A method and apparatus for analysing a signal is disclosed. The method for analysing the signal includes collecting signals needed to learn signal analysis, clustering features of the signals on a feature space, generating a signal analysis model for each of clusters formed by the clustering, and integrating outputs of the signal analysis models generated for the clusters. According to the present disclosure, it is possible to robustly diagnose a testee based on biomedical signal analysis that uses an artificial intelligence (AI) model through a 5G network.

A body characteristic measurement method causing a computer to execute a process, the process includes: determining a gait of a quadruped based on accelerations, acquired from a multi-axis acceleration sensor attached to the chest of the quadruped, in multiple axial directions; and determining, if the determined gait is gallop or canter, an injury risk of the quadruped based on a waveform of a positive half-wave region of acceleration in a movement direction of the quadruped in the case where an increase in the acceleration in the movement direction of the quadruped is defined as positive.

In one embodiment, a computer-readable non-transitory storage medium embodies software that is operable when executed to, in real time, capture a number of images of a user; identify one or more regions of interest corresponding to one or more superficial arteries of the user. The identification is based on a signal-to-noise ratio of photoplethysmogram (PPG) data obtained from the plurality of images. The software is further operable to measure, based on the PPG data, blood volume pulse (BVP) signals; and based on the measured BVP signals, compute one or more cardiological metrics for the user.

In one embodiment, a computer-readable non-transitory storage medium embodies software that is operable when executed to, in real time, capture a number of images of a user; and determine a time-series signal for the user based on the plurality of images. The signal includes one or more segments that are physiologically plausible and one or more segments that are physiologically implausible. The software is further operable to identify one or more of the physiologically plausible sub-segments based on one or more pre-defined signal characteristics; and calculate one or more heartrate measurements based on the physiologically plausible sub-segments.

A sleeping/waking determining system (100) configured to determine whether a subject on a bed (BD) is in a sleeping state or in a waking state, includes: a load detector (11) configured to detect a load of the subject on the bed; and a determining unit (33) configured to determine whether the subject is in the sleeping state or in the waking state, based on a comparison between a threshold value and a value obtained by performing a time-integration of a standard deviation of a temporal variation of the load of the subject.

A diagnostic Electrochemical Impedance Spectroscopy (EIS) procedure is applied to measure values of impedance-related parameters for one or more sensing electrodes. The parameters may include real impedance, imaginary impedance, impedance magnitude, and/or phase angle. The measured values of the impedance-related parameters are then used in performing sensor diagnostics, calculating a highly-reliable fused sensor glucose value based on signals from a plurality of redundant sensing electrodes, calibrating sensors, detecting interferents within close proximity of one or more sensing electrodes, and testing surface area characteristics of electroplated electrodes. Advantageously, impedance-related parameters can be defined that are substantially glucose-independent over specific ranges of frequencies. An Application Specific Integrated Circuit (ASIC) enables implementation of the EIS-based diagnostics, fusion algorithms, and other processes based on measurement of EIS-based parameters.

An oxygen mask configured for CO2 sampling and oxygen delivery, the oxygen mask having an oxygen inlet, a nasal breath-sampling element and a breath sampling port configured to receive breath samples, sampled by the breath-sampling element, wherein the breath-sampling element is configured to reduce dilution of exhaled breath by the delivered oxygen.

A processor-implemented method comprises obtaining current operational context information associated with a sensing device; obtaining an expected calibration factor parameter model associated with a patient; calculating an expected calibration factor value based on the expected calibration factor parameter model and the current operational context information; obtaining one or more electrical signals from the sensing device, the one or more electrical signals having a signal characteristic indicative of a physiological condition; converting the one or more electrical signals into a calibrated measurement value for the physiological condition using the expected calibration factor value; and outputting the calibrated measurement value for the physiological condition.

A pulmonary health monitoring system aims at assessing pulmonary health of subjects. Conventional techniques used for pulmonary health monitoring are not convenient to the subjects and needs considerable cooperation from the subjects. But, there is a challenge in utilizing the conventional devices to the subjects not capable of providing considerable cooperation. The present disclosure includes a blow device applicable to all kind of subjects and doesn't need cooperation from the subjects. Further, in the present disclosure, the blow device generates a phase shifted signal corresponding to a breathe signal and the phase shifted signal is further processed to extract a set of physiological features. Further, pulmonary health of a subject is analyzed by processing the set of physiological features based on a ridge regression based machine learning technique.

A system or a method for assisting an individual to acclimatize to hot environment prior to being exposed to the hot environment using a measured heart rate with or without a measured skin temperature and/or a measured body core temperature. A Physiological Strain Index (PSI) or adaptive PSI (aPSI) is calculated for the individual to provide a target for the individual's exertion level such that an area under the calculated PSI/aPSI curve is used to determine the amount of heat acclimatization that has occurred for that particular training session and/or prior training sessions.