1069 results about "Sleep state" patented technology

A computer system for persistent cognitive neural architecture implementing sophisticated state preservation and sleep-state optimization capabilities. The system operates a layered neural network monitored by a hierarchical supervisory system that collects activation data, identifies operation patterns, and implements architectural changes. A meta-supervisory system tracks behavior patterns and extracts generalizable principles. A cognitive neural orchestrator manages operational states and coordinates decision-making across the network. The system maintains persistent neural network state through mechanisms that store and retrieve neural activation patterns and architectural configurations across operational sessions. During designated sleep states, the system executes optimization operations including memory consolidation and insight generation. This innovative architecture enables neural networks to maintain knowledge continuity across system restarts while implementing sophisticated optimization during periods of reduced demand, enhancing long-term performance through persistent cognitive capabilities.

The invention discloses a sleep state real-time monitoring method and system, and belongs to the technical field of sleep monitoring, and the method specifically comprises the steps: collecting a heart rate variability signal, an electroencephalogram signal and body movement data of a user in real time through a non-invasive sleep pad integrating a piezoelectric sensor, a flexible dry electrode and a pressure sensor; based on the physiological data, whether the user reaches an autonomous sleep state or not is judged through a first algorithm model; if not, starting an active intervention program for playing the adjustable music, and dynamically adjusting the music volume, the playing speed or the track in combination with the physiological data feedback until the user enters an autonomous sleep state; after the user falls asleep, physiological data are continuously collected, and a sober period, a light sleep period, a deep sleep period and a rapid eye movement period are divided through a second algorithm model; if the staging result is a waking period, the autonomous sleep state judgment is executed again; according to the invention, non-intrusive monitoring and personalized intervention are combined, and the monitoring comfort and accuracy are improved.

The invention discloses a self-adaptive equipment dormancy method based on a Wi-Fi signal state, and the method comprises the following steps: 1, based on the continuous monitoring of a physical layer index of associated equipment, sending a suggested wake-up instruction to the corresponding associated equipment when it is predicted that the signal quality is rapidly reduced but does not fall off a switching threshold; step 2, according to a received suggested wake-up instruction or a signal degradation trend detected by the corresponding associated device, specifically, switching from a deep sleep state to a standby wake-up state, shortening a sleep period and pre-loading authentication information; 3, the wireless access point dynamically adjusts the sending opportunity of the suggested wake-up instruction according to the service delay requirement of the associated equipment; 4, the associated device dynamically elects a main link and a slave link based on a multi-attribute decision algorithm, and the main link is responsible for receiving a wireless access point instruction and uniformly scheduling dormancy and awakening of the slave link; and 5, generating a prediction result of future data traffic, and controlling the on-demand wake-up time of the slave link by the main link.

The invention relates to the technical field of physiological signal processing and sleep monitoring, in particular to a sleep staging method and a sleep staging system for collecting multiple physiological signals, and the sleep staging method and the sleep staging system for collecting the multiple physiological signals synchronously collect auditory meatus photoelectric volume pulse waves, temperature and head micro-motion signals through an in-ear sensor array. According to the method, the signal quality index is calculated, time domain, frequency domain and nonlinear features are extracted, a dynamic weighted fusion mechanism is adopted, feature weights are adjusted according to the signal quality index, a hierarchical depth time sequence learning model is input for sleep staging, and the sleep staging accuracy is improved to 89% or above and is improved by 15-20% compared with a single brain wave method. A sleep state evaluation report and a personalized feedback intervention strategy generated by the system are beneficial for improving sleep quality, a dynamic weighted fusion mechanism enhances system robustness, adapts to different signal qualities and ensures stable performance, and the invention provides an efficient and accurate new method for the field of sleep monitoring.

