Intraoral electronic sensing for health monitoring is disclosed. Wireless connectivity is provided between a processor and a wireless transmitting device. The wireless transmitting device is embedded in an intraoral sensing interface for use in a person. Sensors are coupled to the wireless transmitting device, wherein the sensors are attached to the intraoral sensing interface. The sensors include a photoplethysmography (PPG) sensor to detect cardiac activity, a breathing sensor to detect pulmonary function, an inertial measurement unit (IMU) sensor to detect three-dimensional motion, and a temperature sensor to monitor body temperature. Further sensors include an electroencephalogram sensor to detect brain activity. Health data about the person is provided to a receiving device, based on data from one or more of the PPG sensor, the breathing sensor, the IMU sensor, and the temperature sensor. The health data is provided using the wireless connectivity.

A sleep assessment system includes a housing, processing circuitry, and a sensor assembly with a plurality of sensors configured to capture measurements that include indications of both brain and eye activity through the forehead of a subject. The processing circuitry is configured to receive sensor signals based on the measurements made by the plurality of sensors, process the sensor signals to generate sleep data, apply a sleep state convolutional neural network to the sleep data to determine a current sleep state of a subject, identify, based on the sleep data and the current sleep state, a sleep state-based data feature, and output a stimulus to the subject based on sleep state-based data feature. The housing is configured to be secured to the forehead of the subject, and the sensor assembly and processing circuitry are disposed on or within the housing.

A sleep state sensing method and a sleep state sensing system are provided. The sleep state sensing method includes: sensing a designated physiological state through an electronic device to generate a first physiological signal group and sensing the designated physiological state through a physiological signal sensing instrument to generate a second physiological signal group; calibrating the electronic device based on the first physiological signal group and the second physiological signal group such that the electronic device generates a third physiological signal group; and determining a sleep state through the third physiological signal group.

Provided are an apparatus, method, and computer-readable medium for controlling a biological clock and a sleep cycle. The apparatus may include a collection unit configured to receive a brain image of a subject, a stimulation control unit configured to analyze the brain image, set a target point to be stimulated in a target region located in the brain, and derive optimized stimulation conditions, and a stimulation unit configured to apply stimulation according to the stimulation conditions derived by the stimulation control unit, wherein the stimulation control unit comprises a target setting unit configured to analyze the brain image, acquire anatomical central coordinates of the target region, and set the target point according to the acquired central coordinates and a stimulation setting unit configured to configure a stimulation electrode combination according to the set target point and derive a stimulation function according to a stimulation modulation depth maximization function (M.sub.MAX).

The invention relates to a device, a method, a computer program product and an application for establishing a current load level of a user. The device and the method comprise a mobile terminal, which comprises at least one sensor generating signal data and a plurality of available applications for use by the user and also an evaluation unit. According to the invention, provision is made for the mobile terminal to comprise an application, which is configured as further application and establishes a plurality of biometric data in respect of the user, at least from the signal data or from available applications used by the user, and to make said data available to the evaluation unit, and for the evaluation unit to determine the current load level of the user from the biometric data.

Various types of data can be collected regarding the physical or mental health of a user, as may relate to sleep of the user over a period of time. Health metrics can be determined from this data that can enable the user to be associated with a particular health type or category. For sleep, this can include associating the user with a sleep animal that has specific characteristics. This can help a user to better understand that user's sleep, and how that sleep compares to sleep of others. In addition to being able to provide health information in a way that is easy to understand, such an approach can also help to make more accurate recommendations or take specific actions to help improve the health of a user, such as to improve sleep. This can include making recommendations to a user or automatically adjusting operation of at least one device.

Methods, techniques, apparatuses, and systems for setting up and tracking sleep consistency goals of users are provided. In one example, a computing system for setting a sleep schedule of a user of a biometric monitoring device may obtain sleep data derived from sensor data generated by the biometric monitoring device, store the sleep data in a sleep log data store as one or more sleep logs associated with an account assigned to the user, and calculate a target bedtime based on a scheduled waketime of the user and a sleep efficiency derived, at least in part, from the sleep data for one or more users stored in the sleep log data store. The computing system may also be configured to provide a number of personalized user interfaces to an individual for the purposes of setting a sleep schedule. Such interfaces may include parameters that are tailored to the individual sleep needs and/or characteristics of the individual's sleep.

A wearable device and methods for providing a wearable device are disclosed. In a first aspect, the wearable device comprises at least one power source, one computer controller and a plurality of instruments that when worn on a user access physiological data from at least the user axilla. The wearable device monitors one or more or a combination of body temperature, pulse, R-R interval, respiration rate, pulse ox (SpO2), sleep, movement included fall detection. The device stores, processes and communicates collected or processed data to an external computer system. A software system provides summary information, reporting and alarms based on data collected by the one or more instruments.

Apparatus and associated methods relate to a connected pill dispenser configured to treat a patient's illness with a robotic arm adapted to deliver a prescribed medication dose to a patient in their home, analyze the patient's response to the dose based on various sensor inputs, automatically determine whether the patient took the medicine and send the provider the result of providing the medication dose to the patient. In an illustrative example, the patient may be a chronic, acute, or terminal illness patient. The medication dose may be, for example, a medication prescribed by a doctor to be taken in a specific amount at specific times. In some embodiments, the connected pill dispenser may automatically notify the patient when a medication dose is due. Various embodiments may determine if the patient consumed a medication dose based on machine vision, audio, or other sensor data, permitting caregiver notification of patient medication adherence, medication compliance, or non-adherence and sensor inputs as data on potential effectiveness and/or side effects.

The present invention provides systems, methods, and articles for stress reduction and sleep promotion. A stress reduction and sleep promotion system includes at least one remote device, at least one body sensor, and at least one remote server. In other embodiments, the stress reduction and sleep promotion system includes machine learning.