33 results about "Sleep behavior" patented technology

The invention discloses a sleep behavior monitoring method and device, a storage medium and a terminal, and the method comprises the steps: obtaining a cover image corresponding to a sleep event video, the sleep event video being a video collected for a sleep event in a current scene through a sleep monitoring device; the cover image is input into a large action recognition model, sleep actions output by the large action recognition model based on the cover image are determined, and the sleep actions are normal sleep actions or quilt kicking actions; and when the sleep action is a quilt kicking action, generating first notification information corresponding to the quilt kicking event, and sending the first notification information to a target terminal bound with the sleep monitoring equipment, so that the target terminal displays the first notification information. And the server can analyze the cover image by using the action recognition large model, so that the sleep action of the user is obtained. In this way, when the user kicks the quilt, the corresponding prompt notification information can be sent to the bound target terminal in time, and the sleep experience of family members is improved.

The invention belongs to the technical field of traditional Chinese medicine compound preparations, and particularly relates to a qi-regulating, phlegm-reducing and nerve-calming paste, a preparation method thereof and application of the paste in preparation of a medicine for treating Parkinson's disease combined with rapid eye movement sleep behavior disorder. The paste is prepared from the following raw materials in parts by weight: 9-30 parts of semen ziziphi spinosae, 15-30 parts of Chinese yam, 10-15 parts of poria with hostwood, 9-30 parts of semen coicis, 3-10 parts of pericarpium citri reticulatae, 3-10 parts of fingered citron, 10-20 parts of sesame, 10-20 parts of rock candy and 3-5 parts of maltose. By nourishing the heart and strengthening the spleen to consolidate the root, regulating qi and reducing phlegm to relieve stagnation and tranquilizing and sedating the physique to return to the soul, the three methods are combined to achieve the purposes of tonifying without stagnation, eliminating without injury, recovering the heart and the spleen, smoothing phlegm and qi and self-hiding the soul.

The invention discloses an unattended sleep quality analysis method and system, and the method comprises the steps: obtaining sleep related signals from the surrounding environment of a user through a non-contact sensor technology, carrying out the real-time capturing of body movement frequency characteristics and breathing rhythm characteristics, and obtaining a preliminary sleep behavior data record; according to the initial sleep behavior data record, filtering the body movement frequency signal and the breathing rhythm signal by adopting a signal denoising processing method, eliminating environmental noise interference, and determining an optimized sleep signal data set; aiming at the optimized sleep signal data set, performing feature extraction by utilizing a pre-established sleep stage classification model, analyzing regular changes of a body movement mode and a breathing law, and judging a sleep stage distribution condition of the user; according to the sleep stage distribution condition, the sleep quality of the user is graded and evaluated in combination with the preset threshold range, and accurate sleep stage judgment and sleep quality evaluation can be achieved.

The invention belongs to the technical field of medical health monitoring and behavior intervention, and discloses a pre-school autism sleep personalized intervention system based on AI behavior analysis, which comprises a behavior acquisition unit, an environment monitoring unit, a living habit management unit, a fusion analysis unit, an intervention execution unit, a self-adaptive optimization unit and a family interaction unit, according to the system, body movement, heart rate, skin electricity and sleeping posture information of children and environment parameters such as illumination, noise and temperature and humidity of a bedroom are synchronously collected, diet and exercise balance indexes are combined for fusion analysis, and behavior risk indexes are generated; a personalized intervention scheme is automatically generated according to the risk level, and intervention is implemented through light, sound and visual guidance modes. The system dynamically adjusts analysis parameters according to an intervention result, realizes closed-loop correlation analysis and continuous regulation and control of sleep behaviors, environment changes and parent feedback, and is suitable for individualized sleep management of children with autism.

The invention discloses a sleep scene-oriented bimodal fusion radar dynamic attitude identification method and device. The method comprises the following steps of: S1, acquiring and demodulating radar original data; s2, double-window feature construction of a velocity mode and a structural mode; s3, carrying out speed-guided dual-mode time alignment and feature reweighting; s4, the dynamic sleeping postures are output in a classified mode. According to the method, the limitation of an existing single-mode radar processing method in the aspects of weak action visibility, bedding shielding robustness and directivity discrimination capability is broken through, and a new implementation mode is provided for whole-process dynamic posture monitoring of complex sleep behaviors.