The invention discloses a sleep staging method and system. The method comprises the steps that physiological feature original signals of a target object are obtained; performing signal state detection on the physiological feature original signal; if the user leaves the bed or the signal is invalid, performing data cleaning on the sleep staging data of the day to obtain a final sleep staging result, and if the user is in the bed state and the signal is valid, performing preprocessing operation on the physiological feature original signal, and performing separation to obtain at least two physiological parameter signals related to the sleep state; performing multi-dimensional feature extraction on the physiological parameter signals to construct a target physiological feature array; based on the target physiological feature array, the target object information array and the sleep state information array, a sleep staging result corresponding to the current moment is obtained through a preset sleep staging model. According to the invention, non-inductive home monitoring can be realized by relying on non-intrusive equipment such as an intelligent mattress and an intelligent pillow in an intelligent home scene, and the signal anti-interference capability and the sleep staging accuracy are effectively improved.

The invention relates to an acousto-optic coupled intelligent sleep aiding method and system. The method comprises the following steps: constructing an initial sound scene environment and controlling sound and light output to smoothly transit to a reference value; collecting a user respiration signal to obtain a respiration rhythm; adjusting the sound and light output rhythm to be synchronous with the respiratory rhythm; coupling the acousto-optic output with the respiratory rhythm on the basis of keeping synchronization; executing acousto-optic guidance according to the selected sleep aiding mode and the acousto-optic coupling mode, and guiding the respiratory rhythm to a target frequency by adjusting an acousto-optic output change frequency; and when the user is in the sleep state, controlling the sound-light output to smoothly transit to a closed state. By adopting the method, dynamic synchronization and self-adaptive guidance of acousto-optic output and the breathing rhythm of the user can be realized, the problem of rhythm mismatch caused by fixed output rhythm of traditional sleep-aiding equipment is effectively solved, and the sleep-aiding effect is improved.

This disclosure provides systems, methods, and devices for power state transitions with multiple chiplets. In a first aspect, a method of image processing includes receiving an indication to enter a partial sleep state for a processor comprising a first main domain and a first separate domain coupled through a different power supply path than the first main domain; and in response to the indication, transitioning, by the processor, the first main domain to a sleep state while the first separate domain remains in an active state for receiving processing requests or monitoring the first main domain. Other aspects and features are also claimed and described.

The invention discloses a sleep aiding method and system based on brain wave data, and the method comprises the steps: synchronously collecting brain waves, electrocardiosignals, environmental noise and user voice data through a flexible electrode array, constructing a multi-mode causal graph, and dynamically recognizing the type of a noise source (electromagnetic interference or psychological noise); the method comprises the following steps of: generating a target antagonistic intervention signal (such as reverse sound wave counteracting electromagnetic noise and binaural rhythm relieving psychological pressure) according to the target antagonistic intervention signal, generating dynamically adaptive light pulse and tactile vibration parameters in combination with a psychological-physiological collaborative model, and synchronously outputting sound, light and tactile intervention signals through a multi-modal actuator. Through causal reasoning and multi-sensory cooperative regulation and control technologies, accurate inhibition of noise interference and personalized induction of the sleep state are realized, the sleep time is remarkably shortened, the deep sleep duration is prolonged, and the method is particularly suitable for complex noise environments and anxiety-related insomnia scenes.

The invention discloses a sleep quality improving system based on big data analysis, and relates to the technical field of big data. According to the system, a multi-dimensional sleep state monitoring matrix is constructed, effective intervals of monitoring points are optimized in combination with dynamic adjustment factors and a deep learning algorithm, and redundant data interference is eliminated; generating a sleep intervention regulation factor based on the environmental influence function and the physiological rhythm constraint factor, and training the sleep intervention function through a genetic algorithm to dynamically adjust the intervention intensity; and generating an intervention instruction by utilizing the reinforcement learning model and combining the real-time physiological data of the user, the environmental parameters and the sleep stage prediction result, and controlling the starting sequence and parameter adjustment of the monitoring points until preset ideal sleep state parameters are met. According to the method, the problems of insufficient monitoring precision, poor environment adaptability, intervention lag and the like in a traditional method are solved, the accuracy and the real-time performance of sleep intervention are improved, and the method is suitable for the fields of smart home, medical health and the like.

