A computer implemented method for determining accuracy of heart rate variability is proposed. The method comprises the following steps: a) providing at least one photoplethysmogram obtained by at least one portable photoplethysmogram device (110); b) Determining at least one signal feature by evaluating the photoplethysmogram; c) Determining the accuracy of heart rate variability by using at least one trained model, wherein the signal features determined in step b) are used as input for the trained model.

A blood oxygen saturation measurement method includes: acquiring a pressure measurement signal between a blood oxygen saturation measurement apparatus and an object; in response to determining that the pressure measurement value is within a preset range, and acquiring a blood oxygen saturation measurement signal of the object; and according to the blood oxygen saturation measurement signal of the object, obtaining a blood oxygen saturation measurement value of the object.

In general, regardless of using an optical method or an electrical AC resistance impedance method, non-invasive blood glucose measurement has a problem in that variations in the level of glucose contained in blood are too small to be measured as a signal, and thus measurement results are inaccurate due to noises generated during measurement and errors caused by the difficulty in consistent measurement. To solve this problem, the present invention provides a non-invasive blood glucose sensor includes: a measurement-unit body; an impedance electrode sensor provided on an inner bottom surface of the measurement-unit body; a signal-generation and measurement unit configured to measure impedance while scanning frequency by supplying multiple frequencies to the impedance electrode sensor; a blood sensor configured to measure the amount of blood flowing through a body part brought into contact with the impedance electrode sensor; and a status display LED configured to display different colors according to the amount of blood measured using the blood sensor. Owing to this configuration, the present invention has an effect of measuring an accurate blood glucose level in a non-invasive manner.

A signal measurement method and a signal measurement apparatus (1000) are provided. A mode selection switch (11) may be used to enable a multi-lead measurement mode, to obtain signals of a plurality of leads and a status of a user. When quality of the signals of the plurality of leads is good or the user is in a static state, extracted features of the signals of the plurality of leads are output. When the signals of the plurality of leads are poor and the user is in a moving state, the mode selection switch (11) is used to switch to a single-lead mode with right leg drive, to obtain a signal of a single lead. A common-mode signal is eliminated from the signal of the single lead by using a negative feedback of a right leg drive electrode, and a feature of the signal of the single lead is output.

A wearable device is provided. The wearable device includes a first sensor having a light-emitting part and a light-receiving part, a second sensor having at least one electrode, and at least one processor electrically connected to the first sensor and the second sensor, wherein the at least one processor acquires PPG signal data by using the first sensor for a first time while the wearable device is worn on a user's body, acquires ECG signal data by using the second sensor for the first time for which the PPG signal is acquired, determines an inter-beat interval calculation model, based on the result of a comparison between the PPG signal data and the ECG signal data, and acquires, based on the determined inter-beat interval calculation model, an inter-beat interval of the user from PPG signal data measured for a second time after the first time.

Systems and methods for cardiac pacing during a procedure are disclosed and may include an external pulse generator (EPG) for connecting to a lead. A remote-control module (RCM) wirelessly connected to the EPG may include user inputs to control the EPG. A central processing unit (CPU) with a memory unit for storing code and a processor for executing the code may be included where the CPU is connected to the EPG and RCM. The code may control the EPG in response to user input from the RCM. The CPU may be disposed in the EPG or the RCM, or an interface module (IM) configured to communicate between an otherwise conventional EPG and the RCM. The executable code may perform a continuity test (CT) routine, a capture check (CC) routine, rapid pacing (RP) routine, and/or a back-up pacing (BP) routine, in response to user input from the RCM.

In some examples, a method includes receiving a signal indicative of a blood pressure of a patient and identifying at least one first portion of the signal comprising a first characteristic of the signal exceeding a first threshold. The method also includes identifying at least one first portion of the signal comprising a second characteristic of the signal exceeding a second threshold, the first characteristic being different than the second characteristic. The method further includes determining a filtered signal indicative of the blood pressure of the patient by excluding the at least one first portion and the at least one second portion from the signal. The method includes determining a set of mean arterial pressure values based on the filtered signal and determining an autoregulation status of the patient based on the set of mean arterial pressure values.

An electronic device includes a first outputter and a processor. The processor is configured to acquire biometric information that is information about a living body, calculate a reliability of the biometric information based on the acquired biometric information, and output the biometric information to the first outputter in an output mode associated with the calculated reliability.

The present disclosure generally relates to user interfaces for health applications. In some embodiments, exemplary user interfaces for managing health and safety features on an electronic device are described. In some embodiments, exemplary user interfaces for managing the setup of a health feature on an electronic device are described. In some embodiments, exemplary user interfaces for managing background health measurements on an electronic device are described. In some embodiments, exemplary user interfaces for managing a biometric measurement taken using an electronic device are described. In some embodiments, exemplary user interfaces for providing results for captured health information on an electronic device are described. In some embodiments, exemplary user interfaces for managing background health measurements on an electronic device are described.

This relates to the use of sensor evaluation in a multi-sensor environment. In a first aspect, this specification describes apparatus comprising: at least one processor; and at least one memory including computer program code. The at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to perform: receive sensor data from a plurality of sensors collected during a first time period; process the received sensor data through a plurality of layers of a neural network to generate an output indicative of the sensing quality of each of the plurality of sensors for a task; and cause a subset of the plurality of sensors to collect data during a second time period based on the output indicative of the suitability of each of the plurality of sensors for the task.