54 results about "Sleeping stages" patented technology

A computing device for monitoring a sleep stage of a user in a contactless manner includes a processor, a radar unit, and a memory. The processor executes instructions from memory to cause the computing device to perform a series of signal processing methodologies on the waveforms received from the radar unit to accurately determine the sleep stage of the user in a contactless manner.

The invention discloses a multi-region pressure monitoring and feedback method for a sleep monitoring mattress, which comprises the following steps: step 1, arranging a monitoring feedback system to collect pressure data and biological characteristic signals, step 2, dynamically planning a human body contact region during sleep, step 3, identifying a sleep stage and an abnormal sleep event, and step 4, determining the sleep stage and the abnormal sleep event. 4, dynamic feedback is output based on cooperation of multiple actuators; by means of dynamic area reconstruction and high-density sensor layout, millisecond-level pressure changes of key parts of a human body can be captured, the detection precision is greatly improved, active intervention of user health is achieved through multi-actuator collaborative dynamic adjustment, meanwhile, the method is combined with user body shape parameters, sleep monitoring of the whole crowd can be covered, and the method is suitable for being popularized and applied. The applicability of the mattress is effectively improved.

Method for controlling a ventilation system, ventilation system, and associated ventilation installation. This method for controlling a ventilation system includes a remote control and a ventilation grille, which includes battery-powered receiving means. Cyclically, the ventilation grille switches between a sleep phase (ΔTs), during which the receiving means are off, and a wake-up phase (ΔTr), during which the receiving means are on and ready to receive a control signal sent by the remote control. The control method also includes actuation of the remote control by a user, so as to send a control signal for a transmission time (ΔTt) that is longer than the sleep time (ΔTs). Figure for the abstract: Figure 2

The application relates to the technical field of air conditioners, and discloses a method for controlling an air conditioner, which comprises the following steps: acquiring a current comfort value of an SPMV model associated with a sleep stage of a user; and controlling operation parameters of a fresh air mode associated with the air conditioner according to a matching condition of the comfort value and a preset comfort value. The method can regulate the oxygen content of an environment where the user is located, and improve the comfort of the user in the sleep stage. The application further discloses a device for controlling an air conditioner, an air conditioner and a storage medium.

The invention belongs to the cross technical field of biomedical engineering and signal processing, and discloses a fixed-length sleep staging sequence global correction method based on dynamic programming, which comprises the following steps of: defining an original sleep staging sequence which is divided by taking 30 seconds as a fixed time window and comprises a plurality of continuous fixed time windows, the target generates a corrected sequence, the number of fixed time windows of the corrected sequence is kept consistent with that of the original sequence, only the sleep state value corresponding to each fixed time window is corrected, and the fixed time windows are not added or deleted; a sleep state coding rule is set, and the waking state, the first light sleep stage, the second light sleep stage, the deep sleep stage and the rapid eye movement sleep stage correspond to unique digital codes respectively. Through a global cost accumulation mechanism, subsequent illegal transfer caused by local correction is avoided in advance, iteration is not needed, and the correction efficiency is remarkably improved.

The invention relates to the technical field of intelligent control, and discloses an air conditioner regulation and control method and device based on user sleep parameters, and the method comprises the steps that when it is detected that an air conditioner is in a running state and a user is in a sleep state, sleep sign parameters of the user are obtained; analyzing a current sleep cycle parameter of the user according to the sleep sign parameter; the sleep cycle parameters comprise a current sleep cycle and a current sleep stage corresponding to the current sleep cycle; according to the sleep cycle parameter, determining a comfortable sleep demand parameter corresponding to the user; according to the comfortable sleep demand parameters, regulation and control parameters corresponding to the air conditioner are determined; and according to the regulation and control parameters, the air conditioner is controlled to execute air outlet operation corresponding to the regulation and control parameters. Therefore, operation of the air conditioner can be regulated and controlled in real time based on the user sleep parameters, so that regulation and control flexibility of the air conditioner can be improved, and sleep comfort of the user can be improved.

