1747 results about "Sleep state" patented technology

The invention relates to the field of sleep monitoring, in particular to a sleep monitoring visual analysis method and system based on a smart bracelet. The method comprises the following steps: extracting a sleep physiological signal of a wrist part of a user based on a smart bracelet; sliding time window analysis and multi-mode physiological state sensing are carried out, and a dynamic sleep state sensing map is constructed; performing transient mutation rhythm recognition on the dynamic sleep state sensing map, performing sleep stage evolution trend analysis, and constructing a sleep stage evolution model; the method comprises the following steps: collecting multi-dimensional parameters of a sleep environment, carrying out environment parameter change detection and environment disturbance-sleep stage change association identification, and obtaining an environment disturbance-sleep rhythm mutation association rule; environment disturbance intensity evaluation is carried out according to the association rule, and a real-time sleep state visual view is constructed. According to the method, accurate and high-readability sleep monitoring data visualization is realized, the overall sleep quality is improved, and the long-term health risk is reduced.

Various embodiments herein provide techniques related to transmission of a wake-up signal (WUS) from a base station to a user equipment (UE). In embodiments, the WUS may be received by a wake-up receiver (WUR) of the UE. Based on reception of the WUS, a main receiver of the UE may exit from a deep-sleep state or an off state. Other embodiments may be described and / or claimed.

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 monitoring and analyzing system based on multi-sensor fusion, and relates to the technical field of health monitoring, the sleep state monitoring and analyzing system comprises a multi-modal sensor module used for collecting multi-dimensional data related to a sleep state, the multi-dimensional data comprises a bio-electricity signal, a physiological parameter, body movement data and an environment parameter, and the multi-modal sensor module is used for collecting the multi-dimensional data; the multi-modal sensor module comprises a non-contact sensor, a flexible electronic skin sensor and a bio-electricity signal sensor, and the data fusion and processing module is used for carrying out preprocessing, dynamic self-adaptive fusion and federal learning modeling on collected original data. According to an existing contact type sleep state monitoring scheme, more flexible and accurate sleep state data are realized through a non-contact type monitoring sensor in cooperation with dynamic adjustment of sleep monitoring content and adjustment of weights of various sensors for monitoring the sleep state; and a more accurate and intuitive reference report is provided for the sleep state and the health state of the subsequent user.

The invention relates to a multi-device cooperative control method and device based on a user state and an air conditioner. The method comprises: monitoring a sleep state of a user; and determining a sleep stage of the user based on the sleep state, and performing cooperative control on each device according to a control strategy matched with the sleep stage. The method and the device can adapt to the difference of dynamic sleep stages, and control the devices to operate cooperatively according to the control strategies matched with the different sleep stages, so that the self-adaptive adjustment and parameter adjustment precision of the devices is improved, and the problems of poor sleep mode adjustment effect and low adaptability in the prior art are solved.

The invention belongs to the technical field of intelligent hardware control methods, and discloses a low-power-consumption intelligent hardware control method which comprises the following steps: acquiring working state parameters and user interaction information of intelligent hardware, and generating an equipment state feature vector; judging whether a dormancy condition is met or not according to the equipment state feature vector; when the sleep condition is satisfied, sending a network communication agent instruction; the coprocessor configures an internal timer and awakens signal output after receiving the instruction; the master control stores a system operation state, configures a wake-up source and then enters a deep sleep state; the coprocessor cuts off the power supply of the inactive module; when a timer of the coprocessor reaches a preset network heartbeat cycle, a wake-up signal is sent to the master controller; and the master control recovers the system state, updates the feature vector and re-decides whether to sleep or not. And through multi-dimensional state perception and a master-slave cooperation architecture, and through refined power management and a dynamic reevaluation mechanism, the equipment power consumption is obviously reduced on the premise of ensuring the user experience.

