Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to an Analytics Engine that receives one more signal files that include neural signal data of a user based on voltages detected by one or more electrodes on a set of headphones worn by a user. The Analytics Engine preprocesses the data, extracts features from the received data, and feeds the extracted features into one or more machine learning models to generate determined output that corresponds to at least one of a current mental state of the user and a type of facial gesture performed by the user. The Analytics Engine sends the determined output to a computing device to perform an action based on the determined output.

A diagnosis device, and more particularly, a compartment syndrome diagnostic device is described herein. The diagnostic device may include a display that renders information associated with stimulation of a motor unit suspected of suffering from compartment syndrome. The diagnostic device may generate a stimulation signal for stimulating the motor unit through an electrode. The device may determine whether the motor unit is at risk for compartment syndrome based on the response of the motor unit to the stimulation. The diagnostic device may also measure pressure of a compartment. The device may determine whether the motor unit is at risk for compartment syndrome based on the measured pressure and the response of the motor unit to the stimulation.

The invention relates to a cardiac device comprising an accelerometer (4) and a filter (6) arranged to eliminate noise from an SCG signal from the accelerometer (4), as well as a computer (8) arranged to apply a thoraco-cardiovascular model to the signal from the filter (6) and to obtain at least one cardiac activity indicator (CI) from same.

Systems and methods are disclosed for assessing tissue function based on vascular disease. One method includes receiving a patient-specific anatomic model generated from patient-specific imaging of at least a portion of a patient's tissue; receiving a patient-specific vascular model generated from patient-specific imaging of at least a portion of a patient's vasculature; receiving an estimate of blood supplied to a portion of the patient-specific anatomic model; and determining a characteristic of the function of the patient's tissue using the estimate of blood supplied to the portion of the patient-specific anatomic model.

To identify physiological states that are predictive of a person's performance, a system provides physiological and behavioral interfaces and a data processing pipeline. Physiological sensors generate physiological data about the person while performing a task. The behavioral interface generates performance data about the person while performing the task. The pipeline collects the physiological and performance data along with reference data from a population of people performing the same or similar tasks. In various implementations, the physiological states are brain states. In one implementation, the pipeline computes bandpower ratios. In another implementation, the pipeline decomposes the physiological data into frequency-banded components, identifies brain states derived from the decomposed data—for example, clusters of correlations of decomposed data envelopes—grades the performance data, compares the graded performance data to the brain states, and identifies statistical relationships between the brain states and levels of performance.

Provided is a biological examination apparatus and a biological information analysis method capable of quickly grasping a two-dimensional motions of the up-down and front-back directions of the thyroid cartilage and the lingual bone accompanied by a swallowing sound as swallowing dynamics by a non-invasive examination. In the biological examination apparatus of the present invention, an up-down motion component associated with an up-down motion of the thyroid cartilage and a front-back motion component associated with a front-back motion of the thyroid cartilage are extracted from a fitting result obtained by fitting a model function modeling a swallowing motion to distance information based on detection data detected by a larynx portion displacement detector, and a two-dimensional trajectory data indicating behavior trajectories of an up-down direction and a front-back direction of the thyroid cartilage is generated based on the extracted up-down motion component and the extracted front-back motion component.

The disclosed system for interacting with objects in an extended reality (XR) environment generated by an XR system may include (1) neuromuscular sensors configured to sense neuromuscular signals from a wrist of a user and (2) at least one computer processor programmed to (a) determine, based at least in part on the sensed neuromuscular signals, information relating to an interaction of the user with an object in the XR environment and (b) instruct the XR system to, based on the determined information relating to the interaction of the user with the object, augment the interaction of the user with the object in the XR environment. Other embodiments of this aspect include corresponding apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

A muscle anti-clenching training device comprising a flexible device body conformable in a range of configurations upon a user’s body, and including: a skin-contact side and a top cover side; electrical contacts mounted in the flexible device body and exposed upon the skin-contact side; an adhesive upon the skin-contact side; an alert device mounted in the flexible device body; sensing circuitry electrically connected to the electrical contacts and structured to monitor an electrical property in a user’s body; and an electronic controller coupled to the sensing circuitry and structured to activate the alert device to produce an alert signal where the monitored electrical property is indicative of a muscular contraction.

A system comprising a plurality of neuromuscular sensors, each of which is configured to record a time-series of neuromuscular signals from a surface of a user's body; and at least one computer hardware processor programmed to perform: applying a source separation technique to the time series of neuromuscular signals recorded by the plurality of neuromuscular sensors to obtain a plurality of neuromuscular source signals and corresponding mixing information; providing features, obtained from the plurality of neuromuscular source signals and/or the corresponding mixing information, as input to a trained statistical classifier and obtaining corresponding output; and identifying, based on the output of the trained statistical classifier, and for each of one or more of the plurality of neuromuscular source signals, an associated set of one or more biological structures.

A stretch-measurement probe includes an elongate outer sleeve, expansion feature associated with a distal portion of the outer sleeve, and an elongate inner rod disposed at least partially within the outer sleeve. The expansion feature is configured to allow a longitudinal distance between a proximal end of the outer sleeve and the distal end of the outer sleeve to be varied.