18 results about "Sleep interval" patented technology

Methods, systems, and devices for determining a sleep age metric of a user are described. The system may receive photoplethysmogram (PPG) data throughout a time interval including a plurality of sleep intervals during which the user is asleep, input the PPG data into a first machine learning model, and classify the PPG data into a plurality of sleep stages. The system may input one or more sleep features into a second machine learning model, and the second machine learning model may output a sleep age metric associated with a sleep health of the user relative to a chronological age of the user. In some cases, the system may transmit, to a user device, an instruction to cause a graphical user interface (GUI) to display an indication of the sleep age metric and an insight for a value of the sleep age metric.

Methods, systems, and devices for measuring resilience to stress are described. A system may acquire heart rate variability (HRV) data from a user via wearable device throughout multiple time intervals, where each time interval includes an awake interval that the user is awake and an asleep interval that the user is asleep. The system may determine a stress index and a recovery index associated with the awake interval of a respective time interval, and a sleep recovery index associated with the asleep interval of the respective time interval. The system may then determine a stress resilience metric of the user based on a weighted sum of the stress indices, recovery indices, and sleep recovery indices of the plurality of time intervals, where the stress resilience metric indicates a relative capability of the user to cope with and / or recover from stress.

Methods, systems, and devices for measuring compressive toughness are described. A system may obtain heart rate variability (HRV) data from a user via a wearable device over a plurality of time intervals, wherein each time interval includes a waking interval at which the user wakes and a sleeping interval at which the user falls asleep. The system may determine a stress index and a recovery index associated with a waking interval of the respective time interval and a sleep recovery index associated with a sleeping interval of the respective time interval. The system may then determine a compressive toughness metric for the user based on a weighted sum of the pressure index, the recovery index, and the sleep recovery index for multiple time intervals, where the compressive toughness metric indicates a relative ability of the user to handle and / or recover from pressure.

The invention relates to the technical field of Internet of Things communication and metering instruments, and particularly discloses an intelligent water meter data monitoring system based on NB-IoT, which is characterized in that an inherent system frame number in a downlink signal of a base station is used as a high-precision network timestamp, and the high-precision network timestamp is associated with a local clock value to form a reference; periodically collecting the time pairs and storing the time pairs into a queue; calculating the drift rate of the local clock relative to the network time by analyzing the ratio relationship of a plurality of time pairs in the queue; when the drift rate continuously exceeds the limit, triggering calibration, and carrying out dynamic compensation calculation on a preset sleep interval according to the drift rate to obtain a corrected actual sleep interval and executing the corrected actual sleep interval; downlink signals in the communication process are multiplexed to serve as a free time source, continuous monitoring and dynamic adjustment are achieved, and long-term reliable synchronization of the intelligent water meter and the network in the whole life cycle is guaranteed on the premise that hardware cost is not increased and power consumption is not remarkably affected.

The invention relates to the field of medical data processing, in particular to a neurology department patient health sign monitoring data processing method, which comprises the following steps: acquiring a plurality of sign data sets of neurology department patients; dividing each physical sign data set into a plurality of sleep stage data sets; dividing each sleep stage data set into a plurality of sleep intervals; obtaining suspicious data of each physical sign data set according to the interval length of each sleep interval; obtaining a suspicious interval of each physical sign data set; obtaining a first abnormal weight of each piece of suspicious data according to the volatility of each piece of suspicious data and the length of the suspicious interval to which the suspicious data belongs; obtaining comparison data of each piece of suspicious data, obtaining a second abnormal weight of each piece of suspicious data according to the comparison data, and further obtaining an abnormal degree of each piece of suspicious data; and obtaining abnormal data according to the abnormal degree of each piece of suspicious data so as to realize targeted identification of the abnormal signs of the neurology patient. The method is more efficient, more convenient and more targeted.

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 present invention relates to the field of medical data processing, and particularly to a method for processing health sign monitoring data of neurological patients, including: obtaining multiple sign data sets of neurological patients; dividing each sign data set into multiple sleep stage data sets; dividing each sleep stage data set into multiple sleep intervals; obtaining suspicious data of each sign data set according to the interval lengths of the respective sleep intervals; obtaining the suspicious intervals of each sign data set; obtaining the first abnormal weight of each suspicious data according to the volatility of each suspicious data and the length of the suspicious interval to which it belongs; obtaining respective comparison data of each suspicious data, and obtaining the second abnormal weight of each suspicious data according to the comparison data, thereby obtaining the abnormal degree of each suspicious data; obtaining abnormal data according to the abnormal degree of each suspicious data, thereby realizing the targeted identification of abnormal signs of neurological patients. The present invention is more efficient, convenient, and more targeted.

Methods, systems, and devices for evaluating a sleep quality of a user using wearable-based data are described. The sleep quality of a user may be evaluated by combining physiological data collected via a wearable device, along with environmental sound data collected via one or more external devices. For example, a microphone on a device may monitor environmental sounds while a user sleeps, and the environmental sounds may be used to improve sleep stage classification. Additionally, or alternatively, sound instances occurring throughout a sleep interval may be identified. In some examples, sound data may be combined with other data, such as physiological data, to determine relationships between the sleep sound data and sleep quality. An indication of the sleep stages and transitions between the sleep stages, the sound instances, the sleep quality, or a combination thereof, may be presented to the user via an application.

A method is provided for running a maximum power point tracker (MPPT) in a controller module. The method includes running the MPPT during an MPPT active update cycle; setting an adaptive sleep interval, tsleep, depending on whether or not power control settings were changed during the MPPT active update cycle; and sleeping for the tsleep amount of time before returning to running the MPPT for a new MPPT active update cycle. Further provided are an MPPT state machine implemented the method, a controller module including such a MPPT state machine, a power manager including such controller module, an energy harvesting system including such power manager and a device including such energy harvesting system.

