80 results about "Sleep analysis" patented technology

The invention relates to the technical field of health monitoring, in particular to a deep sleep analysis and early warning system integrated with heart rate monitoring, which comprises a physiological parameter acquisition module, an environmental parameter acquisition module, a deep sleep analysis module, an anomaly detection and early warning module, a dynamic feedback and optimization module, a personalized suggestion module and a data storage and analysis module. The physiological parameter acquisition module acquires physiological parameters of a user, the environmental parameter acquisition module acquires environmental parameters, the deep sleep analysis module identifies deep sleep features based on a multivariable data analysis algorithm and evaluates sleep quality, and the anomaly detection and early warning module detects physiological anomaly and triggers early warning. The dynamic feedback and optimization module optimizes model parameters according to user feedback, the personalized suggestion module generates sleep improvement suggestions, and the data storage and analysis module stores data. According to the invention, the deep sleep state of the user can be comprehensively analyzed, a personalized health management and sleep optimization scheme is provided, and the sleep quality of the user is improved.

The invention provides a sleep staging analysis method and device based on electroencephalogram signals, and belongs to the technical field of data analys.The method specifically comprises the steps that staging deviation time periods in sleep analysis results are determined according to the sleep staging results corresponding to different credible monitoring positions; determining the matching conditions of the electroencephalogram signals of different credible monitoring positions at different moments and the monitoring data of other dimensions, and determining the abnormal change moments of different credible monitoring positions by combining the deviation conditions of the electroencephalogram signals at different moments and other credible monitoring positions; according to the distribution data of the abnormal change moments of the different credible monitoring positions in the staging deviation period and the early period of the staging deviation period, when it is determined that the reference monitoring position exists in the staging deviation period, the sleep staging result in the staging deviation period is determined through the reference monitoring position, and the accuracy of the sleep staging result 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 discloses an ergonomic pillow intelligent recommendation method and system, and the method comprises the steps: collecting a head and neck sleep video when a target user uses each candidate pillow, and obtaining a sleep analysis image of each ergonomic pillow; performing posture adjustment process image rejection on the ergonomic pillow sleep analysis image to obtain a head and neck posture keeping image of each candidate pillow; wherein the posture adjustment process image elimination is designed to screen out process images of posture adjustment of a target user; according to the user information of the target user and the position relationship between the key parts in each head and neck posture keeping image, carrying out virtual and real matching analysis to obtain each pillow recommendation parameter, and calculating a target similarity index of each candidate pillow; and obtaining a pillow recommendation list of the target user according to a sorting result of all the target similarity indexes. According to the intelligent recommendation method and system for the ergonomic pillow provided by the embodiment of the invention, the accuracy of pillow recommendation is improved.

The embodiment of the invention provides a sleep staging analysis method and system based on electroencephalogram signals and a medium. The method comprises the steps that the electroencephalogram signals are obtained, feature extraction is conducted on the electroencephalogram signals, electroencephalogram features are obtained, and the electroencephalogram features are preprocessed; inputting a sleep analysis model based on the preprocessing result, outputting sleep stage data, and performing stage division on sleep based on the sleep stage data to obtain a plurality of sleep stage analysis results; comparing the sleep period analysis result with set standard information to obtain a staging deviation ratio, and judging whether the staging deviation ratio is greater than or equal to a set deviation ratio threshold value or not; if yes, adjusting model parameters of the sleep analysis model based on the correction information; if yes, evaluating the sleep effect based on the sleep evaluation information; the sleep state is subjected to staging processing by analyzing the electroencephalogram signal, so that the sleep effect is analyzed according to different staging data, and the analysis precision of the sleep state is improved.

The invention relates to the technical field of sleep quality, and discloses an intelligent sleep state analysis and dynamic optimization system based on multi-dimensional biological data, the system comprises a sleep monitoring module, a sleep analysis module, an environment optimization module and a data processing module, the sleep monitoring module collects biological data and environment data according to a sensing device, and the data processing module processes the biological data and the environment data. The sleep analysis module obtains a sleep state by adopting a threshold logic algorithm based on the target biological data and predicts a biological clock sequence of the current sleep state by adopting an LSTM model according to historical sleep state data, and the environment optimization module adjusts a light mode and a playing mode of the sleep equipment based on the environment data. The vibration mode of the sleep equipment is changed according to the time of the change trend, the data processing module judges whether the light mode of the sleep equipment is correct or not according to historical adjustment data, if not, the light mode of the sleep equipment is re-determined through the clustering algorithm, and the sleep environment is dynamically adjusted, and the sleep quality is improved.

