An apparatus and a method for estimating cardiovascular information. The apparatus for estimating cardiovascular information may include: a memory storing instructions; a processor configured to execute the instructions to: obtain a pulse wave signal of an object; determine an area under the pulse wave signal and above a reference point on the pulse wave signal; and estimate cardiovascular information of the object based on the determined area of the pulse wave signal.

To perform measurement of a vascular elasticity rate with high accuracy in a short time. A vascular elasticity rate evaluation apparatus of the present invention includes: a pressure detection unit that detects a pulse wave with an external pressure being applied to a blood vessel; and a control unit that forms a pulse wave amplitude indicating dependent characteristics due to elasticity of the blood vessel from a detection value of the pressure detection unit, calculates a plus area in an elevation process of the pulse wave amplitude and a minus area in a descent process thereof, and calculates the vascular elasticity rate using values thereof.

There is provided an apparatus for detecting the deteriorating health of a patient receiving gas from a respiratory device. The apparatus comprises a sensor unit configured to monitor a flow rate or pressure of a gas that is flowing in a pipe that connects the respiratory device to a gas delivery device worn by the patient, and a processor configured to measure a respiratory rate of the patient based on variations in the flow rate or pressure of gas in the pipe and to implement a trend analysis of the measured respiratory rate. The processor is configured to generate a warning when it determines that there is an upward trend in the measured respiratory rate and that a magnitude of the trend exceeds a threshold.

Systems and methods for monitoring of biometric and contextual variables to assist in screening for, quantification of, and prediction of smoking behavior, and for assisting in smoking cessation are described.

The present invention relates to methods and devices for measuring the musculature of animal. In some embodiments, the musculature of the animal is measured using by generating traces of the animal using a flexicurve ruler and comparing the traces to a reference and/or previous traces of the animal. The methods can be used to improve an animal's musculature by evaluating the muscle development of the animal and changing the animal's diet or exercise regime based on the results of the evaluation.

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.

Systems and methods for processing neural signals are provided. A neural data analysis system may comprise a feature extraction module configured to extract a plurality of features from neural signal waveforms obtained by an implanted neural interface probe with a plurality of channels or electrodes, and transmit the extracted features as a plurality of discrete outputs. The neural data analysis system may also comprise a feature-event coalescence module configured to receive the plurality of discrete outputs from the feature extraction module, and construct a model-based inference of bioelectric activity based on feature event statistics, prior knowledge of bioelectric signals, and/or a behavioral model of the feature extraction module. The neural data analysis system may further comprise an approximator module configured to receive a plurality of coalesced events from the feature-event coalescence module, and apply a series of transformations to the coalesced event data to generate a higher entropy neural code.

Medical patient monitoring sensor devices including a disposable sensor assembly and a reusable pairing device are disclosed. The disposable sensor assembly can collect patient physiological data and provide power for the reusable pairing device. The reusable pairing device can establish wireless communication with a monitoring device. Once the reusable pairing device receives patient physiological data from the disposable sensor assembly, the reusable pairing device can wirelessly transmit the data to the computing device via the wireless communication.

A personal items network, comprising a plurality of items, each item having a wireless communications port for coupling in network with every other item, each item having a processor for determining if any other item in the network is no longer linked to the item, each item having an indicator for informing a user that an item has left the network, wherein a user may locate lost items. A method for locating lost personal items, comprising: linking at least two personal items together on a network; and depositing one or both of time and location information in an unlost item when one of the items is lost out of network.

A method of processing a signal representing a physiological rhythm of a subject, the method comprising the steps of receiving the signal from the subject, filtering the signal with a band pass filter, extracting an analysis window from the filtered signal, performing a plurality of interval length estimation methods on the filtered signal in the analysis window, summing the outputs of the plurality of interval length estimation methods, and determining an interval length from the sum of the outputs of the plurality of interval length estimation methods.