The invention discloses a data processing method and system based on insomnia behaviors, and relates to the technical field of health monitoring, and the method comprises the steps: collecting sleep behavior data of a user through a smart bracelet and a sleep diary application; performing exception labeling on the sleep behavior data according to a preset normal range; evaluating an insomnia risk level according to the number of the abnormal labels; corresponding processing flows are triggered according to different risk levels. The system comprises an acquisition module, an evaluation module and a processing module. According to the invention, automatic assessment and graded intervention of the insomnia risk are realized, and the accuracy of insomnia monitoring and the pertinence of intervention are improved.

The application discloses a sleep behavior recognition method and system under security monitoring, and a medium, and the method comprises the following steps: preparing a video sequence dataset; adopting a convolutional neural network to perform feature coding on the video sequence dataset, and coding image frame features; adopting a video segmentation strategy to segment the image frame features, and calculating segment-level features of a current segment; stacking the segment-level features of each segment in time sequence to form video-level features of the video sequence, and taking the video-level features as inputs of a time recurrent neural network module added with a spatiotemporal non-local attention mechanism; and passing output feature maps of the spatiotemporal non-local attention mechanism through a full connection layer of the time recurrent neural network module, and completing final sleep behavior recognition. The improved CNN-LSTM network architecture realizes sleep behavior detection under monitoring video.

The invention relates to the technical field of sleep health monitoring, in particular to a sleep apnea-oriented monitoring and feedback system, which comprises a sleep sensing module for acquiring physiological parameters, sleep behavior data and sleep environment parameters in sleep; the sleep breathing state analysis center performs sleep apnea type identification, severity evaluation and correlation analysis on a sleep cycle and a breathing event; the self-adaptive intervention feedback module dynamically adjusts intervention parameters according to the real-time analysis result and the physiological tolerance of the user and generates a basic feedback strategy; the multi-level risk assessment unit assesses a night hypoxia risk, a sleep structure disorder risk and a cardiovascular stress risk in real time, quantifies a risk weight, and generates a target feedback strategy in combination with historical sleep data of the user; and the cross-end cooperation unit links the household sleep monitoring terminal and the medical early warning terminal according to the intervention execution and state return instruction, executes feedback intervention and returns data. Therefore, the problems of low data accuracy, insufficient adaptability and the like in the prior art are solved.

The invention provides a processing module and system for glucose concentration before and after sleep, and the processing module is used for detecting the glucose concentration of a to-be-detected object and giving guidance information. The processing module is configured to determine the fluctuation type of a glucose concentration curve between the pre-sleep state and the post-sleep state on the basis of glucose data including the time-varying glucose concentration between the pre-sleep state and the post-sleep state of the to-be-detected object, and the fluctuation type reflects the variation trend of the glucose concentration between the pre-sleep state and the post-sleep state; a fluctuation type is obtained based on the glucose concentration, the maximum glucose fluctuation amplitude and the sleep glucose fluctuation amplitude obtained based on the glucose data at the time before sleep, and guidance information or suggestions related to the sleep behavior are generated according to the fluctuation type. Therefore, the time for seeking suggestions of doctors can be reduced.

The invention relates to the technical field of data processing, and particularly provides an environment adjusting method, system and device based on sleep state monitoring and a medium, the method comprises the steps that state data are collected through a multi-mode non-inductive sensing network, and the state data comprise bedding pressure data, vital sign data and environment parameters; extracting key features from the state data, wherein the key features comprise an off-bed tendency feature, a sleep stability feature and an early awakening feature; analyzing the key features through a machine learning model, and predicting sleep behavior intentions of the user, including an off-bed tendency, an awakening precursor and an early awakening risk; triggering an environment actuator according to the predicted intention, and adjusting at least one parameter of illumination, temperature and humidity; recording user state data before and after intervention, and optimizing a decision threshold and intervention parameters through a reinforcement learning algorithm; the invention provides an unprecedented safe and comfortable sleep health management solution for sensitive people with cognitive impairment and the like.