The invention discloses a sleep analysis system, method and device based on multi-mode PSG data and a medium, and relates to the technical field of artificial intelligence. The system comprises a data acquisition module used for acquiring multi-modal PSG data of a target object; the data processing module is used for preprocessing the multi-mode PSG data; the sleep recognition module is used for performing multi-dimensional prediction on the sleep condition of the target object according to the preprocessed multi-mode PSG data; wherein the sleep recognition module is provided with a sleep staging model for recognizing sleep stages, a breathing event recognition model for recognizing breathing event types and a micro-awakening recognition model for recognizing micro-awakening events; and the data statistics module is used for integrating and analyzing the preprocessed multi-modal PSG data and the multi-dimensional prediction result of the sleep recognition module to obtain a sleep analysis report of the target object. According to the scheme, the multi-mode PSG data is fully utilized to realize automatic joint identification of multiple sleep tasks in the same system.

The present invention provides a sleep quality analysis method, system, storage medium and electronic device, the method comprising the following steps: obtaining a multimodal signal of a test subject when sleeping; the multimodal signal comprises an electroencephalogram signal, a pulse signal and a body movement signal; preprocessing the multimodal signal to obtain the multimodal features of the test subject; calculating and obtaining multiple types of sleep analysis parameters of the test subject based on the multimodal features; calculating and obtaining the corresponding sleep score coefficient for each threshold interval to which the sleep analysis parameter belongs; and calculating and obtaining the sleep quality analysis value of the test subject based on the multiple types of sleep score coefficients. The sleep quality analysis method, system, storage medium and electronic device of the present invention are configured to achieve accurate analysis of sleep quality based on multimodal signals.

The present invention relates to the field of data processing technology, and in particular to an electroencephalogram (EEG) signal processing system for sleep analysis. First, EEG signals from different areas of the brain are obtained, and the initial interference degree is obtained according to the amplitude change characteristics of the EEG signals within a preset window; the local correlation is obtained according to the difference characteristics of the interference degree at corresponding moments in different areas. The final interference degree and the preliminary pseudo-electrooculogram (EOG) signal are obtained according to the local correlation of different areas and the EEG signal amplitude difference. The removal index is obtained according to the change characteristics of the final interference degree after removing the preliminary pseudo-electrooculogram (EOG) signal, and the final pseudo-electrooculogram (EOG) signal can be adaptively obtained according to the final interference index and the removal index, so that the final pseudo-electrooculogram (EOG) signal value is more accurate, thereby improving the accuracy of obtaining new EEG signals, and can more accurately assist doctors in analyzing sleep conditions through new EEG data.

This invention discloses a breathable, conductive hydrogel electrode, its preparation method, and its applications. The breathable, conductive hydrogel electrode comprises a porous fiber support structure and a hydrogel conductive network. The porous fiber support structure has a three-dimensional porous structure formed by interwoven porous fibers, each fiber possessing a three-dimensional interconnected channel structure formed by interconnected pores distributed on and within the porous fibers. The hydrogel conductive network is distributed on the surface and within the porous fiber support structure, forming a continuous ion conduction path within the electrode. The breathable, conductive hydrogel electrode provided by this invention has advantages such as high breathability, self-humidification, low impedance, and breathability, achieving high-fidelity, long-term acquisition of physiological signals such as electroencephalograms (EEG), electrocardiograms (ECG), and electromyograms (EMG). It can be widely used in electrophysiological signal monitoring, smart wearables, sleep analysis, and human-computer interaction.

The present disclosure relates to a sleep analysis segment detection method and system, and specifically, a sleep analysis segment detection method according to an embodiment of the present disclosure, which is performed in a system including a layer unit, a detection unit, a memory unit, and a data analysis unit, may include detecting a user's biosignal from the detection unit provided in the layer unit; storing the user's biosignal detected by the detection unit in the memory unit; and analyzing the user's biosignal stored in the memory unit by the data analysis unit to detect the user's sleep analysis segment.

System and methods of a mobile application to remotely monitor residents with device-free sensing of human motion over Wi-Fi is provided. A detailed behavioral analysis of various functions related to the sleep and activity of the subject has been provided. The Remote Care Application (RCA) is a mobile application on several platforms that displays the results of all the analysis and behavioral patterns obtained from the motion from the subject. The system relies on existing wireless communication signals and machine learning techniques in order to automatically detect the motion of the subject, sleep analysis of the subject, activity analysis of the subject.

