123 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.

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.

According to the intelligent awakening system and method based on sleep monitoring, on the basis of wearable sleep monitoring equipment, by analyzing the sleep stage of a user in real time and combining strategies such as blue light awakening and vibration awakening, the user is awakened at a proper time, so that interference to sleep is reduced, and the sleep monitoring effect is improved. And the comfort of the awakening process and the mental state of the user are improved. Multi-channel physiological signals such as electroencephalogram signals, electro-oculogram signals and mandibular electromyographic signals are used, the advanced signal processing technology is combined, the sleep stage of a user can be accurately analyzed in real time, awakening is carried out at a proper time point, especially sudden awakening is avoided in the deep sleep (N3) stage and the rapid eye movement sleep (REM) stage, and the sleep state of the user is improved. And the sleep inertia is effectively reduced.

The invention relates to the technical field of intelligent bed early warning, and discloses an intelligent bed integrated AI monitoring and early warning method and system, and the method comprises the steps: collecting sleep-related data, and carrying out formatting and quantity stiffening processing to obtain target sleep data; constructing a sleep stage thermodynamic diagram, analyzing the sleep cycle characteristics of the intelligent bed user, analyzing the body movement frequency condition of the intelligent bed user in the sleep process by using the target sleep data, and analyzing the overall sleep characteristics of the intelligent bed user; analyzing the sleep quality of the intelligent bed user, and when the sleep quality is poor, analyzing cardiovascular health characteristics and respiratory system health characteristics of the intelligent bed user so as to analyze preliminary health state characteristics of the intelligent bed user; and collecting sleep adjustment related data after the intelligent bed user uses the adjustment equipment, analyzing the health state adjustment characteristics of the intelligent bed user, and performing sleep health early warning on the intelligent bed user in combination with the preliminary health state characteristics. According to the invention, the early warning reliability of the intelligent bed integrated AI can be improved when the number of influence factors is large.

The invention discloses a plateau tourism bedside intelligent interaction microenvironment adjusting system and method, and the system comprises a signal collection and transmission unit which is responsible for collecting bedside signals, carrying out the filtering processing of the bedside signals through an MCU, and transmitting the bedside signals to a cloud platform; the intelligent eyeshade data receiving unit is responsible for receiving the electro-oculogram, the eye movement and the blood oxygen collected by the intelligent eyeshade, transmitting the electro-oculogram, the eye movement and the blood oxygen to the MCU for processing, and transmitting the processed electro-oculogram, the cloud platform is responsible for receiving data from the signal acquisition and transmission unit and the intelligent eyeshade data receiving unit, and storing, analyzing and processing the data; the artificial intelligence unit is responsible for mining the data received by the cloud platform and identifying the physiological status, sleep quality and behavior habits of the tourists through a health behavioral theory and an artificial intelligence sleep staging algorithm; the regulation and control unit is responsible for analyzing an analysis result of the cloud platform and an instruction of a user; and the terminal equipment is responsible for providing a user interface.

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.

The invention relates to the technical field of sleep assessment, and discloses a sleep monitoring method, a breathing parameter control method and breathing equipment, and the method comprises the steps: obtaining an airflow rate signal of a user in a first time period; according to the airflow rate signal of the first time period, the breathing depth and the breathing rate of each breathing cycle are determined; processing the breathing depth and the breathing rate of each breathing cycle, and determining time domain features, frequency domain features and nonlinear features corresponding to the breathing depth and the breathing rate in a plurality of second time periods; based on the trained machine learning model and the time domain features, the frequency domain features and the nonlinear features corresponding to the breathing depths and the breathing rates in the multiple second time periods, the sleep states of the user in all the second time periods are determined, and the sleep states comprise the sober stage, the light sleep stage, the deep sleep stage and the rapid eye movement stage. According to the invention, sleep quality evaluation can be carried out without adding extra hardware and changing the sleep environment, and the accuracy of the determined sleep quality is improved.

