Devices, systems and methods for microelectronic animal identification are disclosed. The microelectronic animal identification device includes an inserter configured to releasably holding the microelectronic chip at a distal end of the inserter, and an actuator configured to release the microelectronic chip from the inserter when the distal end of the inserter is inserted into a substrate of an animal body part. The microelectronic animal identification system includes a microelectronic chip and delivery system, a pigment marking device, a media transfer assembly for producing a pigment mark, a tissue sample device, and a data management system providing traceability of the animal and the materials used.

A method is implemented by a computing device for helping a particular user use a user interface (UI). Electroencephalography (EEG) data is obtained that indicates brain activity of a particular user during a period in which that user views the UI and/or interprets help information that describes how to use the UI. Based on the EEG data, the computing device selects, from among multiple predefined cognitive states, the one or more cognitive states that characterize the particular user during the period. The computing device assists the particular user to use the UI by customizing the help information for the particular user based on the one or more selected cognitive states. A complementary computing device and computer program product are also disclosed.

Configurations are disclosed for a health system to be used in various healthcare applications, e.g., for patient diagnostics, monitoring, and/or therapy. The health system may comprise a light generation module to transmit light or an image to a user, one or more sensors to detect a physiological parameter of the user's body, including their eyes, and processing circuitry to analyze an input received in response to the presented images to determine one or more health conditions or defects.

Two biomarkers are provided for obstructive apnea. A first biomarker determines amplitude and timing of inspiratory efforts from a bioelectric signal. The respiratory rate is compared with a normal pre-detection rate, and the amplitude of the effort is compared with a normal amplitude. The obstructive apnea is likely present if a series of inspiratory efforts are above a normal amplitude and with increasing amplitude, but at a normal rate. A second biomarker determines heart rate and respiratory rate. A normal lower threshold for heartbeat interval is established, and if subthreshold events occur (short RR intervals), a commencement time for each sequence of subthreshold events is compared for a respiratory rate-normalized window. If the number of subthreshold events exceeds a minimum for the window, obstructive apnea is likely present.

Applications for a Composite Neuro-physiological State (CNS) value include rating using the value as in indicator of participant emotional state in computer games and other social interaction applications. The CNS is computed based on biometric sensor data processed to express player engagement with content, game play, and other participants along multiple dimensions such as valence, arousal, and dominance. An apparatus is configured to perform the method using hardware, firmware, and/or software.

A method for forecasting an environmental event/a type of environmental event includes acquiring at least one test data set of at least one behavioural and/or physiological parameter of a population of animals; generating a test profile based on said at least one test data set, representing behaviour and/or physiological status of the population of animals; calculating a ratio between the test profile and a first reference profile; and setting an alert, if said ratio reaches a predefined threshold value.

System and methods for stimulating the neurovascular system of the cerebral tissue through optimally placed devices, while simultaneously measuring the evoked neuronal and hemodynamic responses, also using optimally placed devices, is disclosed. Systems and methods for iteratively stimulating the neurovascular system and recording neuronal and hemodynamic responses are also disclosed. Further, a method for determining cerebral neurovascular functioning from the combined stimulation and measurement is disclosed, for use in diagnosis of neurovascular disorders.

An apparatus for applying Transcranial Magnetic Stimulation (TMS) to the brain of an individual comprising: a head mount; a plurality of magnet assemblies for releasable mounting on the head mount; each of the magnet assemblies comprises a permanent magnet, and at least one of (i) a movement mechanism for moving the permanent magnet and/or (ii) a magnetic shield shutter mechanism, to provide a rapidly changing magnetic field; and a computerized controller for operating at least one of the movement mechanism and the magnetic shield shutter mechanism of at least one of the magnet assemblies to apply a rapidly changing magnetic field to the brain of the individual, wherein the computerized controller is configured to provide one of intermittent oscillatory stimulation and continuous oscillatory stimulation, to induce weak electric currents in the brain of an individual so as to modify the natural electrical activity of the brain of the individual.

Approaches to determining a sleep fitness score for a user are provided, such as may be based upon monitored breathing disturbances of a user. The system receives user state data generated over a time period by a combination of sensors provided via a wearable tracker associated with the user. A system can use this information to calculate a sleep fitness score, breathing disturbance score, or other such value. The system can classify every minute within the time period as either normal or atypical, for example, and may provide such information for presentation to the user.

There is provided a system and method for interfacing with biological tissue. The system includes: a feature extraction module to implement an extraction approach to extract one or more features from the one or more physiological recording signals; a machine learning module to apply a machine learning model based on input data to detect a physiological event or condition for classification, the input data including the extracted features, the machine learning model trained using a training set including feature vectors of time-series data labelled with known occurrences of the physiological event or condition; and an output module to output the classification of the machine learning module.