31 results about "Sleep period" patented technology

The present application relates to the field of artificial intelligence and intelligent sleep technology, and discloses a health state evaluation model generation method and system based on multi-point body surface temperature and vital sign data during sleep. The method continuously collects body surface temperature data of multiple body contact areas during the user's sleep process, and synchronously collects data such as heart rate, respiratory rate, body movement, environmental temperature and bed surface temperature, to construct a multi-dimensional sleep physiological data set. On this basis, the multi-dimensional sleep physiological data is preprocessed, feature extraction and model analysis are performed, and the health state evaluation result and personalized health insight information of the user during sleep are generated. The staged evaluation can also be carried out in combination with the change of sleep stage, and the model is continuously optimized based on the long-term temperature adjustment behavior of the user. By using the present application, the accuracy, continuity and individual adaptability of sleep health state evaluation can be improved, and more accurate sleep health analysis basis is provided for the user.

The present invention relates to systems and methods for analyzing data from motion activity monitoring technology. It is particularly, but not exclusively, concerned with a method for determining motion activity patterns during sleep.

The objective is to provide a method for manufacturing hardened, high-rebound products, which are particularly effective during sleep and promote restful sleep. [Solution] The present invention provides a method for manufacturing a hardened, high-rebound product, characterized by using a hardened polyester sheet formed by blending different types of polyester cotton, processing them into a sheet, laminating the processed polyester cotton sheets and heat-pressing them together, and then applying heat treatment. The hardened, high-rebound product obtained through this process is a very firm product despite using polyester cotton as the raw material, and its firmness prevents the occurrence of back pain and other problems, and is also easy to use.

A system includes: a memory storing machine-readable instructions; and a control system including one or more processors configured to execute the machine-readable instructions to: receive first data associated with a first sleep period of a user; determine a first set of sleep-related parameters associated with the first sleep period of the user based at least in part on the first data; receive second data associated with a second sleep period of the user; determine a second set of sleep-related parameters associated with the second sleep period of the user based at least in part on the second data; receive third data associated with a variable condition; and transmit one or more indications associated with (i) the variable condition and (ii) the first sleep period, the second sleep period, or both to the user.

This invention relates to an intelligent sleep monitor. It uses detection devices to measure brainwave frequency, brain current, sleep posture, left and right eye voltage, and blood oxygen concentration. A central control device integrates and analyzes the data. The sleep cycle is segmented based on brainwave frequency and left and right eye voltage. Since different parts of the brain emit effective brain currents at different times during sleep, the device detects current at different brain locations based on different sleep periods, and weights the data used to determine sleep quality.

This invention provides a radar-based sleep staging method, terminal, and storage medium. The method includes: acquiring human echo signals detected by radar over a preset monitoring area in real time, and calculating a sleep staging index corresponding to each calculation cycle during the sleep period based on the human echo signals; calculating a sleep staging threshold corresponding to the current calculation cycle based on the standard deviation of the sleep staging indices corresponding to multiple calculation cycles near the current calculation cycle; and determining the sleep staging result for the current calculation cycle based on the sleep staging index and the corresponding sleep staging threshold. This method can adaptively set the sleep staging threshold based on the user's own sleep patterns, thereby improving the accuracy of sleep staging calculations.

This invention relates to the field of sleep monitoring data fusion processing technology, and discloses a sleep monitoring method based on the fusion of non-invasive brain oxygen saturation (AHI) and multiple physiological parameters. The method includes: S1, simultaneously acquiring multiple physiological parameter signals during the subject's sleep; S2, extracting and analyzing the AHI signal to generate multiple characterization markers; S3, identifying respiratory events related to the subject's apnea information from the respiratory signal and pulse oximetry signal, calculating the apnea-hypopnea index (AHI); and performing correlation analysis on the identified respiratory events and multiple characterization markers to calculate a correlation coefficient; S4, obtaining a comprehensive sleep monitoring assessment report based on the correlation coefficient and the AHI to determine the severity of the subject's current respiratory disturbance and the risk of brain tissue hypoxia. By improving the effectiveness of AHI in non-invasive brain oxygen saturation monitoring, the confirmatory nature of non-invasive brain oxygen saturation information in the respiratory correlation diagnosis during sleep monitoring is ensured.

