A blood pressure prediction method includes performing data fragment division on the pulse wave data; generating an input data tensor according to a total fragment number and the pulse wave data ; performing multilayer convolution and pooling calculation on the input data tensor by using a blood pressure CNN to generate a feature data tensor; generating an input data matrix according to the feature data tensor; performing feature data regression calculation on the input data matrix by using a blood pressure ANN to generate a blood pressure regression data matrix; when a prediction mode identifier is a mean prediction identifier, performing mean diastolic pressure data calculation and mean systolic pressure data calculation to generate mean diastolic pressure data and mean systolic pressure data; or, when the prediction mode identifier is a dynamic prediction identifier, extracting diastolic and systolic pressure data to generate a predicted blood pressure data sequence.

In accordance with one embodiment, a method for determining changes in health of a user is disclosed. The method includes sensing multi-dimensional motion of a body part of a user to generate a first multi-dimensional motion signal at a first time and date; in response to the first multi-dimensional motion signal, generating a first neuro-mechanical fingerprint; generating a first health measure in response to the first NFP and user calibration parameters; sensing multi-dimensional motion of the body part of the user to generate another multi-dimensional motion signal at another time and date; in response to the another multi-dimensional motion signal, generating another neuro-mechanical fingerprint; generating another health measure in response to the another NFP and the user calibration parameters; and comparing the first health measure with the another health measure to determine a difference representing the health degradation of the user.

A medical device is configured to sense an electrical signal and determine that signal to noise criteria are met based on electrical signal segments stored in response to sensed electrophysiological events. The medical device is configured to determine an increased gain signal segment from one of the stored electrical signal segments in response to determining that the signal to noise criteria are met. The medical device determines a noise metric from the increased gain signal segment. The stored electrical signal segment associated with the increased gain signal segment may be classified as a noise segment in response to the noise metric meeting noise detection criteria.

A stress tolerable amount calculation apparatus includes: a stress calculating unit configured to calculate a stress value that indicates stress felt by a subject: and a stress tolerable amount calculating unit configured to calculate a stress tolerable amount that indicates the amount of stress that the subject can tolerate, based on the calculated stress value.

Apparatus, system, and computer readable media determine the probability of visual recognition of an image by a subject using electroencephalography (EEG) corresponding to a Visual Evoked Potential (VEP). The apparatus comprises means for presenting a series of visual stimuli corresponding to said image to evoke EEG signals. The visual stimuli of the image are presented in an orientation sequence based on a timing cycle. At least one prism is provided for placement in front of the series of visual stimuli corresponding to said image. The EEG signals evoked in response to said visual stimuli and said prism placed in front of said visual stimuli are recorded and processed by a processor. VEP is generated corresponding to the presence of a shift and thereby provides object statistic reliability of the visual recognition of the image by the subject.

In one example, a display unit comprises a display panel that is configured to display digital images. The display unit further comprises an at least partially transparent protective layer that is arranged above the display panel. The display unit further comprises a controller that is communicatively attached onto an upper surface of the display panel. A biometric sensor pattern is integrated in the controller, and the controller is configured to control the biometric sensor pattern.

The invention provides a system and method for determining a respiratory effort for a subject. The method comprises obtaining a relaxed signal representing the subject breathing in a relaxed manner and a forced signal representing the subject breathing in a forced manner. A plurality of forced peaks is derived from the forced signal and candidate peaks are selected from the plurality of forced peaks. The candidate peaks are selected based on features of the forced peaks. A user selects a user identified peak from the candidate peaks and thus, a respiratory effort is determined based on the relaxed signal and the user identified peak.

Provided is a computer-implemented method of determining exercise parameters, such as heart rate thresholds, endurance, maximum heart rate and lactate threshold. The method comprises fitting a continuous curve to heart rate data obtained over time from the onset of exercise, the curve comprising a plurality of components that meet at transition points that join the components. The exercise parameters are obtained from the curve fitting of the heart rate data. Also provided is a system for determining exercise parameters, in particular a computer-implemented system that determines exercise parameters using the disclosed method.

An apparatus for estimating blood pressure includes: a sensor, and a processor configured to estimate blood pressure based on a PPG signal and a contact pressure signal measured by the sensor. The sensor includes: a transparent elastic body; a first polarizing film provided on a surface of the transparent elastic body and configured to come into contact with the object; a light source provided under the transparent elastic body and configured to emit light toward the object; a first detector and a second detector provided under the transparent elastic body and configured to detect light, passing through the first polarizing film after being emitted by the light source and scattered or reflected from the object, to measure the PPG signal; and a second detector provided under the transparent elastic body and configured to detect light, not passing through the first polarizing film, to measure the contact pressure signal.

An apnea analysis system may include a photoplethysmographic (PPG) sub-system, a breath detection sub-system, and an apnea analysis module. An apnea analysis system includes a photoplethysmographic (PPG) sub-system, a breath detection sub-system, and an apnea analysis module. The PPG sub-system is configured to be operatively connected to an individual and output a PPG signal from the individual. The breath detection sub-system is configured to be operatively connected to the individual and output a breath signal from the individual. The apnea analysis module is in communication with the PPG sub-system and the breath detection sub-system. The apnea analysis module analyzes the breath signal and a respiratory component of the PPG signal and, based on the analysis, identifies a presence of apnea, differentiates between obstructive apnea and central apnea, and provides an indication of the identified apnea.