40 results about "Sleep patterns" patented technology

A system and method for generating patient care instructions based on real-time sensor and medical data. The method includes receiving time-stamped sensor data from a sensor network comprising motion, occupancy, and environmental sensors, and receiving medical data associated with a patient, including medical conditions, treatment history, medication data, and biometric data. The sensor data is enriched with room-specific information, and activity pattern data is generated in real time using a pattern recognition model. The activity pattern data includes mobility, sleep patterns, medication adherence, statistical measures, temporal patterns, and correlations with medical data. Anomalies indicating potential health risks are detected by comparing current activity patterns with baseline data. A prediction model, trained on historical patient data, assesses the patient's health and generates care instructions accordingly. The care instructions are securely delivered to patient devices, caregiver applications, or automated medication dispensing systems, enabling timely interventions and continuous patient monitoring.

Introduced are methods and systems for monitoring a person's sleeping patterns, and detecting episodes of sleeping disorders such as snoring and sleep apnea. In one embodiment, a sensor strip attached to the mattress monitors the user's breathing, and detects signature frequencies corresponding to snoring and sleep apnea. Once a sleeping disorder is detected, a notification can be sent to a device associated with the user, or the user's bed can be automatically adjusted to alleviate the sleeping disorder.

A system includes a processor that collects data representing activity levels, sleep patterns, and food intake from an information collection device attached to a dog, transmits the collected data to an analysis server, wherein the analysis server inputs the collected data into a generative artificial intelligence model and analyzes the data, transmits analysis results to a terminal and notifies a user, receives user input regarding questions or symptoms of the dog and generates advice using the generative artificial intelligence model, transmits the generated advice to the terminal for display to the user, and provides online consultation reservation with a veterinarian.

The present invention relates to a system and a method for allowing a user to connect to a controller much faster and more conveniently by performing initial connection between system components through BLE / Wi-Fi chipsets provided in a sensor unit and the controller. A smart mattress networking system according to an embodiment of the present invention comprises: a controller for adjusting a hardness of a smart mattress under control of a monitoring server, wherein the controller establishes a state ready for connection to a user terminal through Bluetooth communication using an internally provided BLE / Wi-Fi chipset, and performs initial connection to the user terminal by inputting an SSID / PW delivered from the user terminal; a user terminal having a monitoring application installed therein to record and report a sleep pattern through wireless communication with the monitoring server, wherein the user terminal identifies Wi-Fi information of a current space and delivers an SSID / PW to the controller through Bluetooth communication; a sensor unit for sensing (measuring) pressure (body pressure) during a sleep of a user located on the smart mattress and transmitting the sensed pressure to the monitoring server through the controller; and the monitoring server for analyzing a sleep posture on the basis of a sensing value of the sensor unit, determining a hardness suitable for the sleep posture, and transmitting the determined hardness to the controller.

A bed includes components to control temperature of a sleep surface, for example based on time and historical usage patterns by a user. In some embodiments the temperature of the sleep surface is controlled based on information indicating a sleep state of the user. In some embodiments the temperature is dynamically adjusted so to achieve particular sleep states and / or sleep patterns for the user. In some embodiments the temperature and timing of temperature adjustments is iteratively adjusted over multiple sleep sessions so to achieve improvements in sleep states and / or sleep quality for the user.

A system is provided.SOLUTION: The system includes an electric stimulation means for relaxing the muscles around the eyes, a camera means for monitoring the state of the pupils, a sensor means for analyzing the sleep pattern of the user, a notification means for notifying the timing of the eye lotion, and a server means for managing data obtained from the respective means.SELECTED DRAWING: Figure 1

An artificial intelligence (AI) based system for nutrient estimation, and meal planning comprises a server processor coupled with a database in network communication with a plurality of users connected over the said network. The database stores characteristics that represent physical conditions about the users (e.g. DNA test results, family health history, BMI along with meal patterns, hunger, sleep patterns, and food habits). The database coupled with the said server processor includes a diet plan dataset, and an ingredient nutritional information dataset. The server processor is operable to generate a meal profile for each of the users, wherein each meal profile comprises a plurality of individual characteristics representative of the user's physical condition. The system analyses the stored characteristics, and recommended nutritional intake levels to generate at least one meal plan.

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.