Techniques for improved respiratory therapy are provided. Glucose data of a user collected during a sleep session is accessed. Based at least in part on the glucose data, a plurality of transitions between sleep stages experienced by the user during the sleep session is determined. A sleep quality measure for the sleep session is generated based at least in part on the plurality of transitions. An apnea risk score for the user is generated based at least in part on the sleep quality measure, and one or more medical interventions for the user are provided based on the apnea risk score.

The invention discloses an end-to-end sleep monitoring and event detection method based on a wrist accelerometer, and the method comprises the following steps: collecting original motion data through the wrist accelerometer, extracting a basic feature vector, generating a sleep rhythm prior feature based on the basic feature vector by using historical activity data of a user, and carrying out the end-to-end sleep monitoring and event detection. Splicing the prior feature and the basic feature vector to form an enhanced feature vector; converting the enhanced feature vector into a two-dimensional feature map, and outputting the sleep state probability of each time point by using a time sequence-variable interaction model; performing multi-scale Gaussian smoothing processing on the original binary event tag to generate a soft tag, and training a time sequence-variable interaction model by taking the soft tag as a supervision signal; performing forward propagation on new accelerometer data based on the trained model, outputting a probability sequence, and detecting sleep events and performing sleep staging through post-processing operation; according to the invention, high-precision sleep staging and sleep event detection are realized by only using accelerometer data.

The invention discloses a sleep staging result determination method, device and equipment, a medium and a product, and the method comprises the steps: obtaining a BCG signal of a detection target, the BCG signal being obtained through the detection of a sensor arranged below the detection target; performing data preprocessing on the BCG signal, and determining a comprehensive input feature; inputting the comprehensive input features into a pre-trained staging model to obtain a prediction result; and correcting the prediction result based on a smoothing rule, a stage transition probability rule and a sleep cycle rule to obtain a final sleep staging result. According to a BCG signal obtained through non-contact measurement, multiple features are extracted and input into a pre-training staging model, different stages of a model output result are corrected, and a final sleep staging result is obtained. Non-contact, non-interference and non-intrusive sleep staging continuous monitoring is realized, so that a user can conveniently obtain a sleep staging result of the user and know the overall sleep condition of the user, and the sleep quality and health are improved.

The application relates to the technical field of model correction, and discloses a method for correcting a thermal comfort model, which comprises the following steps: obtaining environmental perception information of user feedback; determining a third correction amount for correcting an SPMV model according to the environmental perception information; and correcting the SPMV model according to the third correction amount for correcting the SPMV model. According to the scheme, the third correction amount for correcting the SPMV model is determined, and the SPMV model is corrected in combination with the third correction amount, so that a thermal comfort model with higher precision can be obtained in combination with actual environmental requirements of a user, the corrected thermal comfort model is more in line with actual change rules of the thermal comfort of the user, and therefore the accuracy of thermal comfort judgment in a sleep stage of the user is improved, and the demand of the user for the thermal comfort is met. The application further discloses a device for correcting a thermal comfort model and a storage medium.

The present application relates to the field of intelligent health monitoring and digital technology of traditional Chinese medicine, and particularly relates to a traditional Chinese medicine sleep health monitoring system based on millimeter wave radar. The system comprises: a data acquisition module for acquiring original vital sign signals through millimeter wave radar; a data processing module for extracting a sleep physiological parameter set; a sleep staging identification module for identifying sleep staging results corresponding to wakefulness, light sleep, deep sleep and rapid eye movement; a traditional Chinese medicine sleep evaluation module for converting sleep parameters into traditional Chinese medicine quantitative indicators through a quantitative mapping unit, generating traditional Chinese medicine syndrome differentiation labels through a space-time correlation unit, and outputting traditional Chinese medicine health monitoring results after fusion analysis by an analysis and decision unit; and a monitoring result output module for generating a visual health report. The present application realizes the deep integration of non-invasive, continuous sleep monitoring and objective, quantitative traditional Chinese medicine syndrome differentiation, and provides a systematic solution for sleep health management.

