Described are systems for beds that can include sensors for sensing physical phenomena in an environment surrounding a bed, a display for outputting information about the environment, the bed, and a sleeper, and a controller communicably coupled to the sensors. The controller can receive the sensed physical phenomena from the sensors, analyze the physical phenomena to determine at least one of environmental, sleep, and health metrics of a sleeper in the bed, and determine, based on at least one of the environmental, sleep, and health metrics of the sleeper, control signals to modify the environment surrounding the bed. The controller can also output, at the display, the environmental, sleep, and health metrics of the sleeper. The controller can also transmit the control signals to a second controller in order to engage a home automation device. The physical phenomena can include ambient sound, ambient light, ambient CO2 concentration, and/or ambient temperature.

A device and system for promoting more recuperative sleep by regulating a user's body temperature. This may be done by using a series of devices that measure information about the user both while they are awake and while they are asleep, communicate that information to a processing unit, and create an ideal body temperature range profile based on that information. A temperature stimulus device may ensure that the core body temperature of the user stays substantially within the ideal body temperature range. By keeping the core body temperature of the user within the calculated range, the device and system will ensure deeper, and therefore more recuperative, sleep.

This disclosure is related to measuring an individual's executive function under both external and internal pressures. A variety of data points and sensor data may be used to measure executive function data, including, but not limited to: first physiology data, user provided engagement factor data, and mental status data These data points may be converted into first physiology data and engagement factor data, which may be further converted into lifestyle factor data to generated, a first user score, a second user (and/or a third user) score—each score measuring performance under various different cognitive tasks or loads. The scores may be used to measure the individual's executive function under various pressure or load situations.

A method and system are disclosed for use in monitoring/screening/diagnosing sleep or wake state of a subject or patient. The method generally includes monitoring the patient's activity during one or more sleep sessions comprising a plurality of intervals known as epochs. The sleep/wake state of the subject is determined during each epoch of the session using actigraphy data obtained during the monitoring session. The actigraphy data provides information about the activity of a patient during an epoch. The sleep or wake state is determined based on a ratio of the activity count during an epoch to the activity count during a preceding epoch. If the ratio is greater than a first activity threshold, then a “wake” indication may be provided by, for example, the system. Alternatively, or additionally, a “wake” indication may be determined if the activity count during the epoch is greater than a threshold.

A system and method for identifying sleep states of a subject are provided. In some aspects, the method includes acquiring physiological data from a subject over a sleep period using sensors positioned about the subject, and assembling the physiological data into time-series datasets. The method also includes selecting a temporal window in which signals associated with the time-series datasets are substantially stationary, computing a time bandwidth product based on a selected spectral resolution and the selected temporal window, and determining a number of tapers using the computed time bandwidth product. The method further includes computing a spectrogram using the determined number of tapers and the time-series datasets, analyzing the spectrogram to identify signatures of sleep in the subject, and generating, using the identified signatures, a report indicative of sleep states of the subject.

A personalized parameter learning method, a sleep-aid device and a non-transitory computer readable medium are provided. The personalized parameter learning method for a sleep-aid device is provided. The personalized parameter learning method includes the following steps. A process device computes a measured sleep quality of a user after operating a sleep-aid device with an inputted parameter setting at least according to a subjective feedback from the user. The processing device generates a plurality of candidate parameter settings according to the measured sleep quality. The processing device generates a plurality of predicting sleep qualities corresponding the candidate parameter settings. The processing device obtains a recommending parameter setting by selecting one of the candidate parameter settings according to the predicting sleep qualities.

A wearable device for healthcare and a method thereof, wherein the device is worn on a finger for measuring the health data of the user, including but not limited to, the heart rate, blood oxygen saturation, etc. The wearing size of the device is adjustable for different sizes of fingers. In one embodiment, the device includes a main body at least partially worn on a digit of a user; at least one physiological sensor attached to the main body for detecting physiological information; and at least two branches coupled to the main body for holding the digit while reducing the movement of the device. At least a part of at least one branch is changeable such that the wearing size of the device is adjustable for different sizes of digits. In another embodiment, at least a part of at least one branch is movable such that the wearing size of the device is adjustable for different sizes of digits.

Determining low power frequency range information from spectral data. Raw signal data can be adjusted to increase dynamic range for power within low power frequency ranges as compared to higher-power frequency ranges to determine adjusted source data valuable for acquiring low power frequency range information. Low power frequency range information can be used in the analysis of a variety of raw signal data. For example, low power frequency range information within electroencephalography data for a subject from a period of sleep can be used to determine sleep states. Similarly, automated full-frequency spectral electroencephalography signal analysis can be useful for customized analysis including assessing sleep quality, detecting pathological conditions, and determining the effect of medication on sleep states.

Some systems, devices and methods detailed herein provide a system for use in determining metrics of a subject. The system can provide, as an output, a function-metric value determined based on a defined relationship between physiological measures and a chronological age.

The present invention relates to a method for improving sleep quality, comprising the steps of measuring pressure by means of sensors in locations distributed by regions of a mattress; calculating the SQI based on the prominent movements detected depending on the time of night; calculating the mean pressure measured by each sensor; calculating the difference between the mean pressure and the pressure measured by that sensor when there is no user on the mattress; calculating the mean pressure difference for each region of the mattress; calculating a weight factor for each region of the mattress; comparing the weight factor with a reference value; varying the configuration of the mattress by increasing or reducing the support level in the regions. The present invention also relates to a related system and mattress.