A method and apparatus to improve sleep aid efficacy are provided. The method comprises monitoring the sleeping patterns of a user utilizing a non-intrusive sleep monitoring device. The method further correlates the user's sleep quality and sleep quantity with use of the sleep aid. In one embodiment, the method adjusts the sleep aid based on the sleep statistics. The efficacy of sleep aids is increased by monitoring user's sleep patterns continuously.

A system is provided.SOLUTION: A system comprising: a terminal including a sensor that detects a motion of a baby, a crying voice, a room temperature, a humidity, and an intensity of illumination; a server that receives and stores data transmitted from the terminal; a unit that learns a sleep pattern of the baby using an artificial intelligence model installed in the server and proposes an optimal method of putting the baby to sleep and an optimal environment setting; SELECTED DRAWING: Figure 1

A system and method for generating patient care instructions based on real-time sensor and medical data. The method includes receiving time-stamped sensor data from a sensor network comprising motion, occupancy, and environmental sensors, and receiving medical data associated with a patient, including medical conditions, treatment history, medication data, and biometric data. The sensor data is enriched with room-specific information, and activity pattern data is generated in real time using a pattern recognition model. The activity pattern data includes mobility, sleep patterns, medication adherence, statistical measures, temporal patterns, and correlations with medical data. Anomalies indicating potential health risks are detected by comparing current activity patterns with baseline data. A prediction model, trained on historical patient data, assesses the patient's health and generates care instructions accordingly. The care instructions are securely delivered to patient devices, caregiver applications, or automated medication dispensing systems, enabling timely interventions and continuous patient monitoring.

1. Name of this design product: Children's Sleep Training Clock. 2. Purpose of this design: To correct children's sleep patterns. Establish a good daily routine. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

Detection of fast eye movement (REM) sleep and associated abnormalities is important for identifying various nervous system diseases. The present disclosure relates to the detection of time intervals during which a subject is in REM sleep by utilizing electroencephalogram (EEG) data from selective spectral frequencies. The techniques as disclosed herein may use one or more EEG electrodes, and more particularly, single channel EEG signals provide a simple and cost-effective solution. The disclosed techniques may pre-process the EEG data, extract features and / or derived features for one or more time intervals from selective spectral bands (e.g., delta and gamma). These extracted features may be normalized and clustered to further determine REM, non-REM, and waking time intervals. By using one or more non-EEG sensors to detect the presence of muscular tension of the identified REM sleep intervals, and by performing sleep pattern analysis, potential REM intervals can be verified to detect REM behavioral disorders.

The invention relates to a sleep state metric index calibration system and a management system thereof.The calibration system comprises a first sleep monitor, a second sleep monitor and / or a terminal, the first sleep monitor collects physiological feature data in a sleep state and determines a first sleep mode, sending the own sleep state measurement index and the first sleep mode to a second sleep monitor; the second sleep monitor and the first sleep monitor start monitoring at the same time, bio-electricity signal data related to time is collected to serve as second sleep feature data, and a second sleep mode is determined; the second sleep monitor generates update information of a sleep state measurement index of the first sleep monitor on the basis of a difference between the first sleep mode and the second sleep mode in the same sleep cycle by taking the second sleep mode as a reference; and the second sleep monitor transmits update information to the first sleep monitor through direct communication or indirect terminal transmission so as to complete calibration or correction of the sleep state measurement index of the first sleep monitor.

A refrigerator and a method of controlling the same. The refrigerator including a door, a communication unit, a heat exchanger, a defrost heater, and at least one processor, wherein the at least one processor is configured to determine a first time period during which the refrigerator is unused based on an opening and closing pattern of the door during the first time period, control the communication unit to receive sleep pattern information from a smart device, control the communication unit to transmit, to a server, the first time period and the sleep pattern information, control the communication unit to receive, from the server, a second time period obtained by calibrating the first time period based on the sleep pattern information, and determine an operation start time of the defrost heater based on a cooling duration of the heat exchanger and the second time period.

This application discloses a sleep detection and early warning method and device. The method comprises: obtaining a target monitoring image for monitoring a first subject's sleep; detecting target region information and target key point information in the target monitoring image, wherein the target region information includes at least facial region information, limb region information, and quilt region information, and the target key point information includes human body key point information; determining a target sleep pattern for the first subject based on the target region information and target key point information; processing the target region information and target key point information based on a target processing method corresponding to the target sleep pattern to obtain the first subject's sleep state, and generating early warning information corresponding to the sleep state. This application addresses the technical problem in related technologies of poor performance in infant and toddler sleep detection solutions and difficulty in providing timely and effective early warnings.