The invention provides a sleep assisting system based on photoacoustic magnetic vibration wave resonance, and relates to the technical field of sleep assisting. The system comprises an acquisition unit, a processing unit, a stimulation unit and an evaluation feedback unit. Determining a sleep stage of the user, analyzing phases of multi-scale electroencephalogram rhythm components such as slow waves and spindle waves in a cross-scale manner, and extrapolating and predicting a phase value of a target electroencephalogram rhythm at a future moment in combination with a recent phase trajectory; taking the predicted phase as a target state, mapping a rising / falling slope of a slow wave form into an intensity change parameter of photoacoustic and magnetic vibration stimulation by constructing a mapping channel, and selecting an optimal stimulation combination for output through a decision model based on the intensity change parameter; calculating a phase locking effect index, and adjusting a phase prediction model parameter by using an optimization algorithm according to the index so as to optimize subsequent stimulation; and linkage regulation and control of an external environment are supported, and centralized monitoring of sleep states of multiple users can be realized. According to the invention, self-adaptive guidance of sleep brain rhythms is realized, and the sleep quality is effectively improved.

The invention relates to the technical field of physiological signal analysis, in particular to a sleep disorder intelligent diagnosis system based on a multi-modal knowledge graph, which comprises a signal lag analysis module, an abnormal signal identification module, a graph construction module, a graph path generation module and a diagnosis result output module. According to the method, difference comparison and normalized sequence analysis in a time window are performed on multiple types of physiological response data, and derivative trend identification and mutation section calibration are combined, so that collaborative identification of signal delay response and abnormal change positions is realized, and multi-dimensional information of duration, frequency and fluctuation amplitude in abnormal events is further structured; the method comprises the following steps: constructing a traceable symptom expression vector, extracting structural features of the traceable symptom expression vector in a time sequence evolution process through a cross-time window entity evolution path, identifying a growth trend of association strength between symptoms according to an attribute change direction and a path connection mode, and judging a fluctuation mode and an abnormal evolution tendency of a sleep state of a user in a specific time period.

The invention provides a CAN-FD wake-up circuit, a CAN transceiver and an electronic device, the CAN-FD wake-up circuit comprises a level shift module, an amplification comparison module and a wake-up identification module, the differential input end of the amplification comparison module is the source electrode of an NMOS tube, and the source electrode of the NMOS tube allows input of negative voltage, so the level shift module can process a CAN low level signal in a negative voltage range, and the wake-up identification module can process the CAN low level signal in the negative voltage range. Therefore, the input common-mode level range of the wake-up circuit of the CAN-FD is improved. On-off of the first switch is controlled based on whether the CAN transceiver is in a dormant state or a working state so as to control access of the second current source, and the output current of the second current source is a plurality of times of the output current of the first current source so as to increase the bandwidth of the amplification comparison module in the working state. Therefore, the amplification comparison module can be simultaneously applied to the dormant state and the working state of the CAN transceiver, so that the circuit cost is reduced.

The invention discloses a low-power-consumption newborn sleep management system, which relates to the technical field of newborn sleep management and comprises a multi-mode sensing module, a data processing and analyzing module, an environment regulation and control module and a power management module. The system collects environment and physiological data through a low-power-consumption sensor array, analyzes the sleep state in combination with layered feature extraction and dynamic threshold judgment technologies, and optimizes the sleep environment by using a closed-loop feedback mechanism. The power management module adopts an energy recovery and dynamic power supply strategy to reduce energy consumption. According to the application, comprehensive monitoring of the sleep state of the newborn and intelligent environment optimization are realized, the problems of high power consumption and poor real-time performance of an existing system are solved, and the requirements of low power consumption and high reliability are met.

The invention provides a non-contact sleep respiration and staging monitoring method and device combining visual and radio frequency signals, and is applied to the field of data processing. According to the method, sleep monitoring multi-mode original data (radar radio frequency signals and near-infrared human body images) and reference calibration data (breathing bandage waveforms and electroencephalogram data) serve as the basis, and standardized multi-mode data are generated through preprocessing. The preprocessing comprises video area extraction, radio frequency signal purification, time alignment and fragment cutting. Visual and radio frequency features are extracted and fused through an optical flow algorithm and analysis of tiny movement of human respiration, redundant, unique and collaborative information between the features is quantified to dynamically adjust a fusion strategy, and self-adaptive fusion features are generated. A reference calibration data training model is combined, a sleep respiration monitoring and staging model is finally constructed, target monitoring scene information can be processed, an accurate monitoring result can be output, and efficient sleep state analysis driven by multi-modal data is achieved.

