleep management policy comprises:
and when the current state is the deep sleep state according to the physiological data, adjusting the working state to be a silent state, and not playing the audio file or performing harmonic vibration.
4. A method of dynamically intervening in sleep as claimed in claim 3, characterized in that, the step of adapting the sleep management strategy in accordance with the physiological data and/or user feedback information, comprises:
Acquiring the physiological data and the sleep management policy of a user during a period from the sleep preparation state to the deep sleep state;
and acquiring association information between the physiological data and the sleep management strategy, and adjusting parameters of the sleep intervention model based on the association information.
5. The method of dynamically intervening sleep of claim 2, wherein the step of adjusting the sleep intervention model based on the physiological data and/or user feedback information to form a personalized intervention closed-loop management operation comprises:
If the physiological data and/or the user feedback information are good, continuing to execute the current sleep management strategy;
if the physiological data and/or the user feedback information do not meet the preset standard, the operation of the current sleep intervention menu output is adjusted so that the physiological data and/or the user feedback information are improved gradually;
acquiring a sleep adjustment intervention menu for improving the physiological data and/or the user feedback information, and correcting the sleep intervention model based on the sleep adjustment intervention menu.
6. The method of dynamically intervening in sleep of claim 2, wherein said executing a sleep intervention menu output operation in accordance with said sleep management policy comprises:
and when the current sleep state of the user is a shallow sleep state according to the physiological data, the user enters a deeper sleep state by executing the induced same-frequency resonance operation corresponding to the induced same-frequency resonance information.
7. The method of dynamically intervening sleep as claimed in any one of claims 1-6, characterized in that the sleep intervention model is a gradient-lifting tree model, comprising decision tree, random forest, XGBoots, lightGBM weak learners,
The decision tree outputs voice prompts or the content and vibration intensity of the audio file, the random forest outputs the selection of the audio file playing, XGBoost and LightGBM output specific sleep intervention menus.
8. A device for dynamically intervening in sleep, comprising:
The sleep aiding module is used for acquiring sleep habit portraits of the user, inputting the sleep habit portraits into a sleep intervention model, acquiring physiological data of the user, corresponding to a sleep management strategy based on the physiological data of the sleep intervention model, and executing sleep intervention menu output operation according to the sleep management strategy;
the closed loop module is used for dynamically collecting the physiological data and/or user feedback information of the user, and adjusting the sleep intervention model according to the physiological data and/or the user feedback information so as to form individual intervention closed loop management operation.
9. A computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the method of any one of claims 1 to 7.
10. A sleep aid device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method of any one of claims 1 to 7.
CN202311867236.0A 2023-12-29 2023-12-29 Method and device for dynamically intervening sleep, sleep assisting equipment and storage medium Pending CN118001546A (en)

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