A mobile device for identifying a potential candidate includes a screen, a sensor, and a control system. The sensor configured to generate sensor data associated with use of the mobile device. The control system is configured to: extract location data from the sensor data, the location data including physical location of the mobile device at different time intervals; determine from the location data whether mobility of the mobile device is below a threshold; extract mobile device usage data from the sensor data; determine, from the mobile device usage data, a pattern of use of the mobile device to obtain a sleeping pattern of the potential candidate; and based at least in part on the mobility of the mobile device being below a threshold and the pattern of use of the mobile device, cause the screen to display an alert.

A system is provided.SOLUTION: A system comprising: means for collecting genetic information of an individual; means for collecting nutritional intake data, physical activity data, and sleep pattern data; means for integrating and analyzing the genetic information and the daily life data; means for generating a personalized health plan based on the analysis; and means for providing an interactive health improvement experience through virtual reality or augmented reality based on the health plan.SELECTED DRAWING: Figure 1

Proposed is a health care monitoring and smart home convergence system based on a built-in ceiling IoT radar sensor. The health care monitoring and smart home convergence system includes the built-in ceiling IoT radar sensor, and a health analysis part. The built-in ceiling IoT radar sensor is installed into a ceiling of a bedroom, and configured to measure a user's heart rate or respiratory rate on the basis of time of flight (ToF) of a radar signal to generate biosignal data. The health analysis part is configured to analyze the user's sleep pattern on the basis of the biosignal data, and uses the sleep pattern to generate health prediction information on the user's health state.

Multi-mode neurological and cardiopulmonary monitoring systems may be designed for the especially challenging needs of very fragile patients, such as neonates and infants. Compact, lightweight, time-synchronized wireless sensors may be built into wearable modules. The modules' skin-friendly encapsulations and bio-integrated form factors reduce the risk of iatrogenic injury while permitting natural motion and comforting touch. The system's integrated sensors include electroencephalography, near-infrared spectroscopy, electrocardiography, seismocardiography, photoplethysmography, motion, and temperature. By combining and analyzing the sensors' time-synchronized readings, the system can calculate heart rate, respiratory rate, pulse oxygenation, temperature, perfusion index, tidal volume, pulse wave velocity, and pulse arrival time (a surrogate for blood pressure). Advanced algorithms can further evaluate sleep patterns and detect arrhythmias, strokes, hemorrhaging and pre-seizure events. Other design features facilitate low cost, ruggedness, and ease of use in both hospital and non-hospital settings.

The present invention provides an information processing system, comprising: means for collecting activity amount, sleep pattern, and food intake data from an information collection means worn on a dog body; means for transmitting the collected data to a resolution server; the device is used for inputting the collected data into the generative artificial intelligence model by the analysis server and analyzing the data; the device is used for sending an analysis result to the terminal and informing a user; the device is used for receiving input about questions and dog symptoms from a user and generating suggestions through a generative artificial intelligence model; means for transmitting the generated suggestion to the terminal and displaying the suggestion to the user; a device for providing veterinary online consultation appointment functionality.

Detecting rapid eye movement (REM) sleep and associated abnormalities can be significant for identifying various neurological disorders. The present disclosure relates to detection of time intervals during which a subject is in REM sleep by leveraging encephalography (EEG) data from selective spectral frequencies. The techniques, as disclosed herein, may use one or more EEG electrodes, more specifically, a single-channel EEG signal offering a simple and a cost effective solution. The disclosed technique may preprocess this EEG data, extract features and / or derived features for one or more time intervals from selective spectral bands e.g., Delta and Gamma. These extracted features may be normalized and clustered to further determine REM, non-REM, and awake time intervals. By leveraging one or more non-EEG sensors to detect presence of muscle tones for the identified REM sleep intervals and by performing sleep pattern analysis, potential REM intervals may be validated for detection of REM behavior disorder.

The present invention relates to a sleep analysis-based disease prediction device installed in a senior house. The sleep analysis-based disease prediction device according to an embodiment of the present invention may be installed in a senior house and include: a bio-signal sensor which is installed in a bedroom to remotely measure the heart rate or respiration of a sleeper and generate bio-signal data; and a sleep analysis unit for analyzing a sleep pattern of the sleeper on the basis of the bio-signal data and generating an alarm message when a disease suspicion pattern is determined from the sleep pattern.