The application discloses an automatic sleep staging method, system, device and storage medium, wherein the method comprises the following steps: acquiring a polysomnogram of a sleeper; extracting physiological signal features from the polysomnogram; performing an adversarial enhancement process on the physiological signal features to obtain enhanced physiological signal features; learning high-order correlation relationships between multi-modal physiological signals based on the enhanced physiological signal features; performing multi-modal fusion according to the high-order correlation relationships; and obtaining sleep stage classification results. The application obtains physiological signal data with balanced information quantity through adversarial enhancement of physiological signals, and realizes automatic sleep state detection through data fusion, thereby improving the prediction accuracy. In addition, the high-order correlation between modalities is captured for the collected multi-modal physiological signal features, and the redundant information and correlation information of the multi-modal physiological signals are effectively utilized for feature fusion. The application can be widely applied to the technical field of sleep analysis.

The invention relates to the technical field of enuresis nursing equipment, and discloses a night intelligent urine monitoring and nursing wearable system for enuresis children, which comprises a wearable diaper unit, a physical sign monitoring module, an intelligent interaction terminal, a data fusion analysis module and an intervention strategy generation module. According to the invention, through multi-modal data fusion of the distributed capacitive humidity sensor array and physiological sign monitoring, high-precision capture and urine volume estimation of the whole enuresis event process are realized; on this basis, sleep analysis and historical data are combined to generate a dynamic and personalized hierarchical intervention strategy, so that the accuracy and effectiveness of alarm are improved, false alarm and missing alarm are reduced, and meanwhile, comprehensive and associated data support including urine parameters and physiological states is provided for clinical diagnosis; the comprehensive effects of auxiliary treatment and improvement of child sleep quality and nursing efficiency are achieved.

The present disclosure relates to systems and methods for improving analysis of sleep data by classifying users based on sleeper type. In particular, a wearable computing system can obtain a first set of motion sensor data for a user during a first time period from a motion sensor. The wearable computing system can determine a sleeper type for the user from a plurality of sleeper types based on the first set of motion sensor data received from the motion sensor. The wearable computing system can select a sleep analysis model from a plurality of sleep analysis models based on the sleeper type determined for the user. The wearable computing system can analyze a second set of motion sensor data from a second time period using the selected sleep analysis model to determine one or more sleep features for the user during the second time period.

The application discloses a simple method for mechanically depriving fruit flies of sleep, and comprises the following specific steps: S1, sleep baseline determination before sleep deprivation; S2, putting fruit flies which need to be deprived of sleep together with monitoring small tubes into large tubes, plugging the tube openings with sponges, and binding the tubes with rubber bands after determining the same sleep baseline; S3, mechanical oscillation for depriving fruit flies of sleep; S4, sleep monitoring; and S5, analyzing data through sleep analysis software. The application adds a rotating oscillation device on the basis of an original fruit fly behavior monitoring system, realizes short-term deprivation of fruit fly sleep, provides a method for studying acute sleep deprivation of fruit flies, and has the characteristics of simplicity, high efficiency, economy and practicability.

The invention provides a sleep quality analysis method and system, a storage medium and electronic equipment. The method comprises the following steps: acquiring a multi-mode signal when a test object sleeps; the multi-mode signal comprises an electroencephalogram signal, a pulse signal and a body movement signal; preprocessing the multi-modal signal to obtain a multi-modal feature of the test object; calculating and obtaining multiple types of sleep analysis parameters of the test object based on the multi-modal characteristics; for the threshold interval to which each sleep analysis parameter belongs, calculating to obtain a corresponding sleep score coefficient; and calculating to obtain a sleep quality analysis value of the test object based on the multiple types of sleep score coefficients. The sleep quality analysis method and system, the storage medium and the electronic equipment are configured to realize accurate analysis of the sleep quality based on the multi-mode signal.

The invention relates to the technical field of sleep monitoring, in particular to a sleep monitoring method and system based on data analysis, and the method comprises the steps: synchronously collecting a BCG original signal and electroencephalogram data, and determining a first sleep staging label according to the electroencephalogram data; determining a second sleep staging label according to the BCG original signal; determining an actual sleep staging tag in combination with the first sleep staging tag and the second sleep staging tag; determining sleep characterization states according to the actual sleep stage labels of the users, wherein the sleep characterization states comprise a sleep consistent state and a sleep inconsistent state; and determining a plurality of users for sleep analysis according to the sleep characterization state and the actual sleep staging label of each user so as to perform sleep feature analysis on each user. According to the method, the sleep characterization state is determined through statistical analysis of the actual sleep staging labels of all the users, then the users needing sleep analysis are screened out, and effective management and personalized monitoring of group sleep are achieved.

