A bio-signal quality assessment apparatus may include: a bio-signal obtainer configured to obtain a bio-signal; and a processor configured to extract periodic signals from the obtained bio-signal, and determine a signal quality index based on at least one of similarity between the extracted periodic signals and signal variability of the obtained bio-signal.

A blood pressure measurement method and apparatus, and a wearable device are provided. The blood pressure measurement method includes: detecting a change of a user from a first state to a second state; starting measurement and inflating an airbag (160) to a preset pressure value at a preset rate if determining that the user has changed from the first state to the second state; obtaining first duration of an inflation process; determining a correction value based on the first duration; and after the measurement ends, obtaining a measurement value of a blood pressure, and correcting the measurement value based on the correction value, to obtain a final blood pressure value of the user. The blood pressure measurement method provides an early-morning blood pressure measurement manner in which the early-morning blood pressure can be automatically and accurately measured when the user is insensitive, thereby simplifying user’s operations, and improving user experience.

Disclosed herein are in vivo non-invasive methods and devices for the measurement of the hemodynamic parameters, such as blood pressure, cardiac output, stroke volume and vascular tone, of a subject, and the mechanical anelastic in vivo properties of the subject's arterial blood vessels. An exemplary method requires obtaining the peripheral pulse volume waveform (PVW), the peripheral pulse pressure waveform (PPW), and the peripheral pulse velocity waveform (PUW) from the same artery; calculating the time phase shift between the PPW and PVW, and the plot of pulse pressure versus pulse volume; and determining the blood pressures and power law components of the anelastic model from the waveforms PPW and PVW, the cardiac output from the waveforms PPW and PUW, and the quality factor of the artery based upon the calculations. The disclosed methods and devices can be used to diagnose and treat cardiovascular disease in a subject in need thereof.

A system includes a sensor configured to generate data associated with movements of a resident for a period of time, a memory storing machine-readable instructions, and a control system arranged to provide control signals to one or more electronic devices. The control system also includes one or more processors configured to execute the machine-readable instructions to analyze the generated data associated with the movement of the resident, determine, based at least in part on the analysis, a likelihood for a fall event to occur for the resident within a predetermined amount of time, and responsive to the determination of the likelihood for the fall event satisfying a threshold, cause an operation of the one or more electronic devices to be modified.

The present disclosure discloses a PPG sensor, an electronic device and a wearable device. The PPG sensor includes a first light-emitting assembly configured to emit a first optical signal; a second light-emitting assembly configured to emit a second optical signal; and a plurality of photoelectric sensors configured to receive the first optical signal and the second optical signal. A distance between the first light-emitting assembly and at least one of the plurality of photoelectric sensors is greater than a minimum one of the distances between the second light-emitting assembly and each of the plurality of photoelectric sensors.

An apparatus for providing information about a user's brain resources is provided. The apparatus includes at least sensor interface circuitry and processing circuitry coupled to the sensor interface circuitry. In a calibration mode, the sensor interface circuitry is configured to receive first sensor data from an electroencephalography sensor. The first sensor data are indicative of an electroencephalogram of the user. Further, the sensor interface circuitry is configured to receive second sensor data from a physiological sensor in the calibration mode. The second sensor data are indicative of a physiological property of the user. In the calibration mode, the processing circuitry is configured to train a brain-physiological model for the user based on the first sensor data and the second sensor data.

The present technology relates to the field of medical monitoring. Patient monitoring systems and associated devices, methods, and computer readable media are described. In some embodiments, a patient monitoring system includes one or more sensors configured to capture first data related to a patient and a monitoring device configured to receive the first data. In these and other embodiments, the patient monitoring system can include an image capture device configured to capture second data related to the patient. In these and still other embodiments, the one or more sensors can be configured to instruct the patient monitoring system to display the second data.

A wearable device is provided. The wearable device includes a first sensor having a plurality of electrodes, and at least one processor electrically connected to the first sensor. The at least one processor may obtain a first electrocardiogram signal by using a first sensor in a state where the wearable device is worn on a user's body, obtain an electromyogram signal from the first electrocardiogram signal, obtain a second electrocardiogram signal by filtering the electromyogram signal from the first electrocardiogram signal, determine the wearing state of the wearable device on the basis of the intensity of the electromyogram signal and the quality of the second electrocardiogram signal, and output a guide on the wearing state on the basis of a determination result.

Methods and systems implement a variety of sensors, including in embodiments various combinations of EEG sensors, biochemical sensors, photoplethysmography (PPG) sensors, microphones, and accelerometers, to detect, predict, and/or classify various physiological events and/or conditions related to epilepsy, sleep apnea, and/or vestibular disorders. The events can include neuroelectrical events, cardiac events, and/or pulmonary events, among others. In some cases, the method and systems implement trained artificial intelligence (AI) models to detect, classify, and/or predict. The methods and systems are also capable of optimizing a treatment window, suggesting treatments that may improve the overall well-being of the patient (including improving pre- or post-event symptoms and effects), and/or interacting with various care providers.

A cardiopulmonary resuscitation (CPR) cerebral monitoring device including a measurement probe having one or more optical emitters, and one or more optical detectors, and including an optical instrument having an optical source, and an optical detector. Also included is a controller configured to control the optical instrument to emit multi-spectral light through the one or more optical emitters to illuminate a tissue, control the optical detector to detect multi-spectral light emitted from the illuminated tissue, compare the emitted multi-spectral light to the detected multi-spectral light, compute a plurality of cerebral tissue parameters based on the comparison, determine CPR procedures based on the plurality of cerebral tissue parameters, and control a user output device to instruct a user to perform the CPR procedures, and/or control an automated CPR device to perform the CPR procedures.