This invention discloses a sleep staging system based on multi-threshold neighborhood extreme value pattern statistics, comprising an EEG signal acquisition unit, a signal preprocessor, a multi-threshold neighborhood extreme value time-domain feature extractor, a gray wolf optimizer, and a random forest classifier. The preprocessing involves initial filtering and 30-second segmentation of selected EEG signals. Based on the duration of the single-channel EEG signal annotation, the single-channel EEG signals are divided into two categories: original signal segments and signal segments requiring data augmentation. Next, data augmentation is performed on specified segments, and the extreme values ​​of the augmented data segments are selected. Then, the neighborhood differences of the extreme values ​​are calculated, and multi-threshold and multi-state processing is performed on the 5-stage matrix obtained from these differences to derive the multi-threshold neighborhood extreme value pattern statistical code. The thresholds in the multi-threshold processing are determined by the gray wolf optimizer. Finally, the obtained feature matrix is ​​input into the random forest classifier. This invention enhances the automatic sleep staging capability based on EEG signal augmentation and designs and manufactures a complete system encompassing EEG acquisition and sleep analysis visualization.

The application relates to the technical field of intelligent household appliances, and discloses a method for controlling an air conditioner, which comprises the following steps: acquiring a current sleep state of a user associated with the air conditioner in a sleep stage; obtaining a current comfort value of an SPMV model associated with the user; and in the case that the current comfort value does not match a preset comfort value, executing an environment control strategy corresponding to the current sleep state according to the current sleep state, so that an adjusted current comfort value matches the preset comfort value. The method can improve the accuracy of comfort degree judgment in the sleep stage of the user and meet the demand of the user for thermal comfort degree. The application also discloses a device for controlling the air conditioner, the air conditioner and a storage medium.

The invention discloses a sleep awakening method and device, equipment, a storage medium and a product. The method comprises the steps of obtaining a to-be-awakened time period; physiological parameters of the user are collected; determining a first sleep stage of the user according to the range of the physiological parameters; and if the first sleep stage is the second sleep stage and the current time is in the to-be-awakened time period, executing an awakening operation. According to the method, the physiological parameters of the user are collected, whether the user is in the second sleep stage conforming to the natural wake-up rule or not is judged, if yes, the wake-up operation is executed in the to-be-wake-up time period, the sleep inertia phenomenon caused by forced wake-up of the user in the deep sleep stage can be effectively avoided, and the wake-up flexibility is improved.

The invention provides an automatic sleep staging method based on multi-scale decision fusion, which comprises the following steps: S1, performing multi-scale windowing processing on an original multi-modal physiological signal to obtain a long window stream and a short window stream; s2, performing feature learning on the signals of the long window flow and the short window flow by adopting a parallel double-flow feature learning architecture to obtain respective sleep staging prediction probabilities; and S3, performing decision-level fusion on the prediction probabilities of the long window stream and the short window stream to generate a final sleep staging result. According to the method, the physiological signals of the long time window and the short time window are processed in parallel, decision results of the physiological signals are fused, accurate recognition of the sleep stage is achieved, and the problem that in the prior art, the staging accuracy is limited due to the fact that macroscopic background and microcosmic event characteristics are difficult to capture at the same time through a single time window is effectively solved.

The application discloses an automatic sleep staging method based on a visual Transformer. The original PSG signal is processed through a sliding window to obtain a PSG signal sequence; the PSG signal sequence is processed through data enhancement to obtain an enhanced signal sample; a sleep staging network is constructed through a visual Transformer frame-level encoder, a bidirectional GRU sequence-level encoder and a softmax layer; each group of PSG signal samples is input into the sleep staging network to predict the sleep stage of the PSG signal sample; the network is initialized through cross-modal transfer learning; a loss function is constructed in combination with the real sleep stage of the PSG signal sample; an ADAM optimizer is used for training to obtain an optimized sleep staging network; PSG signals are collected in real time; the PSG signal sample is input into the optimized sleep staging network to predict the sleep stage. The application designs data enhancement according to the noise and artifacts of the PSG signal, improves the robustness of the network to the noise and artifacts of the PSG signal, introduces an encoder based on the visual Transformer to improve the feature representation capability of the network, and reduces the dependence on a large amount of PSG data through transfer learning.