The invention provides a radar-based full-time in-bed state monitoring method, system and product, and the method comprises the steps: carrying out the signal preprocessing of a collected radar echo signal, and obtaining target existence information and target physiological information; performing feature extraction on the target existence information and the target physiological information, and adaptively dividing a daytime period and a night period based on a feature extraction result; performing multi-mode sleep state staging based on the target existence information and the target physiological information in the night time period; performing daytime activity state classification based on the target existence information and the target physiological information in the daytime period; and generating a full-time in-bed state monitoring report based on the sleep state staging result and the daytime activity state classification result. According to the method provided by the invention, both long-term bedridden people and non-bedridden people can be considered, the day and night time periods are automatically switched, and the function that single radar equipment synchronously supports night sleep staging and daytime activity classification is realized.

Devices, systems, methods, and processes for operating a plurality of power supplies of a network device using cold redundancy are described herein. Traditionally, redundant power supplies are operated in parallel with primary power supplies, thus making power supply system less efficient. To address these issues, an analog architecture is provided for implementing cold redundancy in a network device. Each power supply includes two pins: one for designating the power supply as primary or secondary, and the other pin for configuring an activity state of the power supply. The activity state can be an active state or a sleep state. The secondary power supplies are further ranked in an order of priority using different voltage levels for assignment. In response to surge in load demand, activity states of one or more secondary power supplies are changed to the active state without requiring a signal from a centralized control software.

The invention discloses an undisturbed sleep monitoring method and a related device, and the method comprises the steps: carrying out the feature extraction of a first piezoelectric signal, obtaining a body movement feature, and enabling the first piezoelectric signal to be obtained through the superposition of a plurality of preprocessed original piezoelectric signals; frequency domain analysis is carried out on the second piezoelectric signals, frequency components corresponding to wave crests are recognized in a sleep heart rate related frequency band, a frequency pair corresponding to each moment is extracted, a frequency set is obtained, the frequency pair comprises a plurality of preorder frequency components in the first frequency sequence and the frequency of each frequency component, and the frequency set is obtained; the first frequency sequence is obtained by arranging frequency components in a descending order according to amplitude intensity; determining heart rate characteristics at each moment according to the frequency set; performing feature extraction on the acquired piezoresistive signals to obtain piezoresistive features; and determining the sleep state according to the body movement characteristics, the heart rate characteristics at each moment and the piezoresistance characteristics. The reliability and accuracy of sleep state recognition can be improved.

Implementations of systems and methods for coordinating the sleep mode of a vehicle processor and coprocessor. The systems and methods may include determining whether each of a plurality of subsystems is in an operational state that permits entering the sleep state, transmitting, from the processor to the coprocessor, a trigger message, and switching the plurality of subsystems into the sleep state. The systems and methods may identify by the coprocessor whether each of the plurality of subsystems has switched to the sleep state, start a first timer with a predetermined time period, and switch the processor and coprocessor to the sleep state within the predetermined time period in response to identifying whether each of the plurality of subsystems has switched to the sleep state.

The invention discloses a processing method and system for power management, and the method comprises the steps: judging an STR sleep state through two flag bits, and obtaining an STR wake-up state through the design of the communication between dual systems, thereby guaranteeing the sleep wake-up consistency of the dual systems through a dual authentication function, and effectively improving the sleep wake-up reliability of the dual systems.