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 particularly relates to a quartered sleep state monitoring method and a quartered sleep state monitoring system based on a non-contact vibration sensor. The quartered sleep state monitoring method and the quartered sleep state monitoring system are particularly suitable for realizing non-sensitive sleep quality evaluation through an intelligent bed in a family environment. Comprising the following steps that 1, vibration signals of a human body in the sleep process are continuously collected through a non-contact vibration sensor; step 2, preprocessing the vibration signal, including denoising and segmentation processing, to obtain time domain vibration data; step 3, performing frequency domain transformation on the time domain vibration data, and extracting frequency domain features including respiratory rate, heart rate and heart variability parameters; 4, time alignment is carried out on the frequency domain features and sleep staging labels synchronously obtained through a polysomnography, and a training data set is constructed; an XGBoost classification model based on time-frequency domain feature fusion is provided, fundamental frequency and harmonic energy proportions of respiratory signals are extracted through short-time Fourier transform, and quartering classification is achieved in combination with the LF / HF ratio of heart rate variability.

The invention relates to the technical field of smart home, and discloses an electric blanket system capable of adjusting calorific value by self-adaption to environment temperature, comprising a sleep sensing module used for collecting physiological signal data of a user; the state recognition module is used for recognizing the current sleep state of the user; the temperature target generation module is used for determining a corresponding target temperature value according to the current sleep state; the control calculation module is used for calculating a control instruction of each heating area; the multi-area heating execution module is used for performing heating control on the plurality of independent heating areas; and the temperature acquisition and feedback module is used for acquiring the real-time temperature of each heating area and feeding back the temperature to the control calculation module. Through linkage of the AI housekeeper and the external electronic bracelet, adaptive adjustment of the intelligent temperature control system is realized, a temperature control strategy is dynamically optimized according to real-time temperature data, user behaviors and environmental changes, limitation of a traditional static scheme is avoided, personalized temperature adjustment is provided, and flexibility and comfort are improved.

The invention provides a sleep scheduling method and a related device, the method is applied to a terminal device, and the method comprises the following steps: after first service data is transmitted through a wireless communication system (such as a wireless fidelity Wi-Fi system) and a network device, the wireless communication system is dormant and awakened according to a first sleep scheduling strategy, the first sleep scheduling strategy comprises a first duration (comprising at least one first time slice and at least one second time slice), the first time slice and the second time slice in the first duration are alternately performed, and the wireless communication system is in a sleep state in the first time slice and is in a working state (comprising a monitoring state) in the second time slice. For example, the length of the first time slice and / or the second time slice is determined according to the time delay of the service requirement corresponding to the first service data. Therefore, periodical dormancy and monitoring can be carried out based on the time delay requirement of the current service instead of being in a monitoring state all the time, the real-time experience of the service is ensured, and the power consumption is reduced.

The invention discloses a multi-modal data fusion and intelligent analysis high-precision sleep monitoring system and method. The system comprises a data acquisition layer, a transmission layer, an analysis layer and an application layer, the data acquisition layer transmits data acquired by a sensor to the transmission layer step by step through BLE or Wi-Fi, the transmission layer divides the data acquired by the sensor into two paths through a Wi-Fi module and transmits the data to a main control device and a cloud server in the analysis layer, and the main control device and the cloud server process the data. The data collection layer comprises a data collection module, the data collection module comprises a physiological data collection unit, an environment data collection unit and a subjective data collection unit, the data collection layer is used for collecting data of multiple sources such as physiological data, environment data and subjective data, and the data collection layer is used for collecting data of multiple sources such as the physiological data, the environment data and the subjective data. The deep learning and big data analysis technology is applied, high-precision recognition of the sleep state and precise evaluation of health risks are achieved, and a personalized sleep improvement scheme is provided.

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 communication, and discloses an awakening method, equipment and a storage medium. The wake-up method comprises the following steps: when a hardware device enters a sleep state, setting a data acquisition pin in a serial communication protocol interface in a power-off state in the hardware device as a trigger function. When a data acquisition pin receives a low level signal on a data line of a corresponding communication bus, a hardware device can be directly triggered to wake up from a sleep state; or when the data acquisition pin receives a low level signal on the data line of the corresponding communication bus, the serial communication protocol interface can be triggered to enter a working state, and after the serial communication protocol interface receives address data and an awakening instruction matched with the hardware device, the hardware device is awakened from the sleep state. Therefore, based on the method, when the hardware device enters the sleep state, the wake-up source (namely the serial communication protocol interface) can enter the power-off state, so that the power consumption of the electronic equipment is saved.