The invention discloses a control method of a massage device for assisting sleep, which comprises the following steps: S1, collecting a multi-modal physiological signal of a user in a sleep process in real time through a non-contact or micro-contact sensor, and preprocessing the multi-modal physiological signal; s2, inputting the collected multi-modal physiological signals into a pre-trained sleep analysis model, and calculating and outputting sleep stage probability distribution and sleep stability index of the current user in real time; s3, dynamically generating an optimal massage control instruction according to the sleep stage probability distribution and the sleep stability index at the current moment and a preset intervention target library; and S4, recording physiological signal change and sleep stage evolution data before and after massage intervention, and continuously optimizing the sleep analysis model and the massage decision strategy in combination with the morning subjective sleep quality score of the user.

The invention relates to the technical field of sleep health monitoring, in particular to a sleep health monitoring auxiliary device which comprises a pillow and a paving towel, and a monitoring assembly is arranged on the pillow. A cavity is formed in the pillow, a bottom plate is fixedly connected to the bottom of the cavity, and an adjusting assembly, a driving assembly and an inflation assembly are arranged on the bottom plate. The driving assembly comprises a telescopic piece, an output shaft of the telescopic piece is fixedly connected with a pushing frame, and the pushing frame is rotationally connected with a first gear. The first gear is engaged with a first rack, and a sliding plate is fixedly connected to the first rack. The sliding plate is fixedly connected with a monitoring plate, and the monitoring plate is fixedly connected with the monitoring assembly. The first gear drives the first rack to slide so as to drive the sliding plate and the monitoring plate to slide, and the monitoring plate slides to drive the monitoring assembly to move along with the user, so that the monitoring effect of the monitoring assembly on the sleep health data of the user is improved, and the sleep analysis result is improved.

1. The name of the design product: sleep analysis graphical user interface of electronic device. 2. The use of the design product: an electronic device. 3. The design points of the design product: the content of the graphical user interface in the display screen panel. 4. The picture or photo that best indicates the design points: front view. 5. The use of the graphical user interface: the user interface of the product is the interactive interface of the smart sleep monitor applet. 6. The human-computer interaction mode of the graphical user interface: the display interface in the front view is the home page interface; the gray "data analysis" button below the front view can be clicked for interaction, and clicking the corresponding "data analysis" button switches to the interface of interface change state diagram 1 to display the sleep state statistical analysis interface of the day; the blue "sleep analysis" button above the interface change state diagram 1 can be clicked for interaction, and clicking the corresponding "sleep analysis" button switches to the interface of interface change state diagram 2 to enter the graphical analysis interface. 7. The "gray module" covering part in the figure is a variable figure, and the variable text is replaced by "X".

The embodiment of the invention provides a multi-modal data fusion sleep monitoring system and method based on collaboration. The system comprises a mobile detection terminal, a charging dock used for charging the mobile detection terminal and a cloud data platform, the mobile detection terminal only needs to collect physiological data, and the charging dock serves as data transfer and is selectively detachably connected with multiple detection modules to supplement the modality of detection data. The cloud data platform performs data analysis and fusion to obtain a sleep analysis result, so that comprehensive information data of various types / modalities can be obtained under the condition of limited cost, and meanwhile, the cloud data platform has strong data processing capability.

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 present invention discloses a multi-device multi-sensor sleep monitoring system, including: a data acquisition module and a data processing module, wherein the data acquisition module includes a bracelet acquisition unit, a radar acquisition unit and a sound acquisition unit, wherein the bracelet acquisition unit collects first data of a target including first body movement data, blood oxygen data, heart rate data and first respiratory data in real time. The multi-device multi-sensor sleep monitoring system disclosed by the present invention fully utilizes the advantages of various devices and various sensors, extracts relatively accurate original data detected by various devices and sensors for sleep analysis, evaluates the accuracy of the same type of data, assigns different weights to the same type of data, and uses different types of data to analyze and judge the sleep state according to their respective characteristics, thereby increasing the accuracy of the original data while increasing the amount of the original detection data, thereby improving the accuracy of the sleep detection analysis.

The invention discloses an intelligent lamp based on wake-up requirements and a sleep monitoring system, and relates to the technical field of intelligent lamps, the intelligent lamp comprises a lamp base and a lamp shell, a supporting shaft is fixedly arranged on the surface of the top end of the lamp base, a plurality of moving rails are fixedly arranged on the surface of the outer side of the supporting shaft, and driving systems are fixedly arranged in the moving rails; the outer side surfaces of the plurality of driving systems are connected with intelligent lamps, and the system further comprises a demand acquisition unit, a sleep monitoring unit, an awakening control unit, a light control unit and a sleep analysis unit; the wake-up execution plan is made according to the duration of the wake-up time interval and the physiological data, position regulation and control of the intelligent lamp are completed according to the wake-up execution plan, meanwhile, the working state of the intelligent lamp is switched, the wake-up process is achieved through light change, the lagged wake-up process is added, wake-up failure is avoided, and meanwhile, the sleep quality analysis chart is drawn. And a sleep-aiding reminding signal is generated to remind the user to fall asleep in time.

