An apparatus for estimating blood glucose using a photoplethysmography (PPG) signal is provided. The apparatus for estimating blood glucose includes: a pulse wave sensor configured to obtain a pulse wave signal from an object; and a processor configured to obtain at least two points from a waveform of the pulse wave signal, to extract a feature based on time values of the obtained at least two points, and to estimate blood glucose based on the extracted feature.

A device for liveness detection is disclosed. The liveness detecting device has a simplest structure that principally comprises a light sensing unit and a signal processing module. Particularly, the signal processing module is configured for having a physiological feature extracting unit and a liveness detecting unit therein. The physiological feature extracting unit is adopted for extracting a first physiological feature from a PPG signal, or extracting a second physiological feature from the PPG signal that has been applied with a signal process. As such, through the first and second physiological features, the liveness detecting unit is able to determine whether a subject is a living body or not. The liveness detecting device does not use any camera unit and iPPG technology, such that the liveness detecting device has advantages of simple structure, low cost and immediately completing liveness detection.

A method for measuring respiratory parameters of a subject using a range imaging sensor, wherein the method includes: receiving from the range imaging sensor at least one raw image of at least one portion of the torso of the subject, wherein each point of the raw image represents the distance between the range imaging sensor and the subject; generating a surface image of at least one portion of a surface of the torso of the subject by surface interpolation of the raw image; estimating a respiratory signal as a function of time calculated as the spatial average, in a given region of interest (ROI) defined on the torso of the subject, of the differences between the depth values of the surface image at a given time and the depth values of a reference surface image; and estimating a lung volume.

The present disclosure relates to a method for continuous and noninvasive estimation of mixed venous blood saturation [SvO2] in a mechanically ventilated subject (3). The method comprises the steps of measuring (S1; S10) an expiratory carbon dioxide [CO2] content in expiration gas exhaled by the subject, measuring (S2; S20) an expiratory flow or volume of expiration gas exhaled by the subject, estimating (S3; S30) a cardiac output [CO] or an effective pulmonary blood flow [EPBF] of the subject from the measured expiratory CO2 content and the measured expiratory flow or volume using a capnodynamic Fick method, and estimating (S4; S40) SvO2 based on the estimated CO or the EPBF of the subject.

In an embodiment, a method includes: generating a displacement signal indicative of a distension of a surface of a skin; determining a temperature of the skin using a temperature sensor; during a calibration time interval, collecting a plurality of distension values from the displacement signal, the plurality of distension values associated with a respective plurality of temperature values determined using the temperature sensor, the plurality of temperature values being indicative of a temperature change of the skin; determining compensation coefficients associated with the plurality of temperature values; and after the calibration time interval, collecting a first distension value from the displacement signal, determining a first temperature value using the temperature sensor, and determining a blood pressure based on the first distension value, the first temperature value, and the determined compensation coefficients.

A vein simulator system can be used by clinicians to improve their proficiency in placing catheters such as PIVCs or in otherwise accessing a vasculature. A vein simulator system can include a simulated portion of a body, such as a simulated human arm, that includes at least one simulated vein. The vein simulator system can also include a control system, one or more sensors and one or more feedback components. The control system can leverage the one or more sensors to generate feedback during a clinician's attempt to place a catheter and can output the feedback via the feedback components, either during or after the attempt.

An electrode support includes a first reference electrode and a second electrode, the electrodes being electrically insulated from each other and able to measure two electric potentials at the surface of a haired animal body, the electrode support further including an electronic module including at least one memory, a calculator and a first electric interface to receive electric signals acquired from each electrode for recording a cardiac activity of the haired animal, the electrodes each including a one-piece structure formed of a polymer material in which conductive elements are distributed, the structure including a base and a plurality of projections able to go through a coat.

A muscle mass estimation method including acquiring a height of a living organism, acquiring an electrical resistance value measured for the living organism, and computing a muscle mass of the living organism using a calculation formula including a first variable including the height of the living organism and the electrical resistance value, and a second variable including the electrical resistance value.

Indirect, oscillometric, digital blood pressure monitoring systems and methods enabling self-calibration to obtain absolute blood pressure values using algorithmic analysis of arterial pressure pulses to establish an oscillometric profile and compensate for intervening effects on digital arterial pressure. Proper algorithmic analysis is dependent upon proper positioning and maintained engagement of a digital cuff on the digit of a user and subsequent hydraulic coupling of the cuff to the arteries within the digit.

Disclosed are a method and system for predicting a health risk. In an embodiment, a method of predicting a health risk may include collecting a health condition index, generating time-series data by accumulating the health condition index at given time intervals, calculating a health condition index prediction value in a future time by inputting the generated time-series data to a health condition index prediction model, comparing the calculated health condition index prediction value with a preset threshold, and generating a danger alert signal when the calculated health condition index prediction value is out of the threshold.