The application belongs to the technical field of nematode culture and sleep detection, and particularly relates to a simple single nematode culture model and a sleep monitoring method thereof. The model fixes a single nematode in a culture chamber for culture, and the culture chamber is divided into two layers, the upper layer is a micro cavity for accommodating the nematode, and the lower layer is a culture medium (dry base). A nematode dry bacteria solution is arranged in the culture medium for providing growth of the nematode. The application discards the problem that the sleep behavior of the nematode cannot be monitored due to the use of liquid or droplet delivery in microfluidics. Meanwhile, the problem that the nematode cannot be fixed and a single nematode cannot be analyzed in the sleep monitoring model of Caenorhabditis elegans is improved, the throughput and accuracy are improved, the sleep behavior of the nematode can be directly analyzed by using wormlab, naked eye observation is not needed, the labor and material costs are reduced, and the scientificity and effectiveness of the experiment are improved.

The invention belongs to the technical field of biological medicines, and particularly relates to application of an mTOR inhibitor to improvement of sleep disorders. Aiming at the defects of the existing hypnotics, the invention finds that the sleep disorder can be improved by using the Rapamycin through researching a new sleep pathway target. The Rapamycin disclosed by the invention is a non-addictive sleeping drug, and can induce natural sleep behaviors; compared with an existing clinical hypnotics which has the side effect of greatly increasing the death rate, the Rapamycin administration disclosed by the invention is beneficial to improving the sleep quality and prolonging the life of animals at the same time. The invention discloses a remarkable effect of Rapamycin in the aspect of improving the sleep disorder, and the discovery has important significance in improving the sleep quality of a patient and preventing the progress of diseases, so that the Rapamycin has a wide medical prospect and a remarkable economic value.

Slatted base (1) for a reclining and / or seating furniture, comprising: - at least two zone elements (2), wherein at least one slat (3) is arranged on each of the zone elements, - a frame (4) to accommodate the at least two zone elements (2), - at least one support element (5) arranged on the frame (4) for supporting the at least two zone elements (2) along a longitudinal extension (L) of the frame (4), - at least one electrically operated actuating element (6) for adjusting the at least one support element (5), wherein the sinking depth of the reclining and / or seating furniture can be adjusted by adjusting the support element (5), and - a control device (7) for controlling and / or regulating the at least one actuating element (6), characterized in that at least one sensor (8) is provided for determining a person's sleep behavior, wherein the control device (7) is designed to control and / or regulate the at least one actuating element (6) depending on the sleep behavior determined by the at least one sensor (8).

The present disclosure relates to a system and method for improving sleep quality, and particularly, to a system and method for improving sleep quality that can induce correct sleep behavior of a user by measuring objective sleep quality in a wearless state and measuring and analyzing subjective sleep quality in a user-friendly manner. The system for improving sleep quality according to an embodiment of the present disclosure includes: a mattress configured to measure objective sleep quality information of a user in a wearless type; a behavior correction assistance device disposed at a predetermined distance from the mattress; and an AI speaker configured to integrate and analyze objective sleep quality information measured by the mattress and subjective sleep quality information determined through Q&A with the user.

The invention belongs to the field of food-borne active peptides, and particularly relates to spinach hexapeptide for increasing a rapid eye movement sleep period and application of spinach hexapeptide. The invention relates to an application of spinach hexapeptide in preparation of a product for increasing a rapid eye movement sleep period. The sequence of the spinach hexapeptide is Tyr-Pro-Leu-Asp-Leu-Phe. The oligopeptide sequence is derived from spinach or synthesized by a chemical synthesis method, and the activity of the oligopeptide in the rapid eye movement sleep period is verified by monitoring natural sleep behaviors of mice through electroencephalogram. The spinach hexapeptide YPLDLF can significantly increase the REM duration of a mouse in the natural sleep period, and can be used for preparing food, health care products or medicines with the sleep aiding effect.