The embodiment of the invention discloses a personalized dynamic sleep staging method and system. The method comprises the following steps: determining a user category of a user based on feature information of the user; and taking a basic sleep staging algorithm corresponding to the user category of the user in the branch algorithm library as a target sleep staging algorithm. And generating sleep staging data suitable for different user groups based on the target sleep staging algorithm and the current sleep data of the users. In addition, the method further comprises the steps that feedback information of the user is obtained, and an optimal sleep staging algorithm is determined based on the feedback information of the user, the current sleep data and the historical sleep data; and updating the target sleep staging algorithm into the optimal sleep staging algorithm, so that the target sleep staging algorithm can consider the real sleep state fed back by the user while considering the sleep data of the user, thereby ensuring the real-time accuracy of the generated sleep staging data.

The invention discloses a sleep staging detection method and system based on a smart watch, and the method comprises the steps: S1, obtaining the heart rate variability, triaxial accelerometer data and body movement intensity index signals of a wearer through a sensor of the smart watch, and carrying out the multi-source signal fusion and time synchronization; s2, carrying out noise reduction processing on the synchronized multi-source signal, and constructing a feature vector; s3, calculating a time sequence self-correlation feature of the feature vector; s4, based on a bidirectional long and short time memory network structure, performing time sequence modeling on the input feature vector and the time sequence self-correlation feature to obtain fused bidirectional feature representation; and S5, performing multi-classification processing on the fused bidirectional feature representation based on a Softmax classifier to realize accurate classification of sleep stages. According to the method, the heart rate variability signal and the three-axis acceleration signal can be fused, and the sleep state of the human body can be accurately recognized and classified in combination with the bidirectional long-short-term memory network and time sequence self-correlation feature analysis.

The present invention relates to a smart mattress that primarily uses non-contact sensors to measure the user's physiological data. Through a non-contact sensing method, it is capable of detecting REM sleep stages, monitoring heart rhythm, and measuring vital information such as respiration, as well as recording and storing physiological data such as bed-leaving movements and sleeping position. By connecting to a mobile device or a computer via a control box, all the recorded information can be displayed as a screen output or uploaded to a cloud database for storage. This enables long-term measurement of the user's physiological data or real-time monitoring, thus contributing to targeted patient care.

The embodiments of this specification disclose a personalized dynamic sleep staging method and system. The method includes determining the user category of a user based on the user's characteristic information. The basic sleep staging algorithm corresponding to the user category of the user in the branch algorithm library is used as the target sleep staging algorithm. Based on the target sleep staging algorithm and the user's current sleep data, sleep staging data suitable for different user groups is generated. In addition, the method also includes obtaining user feedback information, determining the optimal sleep staging algorithm based on the user's feedback information, current sleep data, and historical sleep data; and updating the target sleep staging algorithm to the optimal sleep staging algorithm, so that the target sleep staging algorithm can take into account the user's sleep data while also taking into account the actual sleep state reported by the user to ensure the real-time accuracy of the generated sleep staging data.

A sleep staging method based on semi-supervised learning belongs to the field of artificial intelligence and health monitoring, and comprises the following steps: preprocessing original PPG signals, extracting heart rate and respiration signals, calculating morphological and frequency domain features, extracting sleep staging related representation features, modeling short-time features and long-time changes of sleep, and outputting a classification result of each sleep stage; calculating supervised loss by using the labeled PPG data, predicting unlabeled PPG data to obtain a soft label, screening a high-confidence sample, and converting the soft label into a hard label to calculate unsupervised loss; calculating a confidence coefficient, calculating category center features of each sleep stage category, and performing feature adjustment; performing confidence weighting on the sample features and the category center features; and constructing positive and negative sample pairs to carry out semi-supervised contrast learning. According to the method, the generalization ability of the sleep staging model is enhanced, the sleep staging accuracy is improved, and the method can be applied to scenes such as family health management, auxiliary diagnosis and sleep monitoring.

The invention relates to a sleep monitoring method and device. The method comprises the following steps: acquiring sleep monitoring information of a user in a monitoring period, wherein the sleep monitoring information comprises electroencephalogram data, myoelectricity data and electro-oculogram data; the method further comprises the step of determining user sleep information based on the electroencephalogram data, the myoelectricity data and the electro-oculogram data in the monitoring period, and the user sleep information comprises sleep time information and sleep monitoring information in a sober sleep stage, a non-fast eye movement sleep stage and a fast eye movement sleep stage. In addition, the method further comprises the step of determining sleep time information and sleep monitoring information under three sub-stages of the non-fast eye movement sleep stage in the sleep information of the user based on the electro-oculogram data under the non-fast eye movement sleep stage. Through the mode, the sleep staging distribution condition of the user in the current monitoring period can be determined based on the multi-dimensional data monitored by the user in the current monitoring period, and the sleep conditions of the user in different monitoring periods can be accurately reflected.