The application relates to the technical field of Internet of Things communication and metering instrument, and particularly discloses an intelligent water meter data monitoring system based on NB-IoT, which utilizes inherent system frame numbers in base station downlink signals as high-precision network time stamps, associates the time stamps with local clock values to form a reference, periodically collects the time pairs and stores the time pairs in a queue, analyzes the ratio relationship of multiple time pairs in the queue, calculates the drift rate of the local clock relative to the network time, triggers calibration when the drift rate continuously exceeds the limit, dynamically compensates the preset sleep interval according to the drift rate, obtains a corrected actual sleep interval, and executes the actual sleep interval, utilizes the downlink signals in the multiplexing communication process as a free time source, realizes continuous monitoring and dynamic adjustment, and ensures long-term and reliable synchronization between the intelligent water meter and the network in the whole life cycle without increasing hardware cost and without significantly affecting power consumption.

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.

A method of reducing a power consumption of a wireless device according to one embodiment includes performing, by the wireless device, a calibration of wireless communication circuitry of the wireless device in response to establishing a wireless communication connection with a wireless access point, determining, by the wireless device, a number of disconnections between the wireless device and the wireless access point over a predefined period of time, and increasing, by the wireless device, a sleep interval of the wireless communication circuitry of the wireless device in response to determining the number of disconnections between the wireless device and the wireless access point over the predefined period of time is less than a threshold number of disconnections.

The invention relates to a method for operating a maximum power point tracker (MPPT) in a controller module, in particular to a method for operating a maximum power point tracker (MPPT) in a controller module. The method comprises operating (221) the MPPT during an active MPPT update period; setting (231) an adaptive sleep interval t sleep according to whether a power control setting changes during the MPPT active update period; and sleeping (241) for an amount of time of the t-sleep before returning to running the MPPT within the new MPPT active update cycle. Further presented is an MPPT state machine implementing the method, a controller module comprising such an MPPT state machine, a power manager comprising such a controller module, an energy harvesting system comprising such a power manager, and an apparatus comprising such an energy harvesting system.

Methods, systems, and devices for measuring resilience to stress are described. A system may acquire heart rate variability (HRV) data from a user via wearable device throughout multiple time intervals, where each time interval includes an awake interval that the user is awake and an asleep interval that the user is asleep. The system may determine a stress index and a recovery index associated with the awake interval of a respective time interval, and a sleep recovery index associated with the asleep interval of the respective time interval. The system may then determine a stress resilience metric of the user based on a weighted sum of the stress indices, recovery indices, and sleep recovery indices of the plurality of time intervals, where the stress resilience metric indicates a relative capability of the user to cope with and / or recover from stress.

The application relates to an air conditioner control method and device, an air conditioner, a storage medium and a computer program product. When the air conditioner enters a sleep mode, an initial human body temperature and a real-time human body temperature can be acquired, and the current human body sleep interval is analyzed in combination with the initial human body temperature and the real-time human body temperature. Finally, the air conditioner is controlled to operate according to the analyzed human body sleep interval. In the sleep mode, the initial human body temperature and the real-time human body temperature are combined in real time, the operation control of the air conditioner is realized, the operation of the air conditioner changes according to the change of the real-time human body temperature, and the temperature regulation intelligence of the air conditioner in the sleep mode is greatly improved.

This application discloses a model creation method, apparatus, device, and storage medium. The method includes: obtaining the real-time running phase of a real-time model creation task; determining a real-time sleep interval time matching the real-time running phase based on the real-time running phase; during the real-time running phase, pausing the real-time model creation task once every real-time sleep interval time; after each pause of the real-time model creation task, querying the real-time non-model creation tasks in the task list; if a real-time non-model creation task exists, obtaining the real-time sleep time matching the real-time non-model creation task; wherein, the real-time non-model creation task is executed based on the real-time sleep time; and after waiting for the real-time sleep time, the real-time model creation task continues. The technical solution of this application achieves both a good user interaction experience on the editing page and the ability to complete batch model creation tasks in the shortest possible time.

A wearable physiological monitoring device is configured to determine a wearer's sleep intention. In particular, the device evaluates activity that might be objectively associated with awaking from sleep in order to distinguish between a wearer's intention to end sleep and transitory stirring or other intermittent activity occurring in the context of a longer sleep interval. By evaluating sleep intention more accurately in this manner, the device can advantageously request sleep analysis from a remote server in those instances where a user intends to end a period of sleep (so that this data can be available to the user more quickly), while avoiding unnecessary or redundant server-side processing of sleep data in those instances where a user intends to continue sleeping.

The invention provides a sleep state judgment system and method using biological activity Doppler signals. A sleep state determination system using a biological activity Doppler signal according to an embodiment of the present invention comprises: a Doppler signal acquisition unit that acquires a Doppler signal including biological activity information using a radar; an auxiliary signal processing unit that uses a thermal imaging sensor and a noise sensor to acquire a turning-over action and snoring noise as auxiliary signals; a Doppler signal analysis unit that analyzes the Doppler signal, acquires spectral energy at a preset period, determines whether or not the spectral energy is periodically acquired, and uses the auxiliary signal to distinguish the spectral energy; and a sleep interval definition unit that defines the sleep state of each interval in the entire sleep interval by using a combination of the aperiodic spectrum energy and the ratio of the breathing spectrum energy to the heartbeat spectrum energy among the spectrum energy.