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.

Methods, systems, and devices for controlling external devices are described. A method may include receiving physiological data associated with a user from a wearable device, and identifying one or more physiological states, physical activities, or both, associated with the user based on the physiological data. Physiological states may include physiological states associated with waking up, falling asleep, anxiety, relaxation, and the like. The method may further include transmitting an instruction to one or more external devices based on the one or more physiological states, physical activities, or both, where the instruction is configured to selectively modify one or more operational parameters associated with the one or more external devices.

A healthcare apparatus includes a BCG sensor; a camera; and a processor configured: to detect a ROI) corresponding to the face from the color facial image; to convert the detected first color image into a black and white image to acquire a first black and white image; to convert the detected second color image into a black and white image to acquire a second black and white image; to apply the acquired first black and white image and the acquired second black and white image to a predetermined trained algorithm model to output a remote photoplethysmography (rPPG) signal waveform of the subject; to calculate a first stress index based on the first heart rate variability; to calculate a second stress index based on the second heart rate variability; and to output a stress index of the subject based on the first stress index and the second stress index.

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).

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.

The wake-up detection device may include a sensor configured to detect a movement of a person and a biosignal of the person; and a controller configured to analyze the movement and the biosignal recognized by the sensor, and determine whether the person wakes up from sleep, on the basis of a result of analyzing the movement and the biosignal. The controller may determine a change in a heart rate of the person when the person converts from a sleep state to a non-sleep state, and when it is determined that the change in the heart rate and the movement of the person increase, the controller may output a first alarm.

Systems and methods for enhancing wellness in a habitable space are provided. In some forms, an example system for enhancing wellness in a habitable space includes a control system having a processor, communication circuitry, and a memory. The communication circuitry of the control system is configured to communicate with one or more devices positioned within the habitable space. One or more sensors may also be positioned proximate the habitable space to detect indicia (e.g., biometric characteristics of a user, behavioral characteristics of a user, and environmental characteristics) and communicate the detected indicia to the control system. Based at least in part on the detected indicia, the control system is configured to trigger initiation of a scene in the habitable space by communicating a signal to at least one of the devices in the habitable space to adjust operation thereof.

Systems, devices, and methods for remote monitoring and managing of patients with chronic pain are discussed. A remote monitoring system comprises a cloud-computing device and a remote device. The cloud-computing device receives patient data including physiological or functional information sensed by sensors, and provides on-demand cloud-based services including establishing a correspondence between one or more physiological or functional states and one or more pain levels, detecting patient physiological or functional state, predicting a pain level, detecting a patient behavior, generating a recommendation for adjusting sensor operations based on the patient behavior, and storing patient data and other information in a cloud storage. The remote device can access the cloud storage and the cloud-based services, provide the stored information to an authorized user or the patient, control an implantable device to initiate or adjust a neuromodulation therapy, or adjust sensor operations.

An ambulatory medical device configured to analyze heart rates in different operating modes includes a plurality of ECG sensing electrodes, a plurality of therapy electrodes and at least one processor configured to in a default operating mode, perform a default heart rate calculation for determining a heart rate of the patient for use in detecting a cardiac arrhythmia condition of the patient. The at least one processor is configured to change a device operating mode from a default mode based on detecting patient activity to an activity operating mode, and in the activity operating mode, perform a different heart rate calculation from the default heart rate calculation for determining the heart rate for use in detecting the cardiac arrhythmia condition of the patient during the activity operating mode. The at least one processor is configured to deliver the treatment in response to detecting the cardiac arrhythmia condition.