This application provides a method and device for controlling the operation of intelligent pressure-sensitive adjustable bedding, belonging to the field of intelligent control technology. It addresses the problem that existing bedding adjustments mainly rely on manual operation, which is prone to generating noise and affecting the user's sleep quality. The method acquires initial pressure data from each fiber pressure sensor; based on the initial pressure data and a preset sleep posture coordination model, it determines the pending pressure data and coordination completion time. Based on the coordination completion time and sleep stage monitoring data, it determines the pressure adjustment period to generate air spring identification groups. Based on the air spring identification groups corresponding to sound monitoring information and real-time acquired sleep stage monitoring data, it determines whether the pressure adjustment state of each air spring is in a pending state; if so, according to each pending state and the corresponding pending pressure data, it controls an intelligent air pump to adjust the pressure of at least one air spring in the air spring identification group.

The invention relates to the field of physiological signal processing and deep learning, and discloses a sleep staging method and system based on two-way non-contact sensing and deep learning, a non-contact multi-channel body movement signal acquisition module is utilized, a non-contact sensor is arranged below a mattress to acquire multi-source signals, and the multi-source signals are fused; calling a multi-source signal of the non-contact multi-channel body movement signal acquisition module by using a multi-mode signal preprocessing module, and carrying out synchronization, denoising, artifact correction and segmented standardization processing on the multi-source signal; a deep learning sleep staging module is utilized to call the signals processed by the multi-modal signal preprocessing module, a deep learning network is adopted to perform spatial-temporal feature extraction and classification on the processed signals, and an end-to-end deep learning model is established; and calling the deep learning model established by the deep learning sleep staging module by using the sleep staging output and analysis module, generating a sleep structure chart and sleep quality indexes through the deep learning model, and performing abnormal early warning.

The invention provides a sleep cycle analysis method and system for brain-computer interface data analysis, and belongs to the technical field of sleep staging, and the method specifically comprises the following steps: obtaining feature change user data without matching process combination, and combining sleep stage data with matching process combination of different feature change users, when it is determined that construction processing of a plurality of sleep staging models needs to be carried out, based on feature change user data and sleep stage data, falling into a matching process combination, of the feature change user in different sleep processes, a verification analysis strategy of the feature change user after sleep staging model updating processing is determined; on the basis of verification analysis processing results in different feature change users, feature change users whose matching process combinations change are determined, and feature change users needing to be subjected to sleep staging model construction processing are determined according to change data of the matching process combinations of the feature change users whose matching process combinations change. And the reliability of staged processing is improved.

The invention discloses a sleep data processing method and a sleep data processing device.The method comprises the steps that sleep data are obtained, the sleep data comprise a plurality of sleep data segments, and the duration of each sleep data segment is smaller than or equal to the first set duration; obtaining a first feature representation of each sleep data segment; performing attention transformation calculation based on the first feature representation of each sleep data segment to obtain a second feature representation of each sleep data segment; performing hole convolution on the second feature representations of the plurality of sleep data segments, and determining a sleep stage to which each sleep data segment belongs; and determining a sleep cycle corresponding to the sleep data based on the sleep stages corresponding to the plurality of sleep data segments. According to the technical scheme, the sleep data processing efficiency and accuracy can be effectively improved.

The invention provides a classification model training method and device, a prediction method and device, a medium and electronic equipment. The classification model training method comprises the following steps: acquiring sleep audio data of a patient and an actual sleep stage label associated with the audio data; performing feature conversion processing on the audio data to obtain audio frequency data; an initial sleep cycle classification model is trained based on the audio frequency data and the actual sleep stage label to obtain a trained sleep cycle classification model, and an input layer of the initial sleep cycle classification model is time-frequency single-channel 2D convolution. The sleep cycle classification model trained by the classification model training method can complete sleep cycle detection by collecting data in a non-contact mode, and has the advantages of single channel, non-contact mode, remote mode and the like.