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.

This document describes systems, methods, devices, and other techniques used for improving a sleeper's body thermoregulation during sleep. One aspect is a system, comprising a bed with a heating element and a cooling element and a controller configured to receive a current sleep state of a sleep stage sequence for a user in the bed, when the current sleep state is a non-rapid eye movement (NREM) state, activating the heating element to increase a skin temperature of the user, and when the current sleep state is a rapid eye movement (REM) state, activating the cooling element to decrease the skin temperature of the user.

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 discloses a health trend analysis early warning method based on big data, and relates to the technical field of data processing, and the method comprises the steps: analyzing female physiological cycle data based on big data, and building a health trend analysis model; the method comprises the steps of obtaining a physiological cycle type of a target user, collecting a sleep state, a physiological cycle and physical sign data of the target user, generating standard health monitoring data, initializing an analysis model according to basic information of the user, performing sleep quality change analysis, evaluating a health trend of the user according to an analysis result in combination with physiological cycle characteristics, and providing personalized sleep improvement suggestions. The technical problem that traditional health management in the prior art neglects the influence of the female physiological cycle on the health state and the sleep quality and lacks pertinence and individuation is solved, and individualized health management is provided by combining the female physiological cycle data and the sleep state data. And the sleep quality of women in different physiological cycle stages is improved.

The invention provides a multi-modal monitoring integrated intelligent massage bed sleep analysis method, and relates to the technical field of data processing, pressure information of a plurality of positions at a plurality of moments is collected through a pressure sensor array, pressure comfort analysis of a user is carried out, and a plurality of pressure comfort levels are obtained; electromyographic signals of a plurality of main body positions of a user at a plurality of moments are collected through an electromyographic signal sensor array, and a plurality of electromyographic signal arrays are obtained; according to the multiple pressure comfort levels, multiple electromyographic signal completion generation scales are configured, completion generation is conducted on the multiple electromyographic signal arrays, and multiple completion electromyographic signal arrays are obtained; and according to the plurality of complemented electromyographic signal arrays, performing electromyographic comfort analysis of the user, and calculating to obtain the comfort of the user. The technical problems that in the prior art, a sleep analysis detection signal source is single, deep analysis on the body state of the user is lacked, and evaluation on the sleep state of the user is not comprehensive enough are solved, and comprehensiveness and accuracy of sleep analysis are improved.

The invention provides an old people body health early diagnosis and risk assessment system based on artificial intelligence, and relates to the technical field of old people health monitoring and assessment. Old people health parameters are collected in real time according to a set time interval, and a statistical method is used to calculate a mean value and a standard deviation of cardiovascular parameters; then, based on acceleration data, heart rate change, blood pressure fluctuation and position change information, sleep, movement and resting states of the old people are judged, and the day and night blood pressure change rate, the sleep state heart rate standard deviation, the movement state pulse wave conduction speed change quantity and the resting state heart stroke output average value are calculated; four risk assessment formulas of blood pressure risk, heart rate risk, pulse wave conduction velocity risk and heart stroke output quantity are constructed according to the data, a comprehensive health risk value is calculated through weighted merging, and the health state of the old people is judged to be healthy, medium-low risk or high risk according to a set threshold value.

The embodiment of the invention provides a process freezing method and device, relates to the field of terminals, and can reduce resource consumption of background processes and improve performance and endurance of electronic equipment. The method is applied to the electronic equipment and comprises the steps of creating a target thread in response to that the electronic equipment is in a first scene; determining a to-be-frozen process based on the target thread; and the process to be frozen is set to be in an uninterruptible sleep state based on the target thread, and the process to be frozen is set to be in an uninterruptible sleep state based on the target thread.