The invention discloses a continuous relation extraction method combining awakening and sleeping memory circulation and experience iteration, and belongs to the technical field of information extraction. Comprising the following steps: inputting a new task into a model, expanding a new relationship instance by using a data enhancement technology, and constructing and synthesizing a short-term memory together with the task; screening instances by adopting a K-Means algorithm and storing the instances in a long-term memory; in the waking stage, the model updates the processing capacity of the model by quickly adapting to new relation samples, and the samples are incorporated into an experience pool; dynamically adjusting distribution of relation samples in a model learning process by using an experience iteration framework; in the sleep stage, the model experiences two sub-stages of non-rapid eye movement and rapid eye movement. In the NREM stage, the neural network replays the long-term memory in the hippocampus. In the REM stage, the model simulates dream data of a new relation, the model robustness is high, and the problem of disastrous forgetting in continuous relation extraction is effectively relieved, so that the stability and performance of the model are improved.

According to an embodiment, a computing device can include one or more processors and one or more computer-readable media that store instructions that, when executed by the processor(s), cause the computing device to perform operations. The operations can include obtaining a plurality of sleep stages associated with a sleep session of a user. The sleep session can be at least partially defined by an estimated bedtime of the user. The operations can further include identifying, in the plurality of sleep stages, one or more defined sleep stages indicative of a defined sleep state of the user. The operations can further include calculating a time before sound sleep (TBSS) metric based at least in part on the estimated bedtime of the user and a start time of the defined sleep stage(s). The operations can further include performing one or more operations based at least in part on the TBSS metric.

The invention provides a sleep monitoring method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a first type of sleep data and a second type of sleep data of a target object; the first type of sleep data is used for describing the physiological condition of the target object, and the second type of sleep data is used for describing the body movement condition of the target object; the first type of sleep data and the second type of sleep data are obtained based on a non-contact device; predicting the sleep stage of the target object by using the first type of sleep data to obtain a first prediction result, and predicting the sleep stage of the target object by using the second type of sleep data to obtain a second prediction result; obtaining a sleep monitoring result of the target object according to the first prediction result and the second prediction result; the sleep monitoring result is used for indicating the sleep stage of the target object. According to the method and the device, the problems of high cost, inconvenience and inaccuracy of sleep monitoring in related technologies are solved.

The invention discloses a patient sleep awakening three-classification monitoring method and device based on a machine learning model, equipment and a medium, and the method comprises the steps: collecting acceleration data and activity counting and heart rate data of a subject, and carrying out the preprocessing of the collected data; extracting data features based on the preprocessed data, wherein the data features comprise a motion feature, a heart rate feature and a clock proxy feature; the extracted data features are input into a classification model, prediction state data of each Epoch are output, each row in the prediction state data comprises an Epoch starting timestamp, a real sleep staging label and a three-classification prediction result of the model, and therefore the prediction state data of each Epoch can be obtained. According to the method, the three-classification prediction accuracy of awakening, non-fast eye movement sleep and fast eye movement sleep can be effectively improved, and the method can be widely applied to sleep monitoring.

The invention discloses a sleep staging method and system based on self-adaptive trigger time characteristics, belongs to the technical field of plateau sleep stage staging, and solves the problem that accidental events in sleep cannot be effectively identified and extracted when multi-mode signal characteristics are extracted by the existing method. The method comprises the following steps: judging whether a physiological monitoring signal is an accidental event associated signal based on a preset accidental event judgment mechanism, calculating an accidental event value in a single group of time windows by adopting the accidental event judgment mechanism, and identifying and extracting the accidental events by a feature extraction model. The sleep stage staging model analyzes and processes the feature fusion result and outputs a sleep stage staging result; according to the method, an accidental event judgment mechanism with a time decay function is introduced, event data are screened, features are extracted from PPG signals and the event data, the features are input into the sleep stage staging model for sleep stage prediction, and meanwhile, the accuracy of feature extraction is improved by combining a dynamically adjusted trigger threshold value.