This invention provides a sleep regulation method and system based on dynamic fit and collaborative rotation control, belonging to the field of smart home technology. The invention utilizes a macroscopic rotating skeletal device to slowly and continuously adjust the body's positional angle on the supporting platform, enabling the weight-bearing parts of the body to shift over time. A microscopic adaptive fit device dynamically compensates for local support height, ensuring continuous fit during rotation and reducing shear force and friction. Through multimodal perception and stage discrimination, intervention is performed only during the most suitable sleep stage, reducing the risk of awakening due to control actions. By introducing a graph structure spatiotemporal completion model, specific optimization is performed for scenarios with missing pressure data, restoring the usable pressure field and maintaining the effectiveness of subsequent control links. This solves the technical problems of insufficient static support, significant shear discomfort during rotation, and unstable control in scenarios with missing pressure data in existing technologies.

The invention discloses a control method of an intelligent mattress and a control system based on the intelligent mattress, and relates to the technical field of smart home. According to the embodiment of the invention, processing, separation and feature extraction are sequentially carried out on collected original composite signals to obtain heart rate variation features and micro body movement features; calculating a pressure index and a sleep stage according to heart rate variation characteristics and micro body movement characteristics; and finally, the intelligent mattress is controlled based on the processed target composite signal, the heart rate variation characteristics, the pressure index and the sleep stage, so that a complete closed-loop path from'non-sensing 'to'accurate evaluation' and then to'dynamic intervention 'is constructed, and active and intelligent adjustment on the emotion of the monitored object is realized.

The invention provides a sleep estimation system, a sleep estimation method, and a computer-readable recording medium. A sleep stage can be easily estimated. A sleep estimation device is provided with: a first acquisition unit that acquires blood flow data; a generation unit that generates a frequency spectrum of the blood flow data by performing frequency analysis processing on the blood flow data; and a first determination unit that determines the sleep stage of the subject on the basis of the frequency spectrum.

The invention discloses an automatic sleep staging method and system based on a double-branch parallel architecture. The method comprises the following steps: acquiring an electrocardiosignal and an electro-oculogram signal; preprocessing the electrocardio and electro-oculogram signals; obtaining high-dimensional feature representation through the convolutional neural network branch, and obtaining a sleep staging result of the convolutional neural network branch based on the high-dimensional feature representation; the sleep staging method comprises the following steps of: carrying out preliminary feature fusion on time domain-frequency domain features through a Transform branch, then carrying out convolution Token embedding, carrying out self-attention mechanism operation after splicing with classification Token, fusing two-dimensional feature information of a time domain and a frequency domain to obtain classification Token information, and obtaining a sleep staging result of the Transform branch based on the classification Token information; and splicing the sleep stage results output by the Transform branch and the convolutional neural network branch, and then outputting a final sleep stage result. According to the invention, the electrocardiosignal and the electro-oculogram signal of the subject can be synchronously collected, and sleep staging is carried out based on the collected physiological signal.

A device, system, and method for facilitating a sleep cycle in a subject, comprising selecting a waveform from a plurality of waveforms derived from brainwaves of at least one sleeping donor, wherein said waveform corresponds to at least one specific stage of sleep; and stimulating the subject with at least one stimulus, wherein said at least one stimulus is at least one of an auditory stimulus and a visual stimulus modulated with the selected waveform to entrain the brain of the subject with the selected waveform to facilitate sleep in the subject.

The application discloses a temperature control method, device, equipment and storage medium, comprising: obtaining sleep sign data of a target object collected by a monitoring device; determining a heart rate trend, a breathing trend and sleep state data of the target object during sleep according to the sleep sign data; and performing environment temperature regulation according to the heart rate trend, the breathing trend and the sleep state data. The above technical solution can meet the demand of physiological parameter changes of different sleep stages of the human body on the environment temperature, dynamically control a sleep environment air conditioning system, provide a comfortable sleep temperature environment, realize rapid sleep and improve the deep sleep proportion, and improve the flexibility and practicality of temperature regulation control, thereby ensuring the comfort of sleep of the target object.