The invention relates to the technical field of health data analysis, in particular to a traditional Chinese medicine children physique and pathology analysis system which comprises an electroencephalogram signal sensing module, a phase deviation feature extraction module, a wave band energy delay analysis module, a physique type judgment module and a pathology risk prediction module. According to the method, frequency domain analysis and phase offset calculation are carried out on electroencephalogram signals, accurate quantification of sleep state sign characteristics is achieved, the sequential relation between the phase change trend and wave band energy is captured, a dynamic characteristic combination capable of reflecting the rhythm transfer rule is constructed, the separability of physique differences is enhanced in a nonlinear mapping mode, and the accuracy of the physique differences is improved. A classification process is kept stable in a high-dimensional space, and a multi-factor prediction system is formed by combining indexes such as energy delay and phase anomaly, so that health state assessment has continuity and sensitivity, risk change can be identified in an early stage, and refined judgment of children's pathological tendency and improvement of result robustness are realized.

Apparatus and methods detect sleep staging events. The apparatus (100) may be configured to obtain a facial biopotential signal measured between two electrodes connected to a user's face, which may be configured to form a transverse-ocular measurement vector. The biopotential signal may be measured by a biopotential measurement device comprising the two electrodes. The apparatus (100, 200) may be configured to derive, from the facial biopotential signal, a plurality of biosignals suggestive of sleep staging events. The apparatus may be configured to classify individual segments of the plurality of biosignals as belonging to one of a plurality of sleep stating events. The classification may involve using a trained machine learning model, such as a recurrent neural network, to predict sleep staging events for different segments or epochs of the plurality of biosignals.

The application belongs to the technical field of smart home, wearable device, biological signal processing and artificial intelligence control, and specifically discloses a self-adaptive audio adjustment sleep-aiding method based on sleep state recognition, which comprises the following steps: a non-invasive touch interaction mode is introduced to simplify the starting process of the sleep-aiding process; key physiological indexes such as the heart rate and body movement of a user are continuously monitored, and subtle changes in the data are analyzed to determine the physiological state of the user and the reaction of the user to environmental changes, so that the change in the acoustic environment is ensured to be always within the comfortable range that can be accepted by the user; the amplitude of the volume adjustment is controlled to be below the perception threshold of the user, and a special disturbance observation window is set to evaluate the physiological influence of each fine adjustment operation, so that inappropriate adjustment can be found and cancelled in time before substantial interference is caused. The application significantly improves the reliability of the user experience and sleep-aiding effect.

An aspect of this disclosure includes a method performed by a reader device. The method includes transmitting, to a mobile device, a message including information indicating whether a sleep state of the mobile device is enabled or disabled.

The invention discloses a portable wireless radio frequency switching method and system, a medium and a program product, and relates to the field of transmission, in the method, a switching device obtains a radio frequency signal strength value of a device to be switched; the switching device sends an awakening instruction and a handshake request to a to-be-switched device which is in a deep sleep state and keeps low-power-consumption monitoring; executing the handshake request to establish a secure communication connection; the switching device sends the encrypted radio frequency configuration data to a device to be switched, and the device to be switched writes the encrypted radio frequency configuration data into a nonvolatile memory; after the equipment to be switched completes writing of the encrypted radio frequency configuration data, a confirmation signal is sent to the switching equipment, the deep sleep state is automatically switched to, and low-power-consumption monitoring is kept. On the basis of keeping certain power to monitor the configuration request, low-power-consumption standby is realized, so that energy waste is reduced, and the use cost of equipment is reduced.

The invention relates to a sleep monitoring and early warning method and system based on respiration data analysis and a medium, and belongs to the technical field of .The respiration data of a user is predicted through a sleep state and sleep apnea hypopnea index prediction model, the sleep state of the user and the sleep apnea hypopnea index are estimated, and the sleep apnea hypopnea index is obtained. And finally, early warning is performed according to the sleep state of the user and the sleep apnea hypopnea index, and meanwhile, a related treatment scheme is generated according to early warning information. According to the method, a deep learning model is pre-trained, sleep staging and sleep apnea hypopnea index estimation are carried out on the model in a unified framework at the same time, and internal correlation between a sleep macrostructure and a respiratory event is effectively decoupled. Then, through a domain adversarial training mechanism, sleep respiration characteristic knowledge learned in the contact type respiration signals is migrated to millimeter wave radar signals, and the problem that the generalization ability of a model is insufficient due to scarcity of radar data labels is solved;