The application is suitable for the technical field of breathing machines, and particularly relates to a breathing machine control method and system based on sleep behavior data. The method comprises the following steps: obtaining behavior data and first operation data; wherein the behavior data comprises first behavior data for reflecting the movement of the abdominal cavity diaphragm of a user during sleep and second behavior data for reflecting the breathing movement of the user during sleep; determining an adjustment gain parameter based on the first behavior data; identifying sleep feature data for reflecting the actual required breathing machine tidal volume adjustment requirement of the user during the sleep process from the first operation data based on the second behavior data and the adjustment gain parameter; and generating second operation data based on the sleep feature data and the adjusted first operation data after the adjustment of the first operation data based on the adjustment gain parameter. The method makes the tidal volume of the breathing machine in line with the sleep habits of the user during night sleep, thereby improving the use experience of the breathing machine.

The invention relates to the technical field of behavior verification, in particular to a dynamic behavior compliance verification method and device based on AI multi-mode recognition, electronic equipment and a storage medium, and the method comprises the steps: inputting real-time video data to a behavior compliance verification model, and obtaining a behavior compliance verification result, the behavior compliance verification model comprises a personnel departure and sleep recognition model and a security channel anomaly recognition model, inputting real-time video data to the personnel departure and sleep recognition model, extracting corresponding attitude space-time sequence features, recognizing personnel departure and sleep behaviors, inputting real-time video data to the security channel anomaly recognition model, and recognizing personnel departure and sleep behaviors. And obtaining a safety channel abnormity identification result. According to the invention, the behavior compliance verification can be carried out from the two directions of personnel off-post and sleep-post identification and security channel abnormity identification only by accessing the real-time video data collected by the original camera without transforming the original camera, so that the accurate verification of the behavior compliance in the service process of the business hall is realized, the operation efficiency is improved, and the manual intervention is reduced.

AI-DRIVEN INTELLIGENT SLEEP BEHAVIOR SENSING DEVICE

The present application relates to the technical field of data monitoring and analysis, and particularly relates to a personalized sleep suggestion generation system based on long-term sleep diary data. Daily sleep behavior data of each observation period in a preset time coordinate system is obtained, including bed, wake, sleep and bed exit events; a work-rest window is determined based on the bed and bed exit time points in the data, which is consistent with the actual work-rest rules of the user; then the matching degree of actual effective sleep and the work-rest window is accurately evaluated, and the matching cost of each work-rest window is obtained; the distribution of wake events is analyzed, and the wake disturbance is calculated; the deviation accumulation of sleep duration and preset requirements is analyzed, and the duration compression penalty coefficient is calculated; these indicators provide a basis for selecting the optimal window; then the optimal window is selected by comprehensively considering the various indicators, and the bed and bed exit events of the next observation period are dynamically adjusted according to the difference between the optimal window and the data in the latest observation period, so as to assist the user in maintaining good sleep habits and improving sleep quality.

The present application relates to the technical field of health monitoring, and discloses a home health risk assessment method and system based on night sleep activity. The present application collects night sleep physiological data and night activity trajectory data of a user through a sign monitoring module under a mattress and a night behavior trajectory module in a residence, and uploads the data after time stamp alignment by a gateway. Abnormal physiological values are cleaned out, sleep-behavior correlation features are constructed, and composite features such as difficulty falling asleep, heart rate sudden change and apnea are extracted. The risk of heart, respiration and cognitive decline is assessed in different dimensions, and long-term risk aggravation warning can be generated through 30-day personal dynamic baseline and linear regression significance analysis. The present application solves the problems of data isolation, rough index and cognitive assessment blind area in the prior art, realizes non-invasive and accurate early warning of home health, and is suitable for home care monitoring of the elderly.