A first bed includes a first mattress, a first pressure sensor, and a first controller in data communication with the first pressure sensor, the first controller configured to receive pressure readings and to transmit the first pressure readings to a remote server. The system further includes a second bed that includes a second mattress having an inflatable chamber. The system further includes a second pressure sensor in fluid communication with the second mattress inflatable chamber to sense pressure applied to the second mattress of the inflatable chamber. The system further includes a second controller in data communication with the second pressure sensor, the controller configured to receive the one or more sleep-state classifiers and to run the received sleep-state classifiers in order to collect one or more sleep-state votes. The second controller is further configured to determine a sleep-state and operate the bed system according to the determined sleep-state.

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

To improve the quality of sleep. A control device includes a determiner that determines a stage of sleep of a subject on the basis of biological information on the subject, and a controller that controls a sleep induction device including a beat generator that generates a binaural beat. The controller causes the beat generator to generate a binaural beat having a frequency lower than 1.0 Hz at least in the stage of sleep of the subject.

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 provides a sleep staging method based on non-contact electrocardiosignals and body movement signals. Firstly, electrocardio and body movement signals are collected on a bed sheet in a non-contact mode through a capacitive coupling type sensor and a pressure sensor, preprocessing such as filtering, oversampling and signal segmentation is carried out, and a data set is formed. Secondly, a CNN-biLSTM-sleep network model is constructed, the model comprises two paths of input of electrocardiosignals and body movement signals, a CNN feature extraction module and a bidirectional LSTM sequence information extraction module, and six sleep stages of REM, N1, N2, N3, WK and LV are output; and then training and optimizing the model by using the training set. And finally, outputting a classification result through a verification set test model. The method does not need extra feature extraction, is suitable for a non-contact electrocardio acquisition scene, and can effectively recognize sleep stages.

The present disclosure relates to the technical field of brain wave signal processing, and more specifically, to a sleep staging method, apparatus, device, and computer-readable storage medium. The sleep staging method includes: determining the power spectral density of the brain wave signal of each sleep frame during sleep; performing dimensionality reduction processing on the power spectral density to obtain a reduced dimensionality data set; determining multiple characteristic signals of the brain wave signal of the sleep frame based on the power spectral density; for each sleep frame, performing kernel density estimation on the reduced dimensionality data set with weights determined according to each characteristic signal to obtain a probability density corresponding to the characteristic signal; and determining that the sleep frame belongs to the sleep stage corresponding to the characteristic signal when the probability density meets the preset condition corresponding to the characteristic signal, wherein different characteristic signals correspond to different sleep stages.

The embodiment of the invention provides a sleep quality evaluation method and device, electronic equipment and a storage medium, and can obtain current sleep parameters of a target person under the condition that the target person is in a sleep state. And inputting the current sleep parameters, the exercise amount data of the target person in the pre-sleep preset time period and the current health condition data of the target person into a pre-trained neural network model for processing, so that the duration of a core sleep stage, the duration of a deep sleep stage and the duration of a rapid eye movement sleep stage can be determined. According to the method and the device, the duration ratio of the three sleep stages can be determined by combining the total sleep duration of the target person, so that the sleep quality of the target person can be determined, and the technical problem that the sleep quality of the user cannot be effectively evaluated in the prior art is solved.

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.

The invention relates to a data processing method, device and system of a sleep staging algorithm based on a BCG signal. According to the method, a BCG signal sent by an optical fiber sensor mattress system and a sleep staging signal sent by PSG equipment are received; generating a data set according to the BCG signals, and performing feature data extraction on the BCG signals in the data set to obtain a feature set; filtering correlation deletion, L1 regularization logistic regression and a random forest algorithm are sequentially used for carrying out feature screening on the feature set, and a feature set after feature screening is obtained; performing feature standardization on the feature set after feature screening to obtain a feature set after feature standardization; dividing the feature set with the standardized features into a training set and a test set according to the proportion, inputting the training set and the test set into the sleep staging model for training to obtain a sleep staging test result, evaluating the sleep staging model in multiple dimensions, and if the evaluation is passed, obtaining a convergent sleep staging model, and the sleep staging method has the advantage of improving the accuracy and reliability of sleep staging.