The invention discloses a sleep state self-adaptive layered regulation and control method and platform based on an environment regulation window, and relates to the technical field of self-adaptive control, and the method comprises the steps: carrying out the sleep state analysis according to a physiological data sequence, obtaining a real-time sleep stage, carrying out the scheduling task distribution, and generating a function unit scheduling sequence; performing data acquisition priority management and control on the wearable device to obtain real-time sleep associated data; generating a sleep environment adjustment strategy by taking the real-time sleep stage as an environment adjustment constraint; after updating the sleep environment of the user, activating an environment adjusting window; and operating a hierarchical scheduling architecture by taking an environment adjustment window as a time constraint, and circularly performing adaptive control updating. The technical problem that in the prior art, due to the fact that the sleep environment is difficult to adjust accurately in real time according to the sleep state of the user, the sleep quality of the user is low is solved, and the technical effects that the sleep state of the user is accurately judged, the sleep environment is dynamically and adaptively adjusted, and the sleep quality is improved are achieved.

The invention discloses a sleep emotion state recognition-oriented multi-mode sensing signal analysis method and system, and relates to the technical field of sleep emotion data processing. According to the scheme, a progressive data processing method is constructed, and emotion-sleep state recognition and self-adaptive intelligent feedback regulation and control are achieved. The core process comprises the steps of ensuring the time sequence consistency of original data through multi-mode signal transmission time sequence interference analysis, adaptive selection of fixed clock calibration alignment and multi-mode signal dynamic resynchronization, then extracting and fusing deep features of sleep emotion multi-mode signals by using a deep network, and further completing emotion-sleep state recognition. And finally, sleep emotion collaborative intelligent regulation and control are driven according to a sleep-emotion state recognition result, and a physiological safety monitoring mechanism is introduced, so that the reliability and safety of the regulation and control process are ensured, and the effect of improving the emotion-sleep state recognition and intelligent feedback regulation and control accuracy is achieved.

The invention relates to a personalized sleep-aiding regulation and control method and system fusing multi-band characteristics and closed-loop feedback, and relates to the technical field of sleep regulation and control. According to the method, a cross-band coupling matrix is constructed, a nerve-physiological synergy index is calculated, environment-brain state interaction potential energy is calculated based on different band powers and environment sound pressure levels, and the nerve-physiological synergy index and the environment-brain state interaction potential energy are combined into a coupling state vector; solving the coupling state vector to judge the stability of the sleep state; on the basis, personalized sleep induction dynamic attractor intensity is calculated, and different sleep regulation and control strategies are generated according to the steady state / unsteady state and the corresponding attractor intensity. According to the invention, the unique sleep dynamics of the user can be understood and self-adaptively regulated.

The invention provides a multi-mode sleep intervention system and method based on self-adaptive dynamic control. The system comprises a sensor module used for collecting brain wave activity signals in real time in a non-invasive mode; the AI module is used for performing sleep stage identification and stage division on the brain wave activity signal through a sleep stage prediction model, judging a current sleep state through a first AI strategy engine and automatically generating an optimal first intervention parameter sequence; the control module is used for issuing a corresponding control instruction to the multi-mode intervention module according to the optimal first intervention parameter sequence; and the multi-mode intervention module is used for executing one or more of sound, light, magnetism, temperature, touch and smelling multi-mode intervention according to the control instruction. Through multi-mode fusion perception, closed-loop monitoring control, dynamic adjustment of an intervention strategy and multi-mode interaction and intervention, the effects of accurately perceiving the sleep state in real time, improving the data anti-interference capability, self-adapting to sleep management, adapting to individual differences and meeting the requirements of different intervention scenes are achieved.

Embodiments described herein disclose a method comprising: determining a breathing waveform based on an image signal received from a camera focused on at least a portion of a patient; receiving an observed sleep signal of the patient corresponding to the breathing waveform in time, wherein the observed sleep signal comprises a sleep state tag; tagging each segment of the breathing waveform using a sleep-wake state tag to generate a tagged breathing waveform; generating an input feature matrix by processing the marked breathing waveform; and training a machine learning (ML) model using the input feature matrix.