This invention discloses an emotion monitoring system based on sleep HRV characteristics, belonging to the field of intelligent health monitoring technology. The system includes: simultaneously collecting PPG, EEG, and near-infrared light signals during sleep; analyzing HRV sequences and respiratory wave morphology based on PPG signals; achieving sleep microstructure staging through cardiopulmonary coupling analysis; extracting dynamic HRV features based on the staging results and comparing them with personalized stage baselines; detecting and labeling emotional load events; constructing and updating a nighttime emotional event atlas; predicting safe intervention time windows based on the atlas and sleep staging; executing personalized interventions and collecting physiological feedback data to form a closed-loop optimization system. This invention achieves high temporal resolution, event-oriented nighttime emotional dynamic identification and safe intervention, improving the accuracy and reliability of emotion monitoring.

This application provides a sleep quality monitoring method and wearable device, relating to the field of smart wearable device technology. The method first acquires sleep monitoring data during a user's sleep period and extracts core sleep indicators such as sleep duration, sleep regularity indicators, and sleep effectiveness indicators from the sleep monitoring data. Then, it obtains scoring calculation weights, thereby generating and displaying sleep score information based on the scoring calculation weights and the core sleep indicators. The sleep score information includes a sleep quality score, and at least one of a duration score, a regularity score, and an effectiveness score. This method can generate sleep score information containing multiple monitoring results during sleep quality monitoring, achieving score decomposition of the sleep score, reflecting the degree of influence of different dimensions of monitoring data on the sleep quality monitoring results, increasing the information content and reference value of the output results, and helping to increase user engagement with the wearable device.

To provide a system of supporting superior sleep quality.SOLUTION: A sensing unit is configured to generate pressure readings from phenomena within a sleep environment and to generate temperature readings from a sensed temperature. A processing unit is configured to select a thermoregulation adjustment and to alter a thermal property of a sleep environment based on the selected thermoregulation adjustment.SELECTED DRAWING: Figure 20

The objective is to provide a method for manufacturing hardened, high-rebound products, which are particularly effective during sleep and promote restful sleep. [Solution] The present invention provides a method for manufacturing a hardened, high-rebound product, characterized by using a hardened polyester sheet formed by blending different types of polyester cotton, processing them into a sheet, laminating the processed polyester cotton sheets and heat-pressing them together, and then applying heat treatment. The hardened, high-rebound product obtained through this process is a very firm product despite using polyester cotton as the raw material, and its firmness prevents the occurrence of back pain and other problems, and is also easy to use.

A system for managing a sleep disorder of a subject is provided, the system comprising: a sensor assembly for detecting movement and / or the orientation of part or all of the body of the subject and / or movement of the subject during sleep; an emitter for stimulating the subject to change the orientation of their body; and a processor configured to: receive a signal from the sensor assembly, the signal comprising data relating to the orientation and / or movement of the subject; process the data received from the sensor assembly; and transmit a signal to the emitter to apply a stimulus to the subject to provoke the subject to alter the orientation of part or all of their body. The system may also comprise a sensor for detecting sounds emitted by the subject while sleeping. A method for managing a sleep disorder of a subject is also provided, the method comprising: i) sensing the position in which the subject is sleeping; ii) determining if the position is related to a sleep disorder; and iii) if the position is related to a sleep disorder, applying a stimulus to the subject to provoke the subject to change their sleeping position.

A system includes a respiratory therapy system, a memory device, and a control system. The respiratory therapy system includes a respiratory therapy device supplying pressurized air and a user interface connected via a catheter to the respiratory therapy device to direct the pressurized air to the user's airway. The memory device stores machine-readable instructions. The control system includes a processor to execute the machine-readable instructions to: generate data associated with the user of the respiratory therapy system during a current sleep period; analyze the generated data to determine a value of a first indicator associated with sleep apnea (SDB); analyze the generated data to determine a value of a second indicator associated with a health condition other than SDB; and cause an action to be performed based at least in part on the determined value of the second indicator.

