A portable electronic device configured to be positioned on a users wrist, the portable device being configured to perform an electrocardiogram, ECG, the portable electronic device includes a watchcase, a case back, configured to be at least partially in contact with the skin of the wrist, a glass, a bezel, mounted on the watchcase and surrounding the glass, movable in rotation with respect to the watchcase, a first ECG electrode, made of conductive material, on the case back and configured to be in contact with the skin of the wrist, a second ECG electrode, made of conductive material, on the bezel, an ECG electronic module, electrically connected to the first ECG electrode and the second ECG electrode, and configured to receive and process electrical signals from a user and retrieved by the ECG electrodes, to perform an electrocardiogram.

A method for checking a PPG sensor of a hearing apparatus that has a rechargeable battery. A charger has a receiving space for the hearing apparatus and a testing environment for testing the functionality of the PPG sensor. The hearing apparatus is placed in the receiving space and the test environment is used to perform a function test on the PPG sensor.

Patient sleep is staged using personalized circadian models built with data collected by wearable devices over daytime and nighttime hours, thus capturing a patient's personal circadian rhythms. The circadian model is used to identify sleep intervals in incoming nightly data for the patient. The identified sleep intervals are analyzed by the machine learning system which stages epochs of sleep. Methods include receiving patient heart rate data from over a plurality of circadian cycles; creating a circadian model for the patient with a defined operation for applying sleep labels to new data from the wearable device; applying the circadian model to nightly test data from the device to identify a sleep interval; and assigning, with a classifier, sleep stages to epochs of the sleep interval.

A physiological monitoring device is provided and includes a physiological sensing device, a first PPG sensor, a vital signs detector, and a PPG controller. The physiological sensing device senses at least one physiological feature of a subject to generate at least one sensing signal. The first PPG sensor senses pulses of a blood vessel of the subject to generate a first PPG signal when the first PPG sensor is activated. The vital signs detector obtains vital signs data according to the at least one sensing signal. The PPG controller detects whether a specific event is happening to the subject according to the vital signs data. In response to detecting that the specific event is happening to the subject, the PPG controller activates the first PPG sensor. The physiological monitoring apparatus obtains a blood oxygen level of the subject according to the first PPG signal.

A system and methods for a hands-free transmission, reception, and processing of a heartbeat audio is disclosed. In one embodiment, the system includes a heart monitor having a microphone, configured to pick-up a heartbeat sound wave and convert the heartbeat sound wave to a heartbeat audio, and a transmitter, configured to transmit the heartbeat audio; headphones having a receiver, configured to receive the heartbeat audio from the transmitter, and a speaker configured to play the heartbeat audio for a user; and a software application configured to (i) generate an overlaid audio by overlaying a secondary audio file onto the heartbeat audio, (ii) transmit the overlaid audio to the receiver, and (iii) play the overlaid audio using the speaker while the user is meditating, relaxing, or working out.

A method of generating a model for generating a synthetic electrocardiography (ECG) signal comprises: receiving subject-specific training data for machine learning, said training data comprising a photoplethysmography (PPG) signal acquired from the subject and an ECG signal acquired from the subject, wherein the ECG signal provides a ground truth of the subject for associating the ECG signal with the PPG signal; using associated pairs of a time-series of the PPG signal and a corresponding time-series of the ECG signal as input to a deep neural network, DNN; and determining, through the DNN, a subject-specific model relating the PPG signal of the subject to the ECG signal of the subject for converting the PPG signal to a synthetic ECG signal using the subject-specific model.

An advice target location at which a user had a predetermined emotion, for example, is determined based on location information, user biological information, and user transportation means information, which have been acquired by a terminal device (20) being used by the user. Advice information containing information indicating an advice presentation region set by a server device (50) is generated based on the advice target location. This advice information is supplied from the server device (50) to the terminal device (20), so that the terminal device (20) presents advice. With this, advice as to locations pedestrians find dangerous can be presented to drivers, and advice as to locations drivers find dangerous can be presented to pedestrians. Accordingly, accidents and the like can be prevented.

A method for monitoring a health parameter in a person is disclosed. The method involves transmitting radio waves below the skin surface of a person and across a range of stepped frequencies, receiving radio waves on a two-dimensional array of receive antennas, the received radio waves including a reflected portion of the transmitted radio waves across the range of stepped frequencies, generating data that corresponds to the received radio waves, wherein the data includes amplitude and phase data, deriving data from at least one of the amplitude and phase data, and determining a value that is indicative of a health parameter in the person in response to the derived data.

A PPG PRV device for generating a PRV parameter of a PPG signal (20) as an estimation of a HRV parameter of an ECG signal. The PPG PRV device employs a PPG probe (700) and a PPG PRV controller (710). In operation, the PPG probe (700) generate a PPG signal (20). In response thereto, the PPG PRV controller (710) generates a normalized PPG signal (20′) including a plurality of pulses of the PPG signal (20) designated as normal pulses by the PPG PRV controller (710) and excluding at least one pulse of the PPG signal (20) designated at least one abnormal pulse by the PPG PRV controller (710), wherein the normalized PPG signal (20′) is HRV comparable to the ECG signal. The PPG PRV controller (710) derives the PRV parameter from a HRV measurement of the normalized PPG signal (20′).

This application discloses a method for controlling an electronic device to perform photoplethysmography detection. The method includes obtaining historical detection data of a photoplethysmography sensor, calculating a confidence of the historical detection data, determining, based on the calculated confidence of the historical detection data, whether a light source to be turned on by the photoplethysmography sensor in a next detection time segment includes a light source different from a light source turned on when the historical detection data is obtained, and determining that a photoelectric sensing element to be turned on in the next detection time segment is a photoelectric sensing element corresponding to the determined light source to be turned on.