The embodiment of the invention provides a single-channel electroencephalogram sleep analysis method and related equipment, and belongs to the technical field of electroencephalogram signal processing and sleep health monitoring. The method comprises the following steps: carrying out preprocessing and time sequence enhancement on a single-channel electroencephalogram signal; extracting local and global features by using a double-branch feature encoder; feature distribution alignment between a source domain and a target domain is realized through a double-contrast adversarial learning module in combination with cross-view time sequence contrast learning and conditional adversarial alignment; dynamically adjusting a target domain sample weight based on information gain through a learnable reweighting module so as to correct label distribution offset; and finally, high-precision sleep staging or OSAHS event detection is realized by utilizing a classifier. According to the method, the generalization ability and robustness of the model in a cross-subject scene are remarkably improved, accurate analysis can be achieved only through single-channel electroencephalogram signals, the hardware cost and the use threshold are reduced, and the method is suitable for scenes such as family sleep monitoring and telemedicine screening.

The invention belongs to the technical field of artificial intelligence, and relates to a data processing method and device, computer equipment and a storage medium, and the method comprises the steps: carrying out the alignment fusion processing of collected multi-modal data of a user, and obtaining processed data; performing sleep analysis on the processing data based on a sleep analysis model to obtain a sleep stage division result; performing data matching and statistics on the sleep stage division result and the environment data to obtain a stage labeling result; performing causal analysis on the stage labeling result based on a causal attribution model to generate a causal attribution result; comprehensively analyzing the causal attribution result and the individual feature information of the user to generate a target intervention strategy of the user; and executing corresponding sleep intervention processing on the user based on the target intervention strategy. In addition, the target intervention policy may be stored in the blockchain. The method can be applied to home-based care scenes in the field of medical science and technology, the accuracy of sleep detection is improved, and the fitness and accuracy of the generated intervention strategy are improved.

This invention provides a cognitive management method and system for insomnia with intelligent suggestions for bedtime. The method includes: collecting and preprocessing multi-dimensional user data to obtain standardized input data; training a sleep analysis model based on historical datasets and continuously optimizing the model using user feedback data; using the sleep analysis model to perform inference based on real-time user data for the day, dynamically generating and outputting a personalized sleep plan including suggested bedtimes; obtaining actual sleep details and calculating sleep quality indicators; conducting continuous risk monitoring based on sleep quality indicators and triggering tiered intervention suggestions; and feeding the sleep quality indicators and user feedback data as training data into the model optimization step to achieve closed-loop optimization. This invention solves the problems of rigid suggestions, lack of personalization, and insufficient continuous optimization capabilities in existing technologies, achieving precise, dynamic, and intelligent sleep management.

The invention relates to a method for monitoring a user's sleep in a motor vehicle (10), wherein a control device (22): activates a rest program of the motor vehicle (10) (S1) that assists the user while resting in the motor vehicle (10); detects that the user is resting or sleeping in the motor vehicle (10) (S2); and receives an occupant monitoring signal (S3) from a sensor device (14) of the motor vehicle (10), which describes at least one body state parameter that describes a state of one or more body parts and / or one or more body actions of the resting or sleeping user. Based on the occupant monitoring signal, the control device (22) provides a user-specific sleep analysis model (S6) that describes an overall state of the user's body and determines the sleep quality of the user's sleep (S7) based on the provided sleep analysis model.The control device (22) then generates a display signal (S8) that describes the information on the determined sleep quality and transmits this to a display device (30) of the motor vehicle (10) (S9).

The present invention provides a sleep staging analysis method and device based on electroencephalogram (EEG) signals, belonging to the field of data analysis technology. The method and device specifically include: using sleep staging results corresponding to different trusted monitoring positions to determine the staging deviation period in the sleep analysis results, determining the matching situation between the EEG signals of different trusted monitoring positions at different times and monitoring data of other dimensions, and combining the deviation between the EEG signals at different times and other trusted monitoring positions to determine the abnormal change time of different trusted monitoring positions. Based on the distribution data of the abnormal change time of different trusted monitoring positions in the staging deviation period and the period before the staging deviation period, when it is determined that a reference monitoring position exists in the staging deviation period, the reference monitoring position is used to determine the sleep staging result in the staging deviation period, thereby improving the accuracy of the sleep staging result.