The invention relates to the field of animal models, in particular to a preparation method of a fast eye movement sleep behavior disorder animal model with cytopathology specificity, which comprises the following steps: injecting adeno-associated virus for inducing neuronal overexpression alpha-synuclein into a capped karyobrain area on the back and the outer side of a non-human tested animal, and injecting the neuron-derived extracellular vesicles or the oligodendroglia-derived extracellular vesicles into the capped karyocerebral region on the back outer side of the non-human tested animal again. The model prepared by the method disclosed by the invention has the disease characteristics of fast eye movement sleep and has the characteristic that Parkinson-like phenotype is transformed. The model disclosed by the invention can be used for researching the etiology and pathological mechanism of transformation of rapid eye movement sleep behavior disorder to Parkinson's disease or multi-system atrophy and screening prevention and treatment drugs, and has remarkable significance for clinically providing effective drug treatment targets and prevention and treatment means.

The invention provides a millimeter wave radar sleep behavior spectrogram enhancement method based on trajectory mapping, and belongs to the technical field of sleep monitoring. Obtaining a distance time matrix and a Doppler time matrix through fast Fourier transform and short-time Fourier transform; and estimating the azimuth angle and the distance of the target based on the distance-time matrix, constructing an actual track and a reference track, calculating a correction ratio according to the displacement of the two tracks in adjacent time windows, and representing the scale deviation between the actual speed and the radial speed. On the basis of the time dimension of a Doppler time matrix, nonlinear remapping is carried out on a Doppler axis according to a main energy peak and a correction ratio, speed scale deviation caused by azimuth angle changes is compensated, and the consistency of sleep behavior characteristics at different observation angles is achieved.

The application discloses a sleep quality analysis method and system for unattended sleep, sleep-related signals are acquired from the surrounding environment of a user through non-contact sensor technology, real-time capture is performed on body movement frequency characteristics and breathing rhythm characteristics, and preliminary sleep behavior data records are obtained; according to the preliminary sleep behavior data records, a signal denoising processing method is used to filter body movement frequency signals and breathing rhythm signals, environmental noise interference is eliminated, and an optimized sleep signal data set is determined; for the optimized sleep signal data set, a pre-established sleep stage classification model is used for feature extraction, regular changes in body movement patterns and breathing regularity are analyzed, and sleep stage distribution of the user is determined; and according to the sleep stage distribution, the sleep quality of the user is graded and evaluated in combination with a pre-set threshold range, so that accurate sleep stage determination and sleep quality evaluation can be realized.

The invention belongs to the technical field of food-borne active peptides, and particularly relates to application of wheat pentapeptide in preparation of sleep aiding products, and the sequence of the wheat pentapeptide is Tyr-Pro-Ile-Ser-Leu. The oligopeptide is derived from wheat or synthesized by a chemical synthesis method, and the sleep-aiding activity of the oligopeptide is verified by monitoring natural sleep behaviors of mice through electroencephalogram. The pentapeptide YPISL can obviously reduce the wakefulness rate of mice in the sleep period and increase the NREM ratio, shows high sleep-aiding activity and can be used for preparing food, health care products or medicines with the sleep-aiding effect.

The present application relates to the technical field of sleep detection analysis, and particularly relates to a bed mattress type non-inductive sleep detection method fusing millimeter wave radar and pressure sensing, which comprises the following steps: through the combination of millimeter wave radar and pressure sensor, radar echo and body pressure distribution signals of a user are synchronously acquired to provide a data source for subsequent analysis; through the processing of radar and pressure signals respectively, vital sign initial signals, micro-motion trajectories, pressure center trajectories and pressure fluctuation signals are effectively extracted to enhance the analysis capability of physiological and motion characteristics; through the dynamic selection of signal processing strategies according to body motion fusion confidence, the optimal data processing under different physiological states is ensured; through trajectory coding, clustering and body motion syntax library matching, the recognition of macro body motion such as turning over and sitting up is realized to enrich the sleep behavior analysis dimension; through the construction of sleep depth potential field, the simulation of sleep state evolution is realized by combining physiological parameters and body motion types to realize the dynamic discrimination of sleep stages.

