A sleep assessment system includes a blood flow measurement unit and a assessment unit. The blood flow measurement unit acquires first information related to the blood flow of the user. The assessment unit determines the sleep stage of the user based on the first biological information.

A system for monitoring a sleep of a user includes a plurality of patches for placement adjacent to a surface of a body of a user, a processor, and a data communication system. Each patch from the plurality of patches includes at least one sensor. The data communication system transmits positional data generated by the plurality of sensors, including orientation data and motion data, to the processor. The processing of the positional data includes detecting a change in position of the body of the user between a first position and a second position. Based on the first image and the second image, an animation of a movement of the body from the first position to the second position is generated.

A wheel chart for visualizing sleep pressure on a person's body by manipulating a series of moveable circular disks positioned on a common axis wherein the wheel includes a first disk representing a circadian (C) process and second disk representing a homeostatic (S) process. The disks are rotatable relative to each other to simulate how the C and S processes increase and decrease sleep pressure.

A system for monitoring and measuring itch of a patient includes a wearable device including an actigraph sensor; and a controller electrically connected to the wearable device and including a processor and a memory. The wearable device is configured to measure, via the actigraph sensor, a movement of the patient; and send data indicative of the measured movement of the patient to the controller. The controller is configured to receive, via the processor, the data indicative of the measured movement; and determine, via the processor, a scratching movement of the patient based on the data indicative of the measured movement.

A computer system for assessing sound to analyze a user's sleep includes a processor configured to perform operations including: (i) storing sample sound data associated with a plurality of sample sleep events, the sample sound data including a plurality of sample characteristics each respectively associated with at least one sample sleep event of the plurality of sample sleep events; (ii) receiving, from a client device, subject sound data collected during a sleep interval; (iii) analyzing, using a machine learning algorithm, the subject sound data collected during the sleep interval; (iv) identifying, based upon the analyzing, a subject characteristic associated with the subject sound data; (v) comparing the subject characteristic with the plurality of sample characteristics; and (vi) determining, based upon the comparing, whether the subject characteristic substantially matches at least one sample characteristic to identify one or more subject sleep events occurring during the sleep interval.

Provided are a control and apparatus for a wearable device, an electronic device, and a computer-readable storage medium. The wearable device has a first operation mode and a second operation mode. The first operation mode is a mode for running a first system and a second system. The second operation mode is a mode for running only the second system. The first operation mode has a higher power consumption than the second operation mode. The control method includes: obtaining user behavior data (102), determining a user behavior status based on the user behavior data (104), and switching, in response to detecting that the user behavior status is a sleep status, the wearable device from the first operation mode to the second operation mode (106).

Methods, apparatus and systems for radio-based sleep tracking are described. In one example, a described system comprises: a transmitter configured to transmit a first wireless signal through a wireless multipath channel in a venue; a receiver configured to receive a second wireless signal through the wireless multipath channel, wherein the second wireless signal differs from the first wireless signal due to the wireless multipath channel which is impacted by a sleeping motion of an object in the venue; and a processor. The processor is configured for: obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the second wireless signal, wherein each channel information (CI) of the TSCI comprises N1 components, wherein N1 is a positive integer larger than one, computing N1 component-wise analytics each associated with one of the N1 components of the TSCI, identifying N2 largest component-wise analytics among the N1 component-wise analytics, wherein N2 is a positive integer less than N1 computing at least one first motion statistics based on the N2 largest component-wise analytics of the TSCI, and monitoring the sleeping motion of the object based on the at least one first motion statistics.

Embodiments are disclosed for sleep staging using machine learning. In an embodiment, a method comprises: receiving, with at least one processor, sensor signals from a sensor, the sensor signals including at least motion signals and respiratory signals of a user; extracting, with the at least one processor, features from the sensor signals; predicting, with a machine learning classifier, that the user is asleep or awake based on the features; and computing, with the at least one processor, a sleep or wake metric based on whether the user is predicted to be asleep or awake.

A device, system, and method for facilitating a sleep cycle in a subject suing a peri COVID vaccination period, comprising determining a current awake or sleep stage of a person; automatically defining a desired sleep cycle pattern, dependent on the current awake or sleep stage of the person; generating an audio or optical stimulation pattern by an automated processor; entraining brainwaves of the brain of the person with the stimulation pattern corresponding to the desired sleep cycle pattern, to thereby induce a sleep cycle in the person according to the sleep cycle pattern; and administering the SARS-Cov-2 vaccination to the person.

A sleep induction device, wherein the control unit reads a stimulation rate that is associated with a stimulation part from a storage unit, calculates a stimulation candidate by making a shift in the stimulation rate by a predetermined amount, outputs a drive signal according to the stimulation candidate, acquires a detection time from start of output of the drive signal to detection of sleep onset by the sensor, and causes the storage unit to store the stimulation candidate and the detection time as the stimulation rate and a sleep induction time, in association with the stimulation part, when the detection time is shorter than the sleep induction time.