Introduced are methods and systems for an adjustable bed device configured to: gather biological signals associated with multiple users, such as heart rate, breathing rate, or temperature; analyze the gathered human biological signals; and heat or cool a bed based on the analysis.

A sleep-aiding audio signal updating method and apparatus in the artificial intelligence (AI) field is provided. The method includes: obtaining a first biological signal collected when a first audio signal in a sleep-aiding audio library is played, where the first biological signal is a biological signal of a first user; determining a sleep quality of the first user based on the first biological signal; and updating the sleep-aiding audio library based on the sleep quality of the first user, so that a sleep-aiding audio signal can be updated, to provide a proper sleep-aiding audio signal for a user, and ensure a sleep-aiding effect.

According to certain embodiments, an electronic device comprises: a sensor module; a processor operatively connected to the sensor module; and a memory operatively connected to the processor, wherein the memory stores instructions that, when executed, cause the processor to perform a plurality of operations comprising: acquiring sensor information through the sensor module, determining sleep information of a user, based on the sensor information, and determining a biorhythm of the user, based on the sleep information.

The present disclosure relates to systems and methods for detecting sleep-wake activity of a subject using change-point events determined from physiological and/or movement measures. In one implementation, the method may include obtaining at least one set of sensor data generated by one or more sensors for a period of time. The method may also include generating at least two measures from the at least one set of sensor data. The method may further include determining a series of change point events for each measure for the period of time. The method may include determining a sleep stage for each interval of the period of time from at least two sleep stages by processing the series of change point events for each measure using a sleep stage classifier. The sleep stage classifier may include a set of parameters for each measure.

The present invention provides a construction method for automatic sleep staging and use thereof. The construction method for automatic sleep staging comprises: acquiring a plurality of sets of PSG signals and manual sleep information of PSG signals; pre-analyzing to decompose the original time series in the PSG signals into a set of pseudo-intrinsic mode functions (pseudo-IMFs); assembling the pseudo-IMFs to obtain m sets of time series; analyzing by multiscale entropy (MSE), to calculate the entropy values of the m sets of time series on n coarse-graining timescales, thus obtaining an entropy matrix with m*n elements; establishing a correlation coefficient matrix between the levels of consciousness and the elements in the entropy matrix, and finding the coarse-graining timescale and filtering timescale corresponding to the most significantly positively correlated element or the most significantly negatively correlated element in the correlation coefficient matrix; and calculating the entropy value on the coarse-graining timescale and filtering timescale corresponding to the most significantly positively correlated element or the most significantly negatively correlated element, and assessing the sleep state according to the entropy value.

A brainwave feedback system, adapted to generate feedback based on a user's brainwave, the brainwave feedback system comprises: a brainwave sensing device, configured to obtain a first brainwave signal of the user; a server, storing a keyword string pool including a plurality of sorted keywords, and performing a feedback procedure when a first physiological parameter falls outside of a predetermined parameter range, wherein the first physiological parameter is associated with the first brainwave signal, the feedback procedure includes choosing a keyword from the keyword string pool by the server as a feedback keyword, and outputting the feedback keyword; and an output component, in communicable connection with the server, wherein the output component presents an analysis result corresponding to the first physiological parameter. The present disclosure further discloses an operation method of brainwave feedback system.

Physiological parameters such as respiratory rate and/or heart rate variability can be measured over time for a user and correlated to physiological and/or hormonal cycles such as the menstrual cycle. By determining the phase of such a cycle in this manner, an automatic coach for the user can recommend phase-specific adjustments to activities such as sleep and exercise.

A wearable device includes a housing with a window and an electronic module supported by the housing. The electronic module includes a photoplethysmography sensor, a motion sensor, and a signal processor that processes signals from the motion sensor and signals from the photoplethysmography sensor. The signal processor is configured to remove frequency bands from the photoplethysmography sensor signals that are outside of a range of interest using a band-pass filter to produce pre-conditioned signals, and to further process the pre-conditioned signals using the motion sensor signals to reduce motion artifacts from footsteps during subject running. The device includes non-air light transmissive material in optical communication with the photoplethysmography sensor and the window that serves as a light guide for the photoplethysmography sensor. The window optically exposes the photoplethysmography sensor to a body of a subject wearing the device via the non-air light transmissive material.

Aspects of the present disclosure provide methods, apparatuses, and systems for closed-loop sleep protection and/or sleep regulation. According to an aspect, a biosignal parameter and ambient noise are measured. The biosignal parameter is used to determine sleep condition of a subject. The sleep condition is determined based on one or more of personalized sleep data or historical sleep data collected using a subset of society. Based on the sleep condition, an arousal threshold is determined. Based on the ambient noise and the determined sleep condition, one or more actions are taken to regulate sleep and avoid sleep disruption.

A sleep onset determination method includes: detecting a light-out time that is a time at which illuminance in a room becomes less than or equal to a predetermined value; storing the light-out time detected in the detecting of the light-out time and a time period including the light-out time detected; and determining whether a user fell asleep at a currently detected light-out time, based on the light-out time and the time period stored in the storing of the light-out time.