The application discloses a kind of training method and system of depression intervention model based on deep knowledge graph, it is related to intelligent medical treatment and artificial intelligence technical field, the method is by obtaining the medical record text of patient, emotional language interaction data, sleep behavior data and physiological monitoring data.Data are carried out semantic analysis and entity relationship extraction, construct depression intervention knowledge graph, and embedding representation learning is carried out based on graph neural network, generates patient state vector.Patients state vector is input into intervention strategy generation model, and generates personalized intervention scheme prediction result.Based on prediction result, rehabilitation training is carried out, and intervention strategy is iteratively optimized through patient feedback.The method realizes the dynamic association of symptom, intervention and situation, and can continuously optimize personalized intervention scheme according to curative effect feedback, improve the rehabilitation effect of depression symptom and sleep disorder.

The present application relates to the technical field of resting effect recognition, and particularly relates to a resting effect explainable recognition method based on a wearable smart device. The method comprises a PPI data acquisition and preprocessing step, a training data set construction step, a deep learning model and training step, and a result explanation step. The deep learning model and training step comprises inputting PPI data and CR rhythm, completing double-channel feature decoupling extraction in an encoder layer, realizing rhythm perception modeling through a dynamic physiological graph attention layer, and outputting a decision through a classifier layer. The result explanation step explains the resting effect classification through deep learning model node data and sleep behavior CR rhythm. The present application can obtain neurophysiological and behavioral explanations of the resting effect from the contrast between autonomic nervous circadian rhythm and work and rest behavior.

The invention relates to a sleep behavior detection method and device, electronic equipment, a vehicle and a program product. The method comprises the steps that image feature information is extracted from an in-vehicle image, the image feature information comprises first position information of a detection object in the in-vehicle image and second position information of an environment article in the in-vehicle image, then the image of the detection object is segmented from the in-vehicle image according to the first position information and the second position information, and the image of the detection object is extracted from the in-vehicle image. And based on the image of the detection object, identifying whether the detection object has a sleep behavior. According to the scheme provided by the invention, the image of the detection object can be accurately segmented from the in-vehicle image based on the first position information of the detection object in the in-vehicle image and the second position information of the environmental article in the in-vehicle image, and sleep behavior identification is carried out on the image of the detection object. Therefore, the influence of background fluctuation on the eye region feature extraction process is avoided, and the accuracy of sleep behavior detection is improved.

The invention relates to the technical field of insomnia recognition, in particular to an insomnia feature extraction and recognition method and system based on deep learning, and the method comprises the steps: collecting the brain function changes of a subject under different task conditions through designing a task experiment, obtaining the fMRI data of the subject in a resting state and the task experiment through functional magnetic resonance imaging, and obtaining the fMRI data of the subject; obtaining resting state fMRI data and task state fMRI data; collecting personalized data of a subject, wherein the personalized data comprises sleep behavior data, psychological assessment data and gene data; performing dynamic segmentation on the task state fMRI data and the resting state fMRI data through a dynamic function connection analysis model, and calculating dynamic function connection matrixes at different time points; constructing a multi-level insomnia feature extraction model to carry out insomnia feature extraction; and constructing a multi-task insomnia recognition model, carrying out automatic insomnia recognition and classification based on the insomnia characteristics and the dynamic function connection matrix, and predicting the insomnia severity.

The invention relates to the technical field of sleep supervision, and discloses a cognitive behavior therapy-based digital sleep intervention system, which comprises a sleep environment control module, a sleep process monitoring module and a sleep forced intervention module, sleep demand intelligent judgment of a user sleep process is performed based on real-time state image information of the user sleep process in combination with an artificial intelligence algorithm and standard image information of invalid sleep behaviors of the user stored in a standard manner, so that the invalid sleep behaviors of the user sleep process are intelligently and accurately recognized based on AI; user sleep is scientifically managed based on cognitive behavior therapy; personalized matching of sleep forced intervention schemes for invalid sleep behaviors in a user sleep process is performed on the basis of identity text information of a to-be-sleeping user, an intelligent search algorithm and scientifically-stored invalid sleep forced intervention scheme information of a home user, so that a user object scientifically selects invalid sleep getting-up intervention measures of the user.