The present invention relates to a smart mattress in which: a master controller (300) capable of sensing temperature, humidity, lighting and noise in the vicinity of a mattress user who is asleep, and capable of sensing the movement of the mattress user who is asleep is provided; the temperature is measured with a temperature sensor, the humidity is measured with a humidity sensor, the lighting is measured with an illumination sensor, the noise is measured with a noise sensor, and the movement of the mattress user is measured with a radar sensor, such that the master controller (300) collects primary data; the master controller (300) analyzes the primary data, transmits the primary data to an application of a mobile phone and a web server, and enables a sleep pattern graph of the user to be checked on the application; the master controller (300) can control, on the basis of the analyzed primary data, an air conditioner provided therein, and transmits a signal to an auxiliary controller (200) so that a PTC planar film heater and a blowing fan (181) can be controlled; the master controller (300) is provided on the wall of an indoor space in which a mattress (100) is located, or adjacent to the head of the mattress user; the mattress (100) has a bottom cover (102), and a top cover (101) on which a conductive line is arranged; a sponge base (170) inserted into the upper portion of the bottom cover (102) and mounted inside the bottom cover (102) is provided, the material of the sponge base (170) being sponge; the sponge base (170) has a mounting space (100MU-SP), and has an uprightly protruding convex frame (170-FM) that surrounds the perimeter of the mounting space (100MU-SP); and a mounting space stacking unit (100MU) can be inserted and mounted in the mounting space (100MU-SP).

The present invention relates to a healthy sleep management system and method. The system includes a first sleep monitor and a second sleep monitor. The first sleep monitor collects at least one first sleep characteristic data related to the user's time and determines a first sleep pattern while the user is in a sleep state. The second sleep monitor collects at least one second sleep characteristic data related to the user's time and determines a second sleep pattern while the user is in a sleep state. The system compares the difference between the first sleep pattern and the second sleep pattern and generates updated information about the sleep state metric of the first sleep monitor. The system then enables the first sleep monitor to correct its monitoring results for the first sleep pattern based on the updated information about the sleep state metric. The present invention enables high-precision monitoring of sleep monitoring results of portable wearable devices.

The present invention discloses an auxiliary diagnosis system for patients with impaired consciousness based on automatic sleep staging, comprising: a sleep EEG data acquisition module, which uses a polysomnography to collect and label raw sleep EEG data from patients with impaired consciousness; a data preprocessing module, which uses re-referencing, bandpass filtering, and independent component analysis to preprocess the data collected by the sleep EEG data acquisition module; a feature extraction module, which uses the preprocessed data as input and inputs it into a sleep staging network model to obtain sleep staging results for patients with impaired consciousness, and summarizes the sleep staging results as a feature matrix of the sleep staging results; and a consciousness disorder classification module, which uses the feature matrix of the sleep staging results as input and inputs it into an adaptive classification network model to perform a final auxiliary diagnosis on patients with impaired consciousness. The present invention conducts auxiliary diagnosis research on patients with impaired consciousness from the perspective of sleep patterns, and by comparing the heterogeneity of sleep patterns, it is helpful to discover the pathological causes of patients.

A system is provided.SOLUTION: The system includes a means for obtaining basic information of the child, a means for monitoring the child's health condition, a means for analyzing the monitored data and detecting abnormalities, a means for notifying abnormalities in real time, a means for recording the child's sleep pattern and generating an appropriate sleep schedule, a means for guiding the child's sleep based on the sleep schedule, a means for suggesting educational play based on the child's age and interests, and a means for detecting and reporting abnormal behavior in real time.SELECTED DRAWING: Figure 1

This invention relates to a sleep-aiding method and system based on heart rate variability biofeedback, belonging to the field of sleep health management technology. It solves the problem that existing sleep-aiding technologies, which rely on visual biofeedback and lack mechanisms for recognizing sleep trends and automatically exiting sleep patterns, are prone to overstimulation. The method includes: acquiring vital signs signals at multiple time steps and a sleep-aiding rhythm signal at the current time step; the vital signs signals include PPG sensor signals and IMU sensor signals; determining the signal quality index for each time step based on the PPG sensor signals from multiple time steps; calculating the body movement intensity at each time step based on the IMU sensor signals from multiple time steps; and adjusting the sleep-aiding rhythm signal based on the signal quality index and body movement intensity at the current time step or multiple time steps. This invention achieves full-process sleep management with light-free biofeedback, automatic sleep trend recognition, and nighttime awakening re-intervention, improving the effectiveness of sleep-aiding guidance.