A detecting method for a behavior disorder event during rapid-eye-movement sleep is provided. The detecting method includes: collecting a heart rate value and a motion value of a user per epoch within a time period; generating a plurality of corresponding sleep condition values by using the motion values, to distinguish epochs into an awake period and a sleep period; transforming the motion values corresponding to the sleep period into a score according to a predetermined rule, to generate a plurality of sleep depth scores, and distinguishing the sleep period into a light sleep period and a deep sleep period by using the sleep depth scores; grouping the heart rate values corresponding to the deep sleep period as a high heart rate group and a low heart rate group; and determining, when the motion values corresponding to the high heart rate group satisfy a condition, that a behavior disorder event happens.

Provided is a novel mechanism for estimating apnea hypopnea index (AHI). This AHI estimation system comprises: an AH information acquisition unit that acquires temporal pulse wave information from a subject, and acquires, on the basis of the pulse wave information, AH information indicating the time ratio in which the subject experiences apnea (A) and hypopnea (H) during sleep; and an AHI estimation unit that estimates an apnea hypopnea index (AHI) on the basis of the AH information. The AH information acquisition unit: acquires acceleration sensor information; evaluates, on the basis of the acceleration sensor information, whether the subject is in a sleeping state or a wakeful state; and if it is evaluated that the subject is in a sleeping state, acquires the AH information on the basis of the pulse wave information for when it is evaluated that the subject is in the sleeping state.

This invention involves a fire and smoke sensor integrated into the mobile phone system circuit, capable of detecting fire, smoke, and gas. When a threat is detected, the sensor automatically activates an audible alarm through the phone's external speaker, providing an immediate and direct safety mechanism—especially valuable during sleep when the user may not be alert. This innovation promotes the adoption of safety technologies within everyday personal devices. Figure (1)

The application discloses a low-power consumption Mesh networking system and method for taming a crystal oscillator by intermittent satellite timing, wherein each routing node is provided with a GNSS positioning and timing unit and a local crystal oscillator timing unit, the GNSS unit intermittently works in a sleep-timed wake-up mode, and after being woken up, the GNSS unit captures a satellite standard 1PPS pulse and UTC timestamp and then sleeps off; the routing node is provided with a crystal oscillator taming and correction module, which captures the latched counting value of a local crystal oscillator free-running counter at the rising edge of the pulse each time the 1PPS pulse is successfully acquired, calculates the phase deviation of the latched counting value and a theoretical expected counting value, calculates the instantaneous frequency deviation ppm value of the local crystal oscillator from the phase deviations of a plurality of continuous periods, and writes the corresponding compensation amount into a frequency compensation register, so that the crystal oscillator freely runs at the compensated frequency during the subsequent GNSS sleep period; meanwhile, a double-table structure of a backbone routing table and a neighboring forwarding table is simultaneously run to isolate long-distance and short-distance services, and a network access protocol of routing node silent listening-coordinator invitation is adopted to replace active flooding scanning.

The present disclosure relates to a mental state evaluation method and system. The method is based on the heart shock wave signal, the chest and abdominal respiration signal and the sleep cycle signal during sleep monitoring of the evaluation object on multiple collection days, respectively extracts the physiological feature set under each signal, and each physiological feature set contains physiological features in multiple dimensions. The method generates a fused physiological feature set based on the physiological feature set of each of the above signals. The method also outputs an emotional grade label and a first score from a first evaluation model based on the fused physiological feature set, and outputs a psychological state label and a second score from a second evaluation model. In addition, the method determines the mental state evaluation result of the evaluation object based on the first and second scores. The embodiments of the present disclosure can objectively, dynamically and explainably evaluate the individual mental state of the evaluation object by extracting multi-dimensional features from the three modal signals of heart rate, respiration and sleep during sleep monitoring.

