Disclosed are a multiple physiological data collection device and system that collect, manually mark, sampling—physiological data for machine learning and AI analyses in a single operation background. Physiological data uploaded by sensing devices of different type and function, described in different formation, recorded at different times and/or pertaining to different person can be processed in one system. The invented system comprises a data uploading device, a data storage device and a data edition device and, optionally, an automated data analysis device.

The derivation unit determines the stability of a parasympathetic nerve activity of the sleeper for each environment temperature, and derives a correspondence relationship between the environment temperature and the stability of the parasympathetic nerve activity of the sleeper. An estimation unit estimates an appropriate value of the environment temperature suitable for the sleeper based on the correspondence relationship derived by the derivation unit.

Methods and systems for remote sleep monitoring are provided. Such methods and systems provide non-contact sleep monitoring via remote sensing or radar sensors. In this regard, when processing backscattered radar signals from a sleeping subject on a normal mattress, a breathing motion magnification effect is observed from mattress surface displacement due to human respiratory activity. This undesirable motion artifact causes existing approaches for accurate heart-rate estimation to fail. Embodiments of the present disclosure use a novel active motion suppression technique to deal with this problem by intelligently selecting a slow-time series from multiple ranges and examining a corresponding phase difference. This approach facilitates improved sleep monitoring, where one or more subjects can be remotely monitored during an evaluation period (which corresponds to an expected sleep cycle).

A patient monitoring device configured to monitor cardiac activity and sleep stage information of a patient is provided. The device includes a plurality of electrodes to acquire electrocardiogram (ECG) signals from the patient, at least one motion sensor configured to generate a motion signal based upon movement of the patient, and at least one processor. The processor is configured derive motion parameters from the motion signal, derive ECG parameters from the ECG signals, determine whether the patient is in an immobilized sleep stage or a non-immobilized sleep stage based upon the motion parameters and the ECG parameters, adjust one or more cardiac arrhythmia detection parameters such that the device operates in a first monitoring and treatment mode when the patient is in an immobilized sleep stage, and monitor the patient for the cardiac arrhythmia using the first monitoring and treatment mode.

An audio system adapted to provide an auditory stimulation for inducing neural oscillations in a user during sleep.

A method includes applying, via a respiratory therapy system, initial therapy settings for a user during a first sleep session in which the user uses the respiratory therapy system. First physiological data, which is received from one or more sensors, is generated during the first sleep session. Modified therapy settings are applied, via the respiratory therapy system, during a second sleep session of the user. Second physiological data is received from the one or more sensors. The second physiological data is generated by the one or more sensors during the second sleep session. A set of sleep-related parameters is determined based on changes between the first physiological data and the second physiological data. One or more of a recommended therapy or recommended therapy settings is determined based on the set of sleep-related parameters.

A robot according to the present disclosure comprises: a microphone; a camera disposed to face a predetermined direction; and a processor configured to: inactivate driving of the camera and activate driving of the microphone, if a driving mode of the robot is set to a user monitoring mode; acquire a sound signal through the microphone; activate the driving of the camera based on an event estimated from the acquired sound signal; confirm the event from the image acquired through the camera; and control at least one constituent included in the robot to perform an operation based on the confirmed event.

In certain example embodiments, an air delivery system includes a controllable flow generator operable to generate a supply of pressurized breathable gas to be provided to a patient for treatment and a pulse oximeter. In certain example embodiments, the pulse oximeter is configured to determine, for example, a measure of patient effort during a treatment period and provide a patient effort signal for input to control operation of the flow generator. Oximeter plethysmogram data may be used, for example, to determine estimated breath phase; sleep structure information; autonomic improvement in response to therapy; information relating to relative breathing effort, breathing frequency, and/or breathing phase; vasoconstrictive response, etc. Such data may be useful in diagnostic systems.

Traditional analysis of sleep patterns requires several channel of data. This analysis can be useful for customized analysis including assessing sleep quality, detecting pathological conditions, determining the effect of medication on sleep states and identifying biomarkers, and drug dosages or reactions.

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.