A bio-information measuring apparatus bio-information measuring method are provided. The bio-information measuring apparatus includes: a pulse wave obtainer configured to obtain a pulse wave signal, and a processor configured to correct a feature of the obtained pulse wave signal based on a variation in an amplitude of the obtained pulse wave signal, and to measure bio-information based on the corrected feature.

Systems and methods are presented for monitoring brain pulsatility. A change in volume of the brain is estimated based at least in part on an output of a non-contact, surface measuring sensor (e.g., a distance sensor or a camera). A metric indicative of brain pulsatility is then calculated based at least in part on a ratio of the estimated change in volume of the brain relative to a change in arterial blood pressure.

Methods and systems are provided for a light-emitting diode (LED) drive circuit of an optical probe. As an example, a method for an optical probe including an LED in an LED drive circuit comprises reducing power consumption of the LED drive circuit by adjusting a drive voltage of the LED drive circuit based on one or more LED drive circuit characteristics and one or more LED drive circuit operating parameters. In this way, the LED drive circuit may be efficiently operated.

A system for in-vivo monitoring of intracranial pressure is provided. The system includes a probe and a controller. The probe includes optical emitters and optical detectors. The optical detectors detect light emitted by the optical emitters generate signals representative of the detected light. The controller includes memory and processor. The controller connects to the probe to energize the optical emitters and receiving signals from the optical detectors. The system may include modelling, extraction, and pressure prediction modules. The modelling module can relate intracranial pressure to features of an optical signal representative of a degree to which light input into a subject's skull is absorbed by the subject's brain. The extraction module can extract signal features from a signal derived from the optical signals output by the detectors. The pressure prediction module can input the signal features into the modelling module and output an indication of intracranial pressure.

The present invention relates to a system for monitoring a physical movements of a user, the system comprising: a detection unit configured for receiving a signal obtained by a sensor device representative of a sensed bodily activity, calculating a load index value and communicate, e.g. to the user wearing the sensor device, the value of the load index value. The invention also relates to a method for monitoring and evaluating physical movement of a user and providing feedback to the user.

In various examples, a cognitive load of a user may be inferred. Motion sensor data indicative of head movement of the user may be generated with a motion sensor disposed adjacent a head of the user. The motion sensor data may be analyzed to infer a feature of a gait of the user. The user's cognitive load may be inferred based on the feature of the gait.

A source stochastic signal is deconstructed into its intrinsic components using a decomposition process. The intrinsic components are transformed, and a set of machine learning models are defined and trained to operate with individual ones of the transformed components. The source stochastic signal is thus empirically broken down into underlying components which are then used as learning datasets for the set of machine learning models to predict target components. The target components are then individually predicted and combined to reconstruct a predicted target stochastic signal. The source stochastic signal and the target stochastic signals can be biological signals having a related or common origin, such as photoplethysmogram signals and arterial blood pressure waveforms.

A system (101) for monitoring a physiological condition of a user (104) is disclosed herein. The system (101) includes a receiving module (110) configured to receive a plurality of short-term segments of Heart Rate Variability (HMI) (302) or short-term electrocardiogram (ECG) segments (402) or short voice recordings (602) from the user (104) recorded at different time points. The system includes a stitching module (114) for stitching the plurality of short-term segments and creating a stitched segment. The system further includes an extracting module (116) extracting feature from the stitched segment and a predicting module (118) for predict the physiological condition, based on the feature.

In some embodiments, an electric field generator generates an electric field at a nominal frequency. A detector measures, at multiple time points during a measuring period, one or more properties of the generated electric field. In various embodiments, the one or more properties of the electric field change over time due to interactions with a human body in a reactive near-field region of the electric field. From the measured one or more properties, a computation unit determines one or more periodic behaviors (such as a respiration or heartbeat) and one or more non-periodic behaviors (such as movement of a limb). The computation unit also computes, from at least one of the periodic and non-periodic behaviors, one or more physiological parameters of the human body. From the one or more physiological parameters, the computation unit detects one or more symptoms of a condition of the human body.

A stress analysis apparatus includes a stress level estimation unit for estimating a stress level of a user from biological information of the user, a stress state specification unit for specifying a time period during which the user is in a preset stress state, based on a time-series change of the estimated stress level, and a stress cause association unit for associating the specified time period with information regarding the user.