The invention discloses a sleep assistance system and method for dynamic natural sound simulation, and particularly relates to the technical field of sleep assistance, comprising: acquiring historical preference data of a user and external environment data, generating a first reference coefficient based on the historical preference data of the user and the external environment data, the historical preference data comprises a sound type, a volume value and a playing duration; acquiring historical sleep cycle data of the user, and inputting the first reference coefficient and the historical sleep cycle data into a pre-constructed first sound training model; the sleep state is determined on the basis of the physiological evaluation coefficient obtained in real time, the sound playing strategy is optimized by comparing the sleep state with the preset sleep duration, and dynamic intelligent regulation and control of sleep assistance are achieved. In the sleeping process, sound parameters can be adjusted in time according to the actual duration condition of light sleep, deep sleep, dream or awakening state of the user.

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 embodiment of the invention discloses a sleep peptide and differential metabolite detection method for caenorhabditis elegans, belongs to the technical field of organism metabolism, and aims to research sleep-awakening key metabolite of caenorhabditis elegans and a regulation mechanism of the sleep-awakening key metabolite. According to the sleep peptide for caenorhabditis elegans, the sleep peptide is an F4 monomer as shown in SEQ ID NO: 1. Compared with the prior art, the sleep and awakening process of the caenorhabditis elegans under the intervention of the goat milk-derived sleep peptide is analyzed through a metabonomics technology, the key metabolite for regulating and controlling sleep and awakening is successfully screened out, and a core metabolic pathway related to the key metabolite is disclosed. The application also constructs a metabolite-pathway-target regulation framework, fills the blank of sleep research in the aspect of metabolic regulation, provides a scientific basis for a sleep intervention strategy based on metabolic regulation, and also lays a foundation for developing metabolic markers of sleep states in the future.

The invention discloses an air conditioner control method and device, an air conditioner and a storage medium, and the method comprises the steps that sleep information of a user is collected through a radar detection technology, and the sleep information is predicted and output through a neural network model to obtain the sleep state of the user; the method comprises the following steps: acquiring indoor noise information through a multi-modal noise sensing system, and performing voiceprint analysis processing on the noise information to obtain a voiceprint analysis result; and a control instruction is generated in combination with the sleep state and the voiceprint analysis result, and operation control is conducted on the air conditioner based on the control instruction. The sleep state of the user is accurately judged through the millimeter wave radar technology and the neural network model, meanwhile, the noise information is subjected to voiceprint analysis processing through the multi-mode noise sensing system, in this way, sleep state monitoring and air conditioner noise self-adaptive control can be achieved, and therefore the using effect of the air conditioner is improved.

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 provides an AI chip control method which comprises the steps that when an awakening instruction of a host end is received, an AI chip is controlled to exit a first low-power-consumption mode and enter a second low-power-consumption mode, the first low-power-consumption mode is used for waiting for the host end to execute and complete a question-asking stage task in a dormant state, and the second low-power-consumption mode is used for waiting for the host end to execute and complete a question-asking stage task; the second low power consumption mode is used for controlling the AI chip to execute a processing stage task and a decoding stage task in a mode lower than normal operation power consumption; in the second low power consumption mode, controlling the AI chip to execute the processing stage task and the decoding stage task; and after the AI chip executes the decoding stage task, sending a task result to the host end, controlling the AI chip to exit the second low-power-consumption mode, and controlling the AI chip to enter the first low-power-consumption mode. By optimizing software and hardware collaboration, a higher energy efficiency ratio is realized, and the overall power consumption is remarkably reduced while the high performance of the AI chip is ensured to be kept.

Wake-word processing by a wearable electronic device could be carried out when the device is worn by a user and is in a device sleep state, the device including a linear microphone array having at least two microphones vertically spaced from each other, and the device also including a processor. And the example method could involve (i) the at least two microphones of the linear microphone array receiving an audio waveform representing a wake-word utterance, (ii) the processor making a determination, based at least on an angle of arrival of the audio waveform at the at least two microphones of the linear microphone array and / or an energy level of the audio waveform received at the at least two microphones of the linear array, of whether to accept the wake-word utterance or rather to reject the wake-word utterance, and (iii) the processor controlling operation of the device based on the determination.

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.