A method and a system for inducing a sleep posture to prevent snoring and sleep apnea during sleep are disclosed. A system for inducing sleep posture according to one embodiment may comprise a sensor device attached to a body of a user, the sensor device being configured to collect first data comprising at least one of posture data of the user and respiration data of the user and transmit the first data to a user device, and to generate vibration for inducing a change in a sleep posture of the user in response to a control signal received from the user device; and the user device configured to collect second data comprising sound information related to sleep of the user, to aggregate and analyze the first data received from the sensor device and the second data to determine a snoring state or a sleep apnea state of the user, and to transmit, to the sensor device, the control signal for generation of vibration in response to a determination that the user is in the snoring state or the sleep apnea state.

A configurable time-delayed oral mandible displacement device includes a top plate configured for engaging maxillary teeth of a user and a bottom plate configured for engaging mandibular teeth of the user. The bottom plate is slidably coupled to the top plate for relative movement between a non-extended position and an extended position for displacing the mandible of the user during sleep. An adjustment mechanism is provided for selectably setting at least an initial position of the bottom plate relative to the top plate, within a predetermined range of initial positions, the adjustment mechanism comprising at least one element fabricated from a shape memory material (SMM).

Example inventions detect an occurrence of disturbance during a sleep period of a user. Examples receive data corresponding to a single parameter of the user during the sleep period, determine patterns in the data, and based on the patterns, determine a sleep disturbance severity index (“SDSI”) over the sleep period.

This invention provides a sleep cycle segmentation method based on multi-condition extraction and multi-level correction, specifically including the following steps: S1: Preprocess the segmentation sequence, standardizing each time period label to a preset discrete category identifier; S2: Scan the preprocessed segmentation sequence, detect the sleep start point and sleep end point, and extract the segmentation sequence between them as sleep period data; S3: Start scanning from the first time period of the sleep period, taking the non-rapid eye movement sleep (NREM) stage as the start of a new cycle, ending the current cycle and calculating the cycle duration when the cycle end condition is met, and saving the current cycle if the preset minimum cycle duration threshold is met, and then continuing to search for the next candidate sleep cycle until the sleep end point is reached; S4: Postprocess all the initially retained candidate sleep cycles; S5: Perform statistical analysis on the effective sleep cycles obtained from the postprocessing and output the results.

This makes it easier to identify hypoxemia caused by REM hypoventilation. [Solution] The sleep hypoventilation screening system S0 comprises: a measurement means 11 for detecting measurement data regarding the subject U's SpO2; an analysis processing means 121 for generating time-series data of SpO2 by performing predetermined data analysis processing on the measurement data acquired by the measurement means 11; a dip detection means 122 for detecting a predetermined decrease peak of SpO2, which is a dip, from the time-series data of SpO2; and a correction means 123 for interpolating the interval detected as a dip by the dip detection means 122.

A system and method for noninvasive sleep monitoring are disclosed. The system includes an in-ear biosensor system (“biosensor system”) and a data analysis system. The biosensor system includes at least one earbud placed at or within an ear canal of an individual. The earbud includes an acoustic sensor that detects biosignals including infrasonic and audible signals from the individual in the ear canal. A controller board of the biosensor system collects and forwards the biosignals to the data analysis system. The data analysis system analyzes the biosignals to identify and monitor physiological information of the individual during sleep. The physiological information includes sleep stages, sleeping position information, sleep events including snore, breathing cessation and bruxism events, and sleep disorders, in examples. The system can also induce changes to the physiological information during sleep using external stimuli to improve a quality of sleep.

The application provides a kind of intelligent fragrance dynamic emission method and system based on sleep staging and environmental compensation.The method comprises: based on the environmental light variation of target sleep space, sleep staging data is subjected to stage continuity check and boundary event correction, to generate target sleep stage;According to temperature data, humidity data and environmental light data, generate environmental compensation representation for representing fragrance volatile perception state and sleep scene boundary;According to historical fragrance release record, environmental compensation representation and fragrance medium attribute, generate fragrance effective exposure and fragrance residual load of current sleep period;Determine the release window, release intensity and release duration parameters matched with the current sleep stage, generate fragrance dynamic emission instruction;Execute fragrance dynamic emission instruction, and update individualized sleep fragrance control profile according to subsequent sleep staging change, environmental light change and user feedback.The application can improve fragrance emission matching, enhance environmental adaptability and reduce residual superposition risk.

Sleep performance systems and methods of using the same are disclosed. The sleep performance systems can improve the quality of sleep by making one or more recommendations to the subject for increasing a sleep quality score. The sleep performance systems can have one or more electroencephalography (EEG) electrodes configured to measure a subject's brain activity during sleep. The sleep performance systems can have a processor configured to quantify the quality of the subject's slow-wave sleep by determining one or more sleep performance scores associated with the measured brain activity. The sleep performance systems can recommend and / or activate sleep improvement programs based on various threshold scores.

The application discloses a method and system for evaluating the risk of neurodegenerative disease progression. The method comprises: obtaining multi-modal brain image data and all-night sleep electroencephalogram data of the same subject; calculating brain lymphatic indicators reflecting the brain lymphatic system clearance function and sleep coupling indicators reflecting the coupling characteristics between slow wave oscillation and theta rhythm or spindle wave in non-rapid eye movement sleep period; inputting the above two types of indicators into a pre-trained risk prediction model to obtain the risk evaluation result of the subject. The pre-trained model is based on historical population data, and the brain lymphatic and sleep coupling indicators pair with significant intermediary correlation with cognitive decline is screened out through intermediary analysis as correlation characteristics, and the model is trained using these characteristics. The application significantly improves the accuracy and interpretability of disease progression prediction through mechanism-oriented feature fusion, providing a new quantitative tool for early warning and individualized intervention of neurodegenerative diseases.

The application provides a working memory quantitative prediction method based on respiratory-electroencephalogram coupling characteristics and adaptive causal inference, and belongs to the technical field of biomedical signal processing and artificial intelligence. In order to solve the problems that the prior art cannot capture the nonlinear causal dependence between respiration and electroencephalogram and the deep learning model lacks task-specific design, the application synchronously collects sleep period electroencephalogram, nasal airflow, chest and abdominal respiratory movement and snoring vibration signal; extracts the snoring spectrum centroid frequency and calculates the slow wave suppression time constant through the exclusive index mapping; adopts the directional transfer entropy of the dynamic threshold to perform nonlinear causal inference; deeply embeds the physiological parameters and causal indicators into the coupling architecture composed of the snoring modulation graph convolution, the causal gated Transformer and the snoring perception multilayer perception machine, and designs the dynamic gated multi-task output and the ablation perception loss function. The application can realize quantitative prediction of reaction time, accuracy and comprehensive cognitive score, and has the characteristics of high precision and adaptability.

The application provides a signal detection method. The method is applied to a communication system composed of a terminal and a network device. In the method, the network device can evaluate the wake-up reliability of the terminal in a subsequent DRX cycle based on relevant measurement information and operation statistics. When the evaluation result indicates that the terminal has a risk of wake-up failure, the network device generates corresponding risk indication information and sends the risk indication information in association with a WUS. The terminal detects the WUS through a WUR during the DRX sleep period, identifies the risk indication information without completely waking up the main radio frequency, and adjusts the WUS monitoring behavior in the DRX cycle accordingly, thereby improving the WUS monitoring density and the WUS wake-up probability, and ensuring that the terminal can respond to the downlink scheduling and control requirements of the network device in time in a harsh environment.

There is disclosed an anatomical pregnancy sleeping mattress which has a cavity in which the belly adapts during sleep. In this cavity pieces of memory foam material or even better pieces of state-of-the-art material "Motion Isolation Mattress" will be placed, forming "Onion Peel» levels. The advantage of this invention is that it helps the pregnant woman to be able to adjust the space in dependance on the month of her pregnancy and, therefore, on the volume of her belly; at the end of the pregnancy this special mattress is converted into a common mattress.