In one example embodiment of the invention, a simulation based training system is provided having a sensor that unobtrusively collects objective data for individuals and teams experiencing training content to determine the cognitive states of individuals and teams; time-synchronizes the various data streams; automatically determines granular and objective measures for individual cognitive load (CL) of individuals and teams; and automatically determines a cognitive load balance (CLB) and a relative cognitive load (RCL) measure in real or near-real time. Data is unobtrusively gathered through physiological or other activity sensors such as electroencephalogram (EEG) and electrocardiogram (ECG) sensors. Some embodiments are further configured to also include sociometric data in the determining cognitive load. Sociometric data may be obtained through the use of sociometric badges. Some embodiments further automatically customize the simulation content by automatically selecting content based on the CL of the individuals and teams.

Disclosed in the present disclosure is a method, system and apparatus for prediction, estimation and prevention of occurrence of agitation episode in a subject predisposed to agitation. The method comprises receiving, from a first monitoring device attached to a subject, physiological data of sympathetic nervous system activity in the subject and activity data of the subject; receiving, from a computing device, a plurality of indications associated with a plurality of agitation episodes of the subject; analyzing, using at least one machine learning model, the physiological data, the activity data, and the plurality of indications to determine a probability of an occurrence of an agitation episode of the subject; and sending a signal to a second monitoring device to notify the second monitoring device of the probability of the occurrence of the agitation episode of the subject such that treatment can be provided to the subject to decrease sympathetic nervous system activity in the subject.

An electronic vaporizer system includes an anesthetic sump containing anesthetic agent, a vaporizer unit that vaporizes the anesthetic agent from the sump and delivers the vaporized agent to a patient breathing circuit, and a gas sensor configured to measure end tidal concentration of the anesthetic agent and exhalation gasses from the patient. A control system is configured to receive the measured end tidal concentration of anesthetic agent and compare the measured end tidal concentration to a desired end tidal concentration to be maintained for the patient. The vaporizer unit is then automatically controlled to deliver an amount of vaporized agent to the patient based on the comparison.

A method of identifying an epileptogenic zone of a subjects brain includes: receiving a plurality N of physiological brain signals that extend over a duration, each of the plurality N of physiological brain signals acquired from the subjects brain; calculating within a time window a state transition matrix based on at least a portion of each of the plurality N of physiological brain signals, wherein the state transition matrix is a linear time invariant model of a network of N nodes corresponding to the plurality N of physiological brain signals; calculating a minimum norm of a perturbation on the state transition matrix that causes the network to transition from a stable state to an unstable state; and assigning a fragility metric to each of the plurality N of physiological brain signals based on the minimum norm of the perturbation for that physiological brain signal.

A method of identifying an epileptogenic zone of a subjects brain includes: receiving a plurality N of physiological brain signals that extend over a duration, each of the plurality N of physiological brain signals acquired from the subjects brain; calculating within a time window a state transition matrix based on at least a portion of each of the plurality N of physiological brain signals, wherein the state transition matrix is a linear time invariant model of a network of N nodes corresponding to the plurality N of physiological brain signals; calculating a minimum norm of a perturbation on the state transition matrix that causes the network to transition from a stable state to an unstable state; and assigning a fragility metric to each of the plurality N of physiological brain signals based on the minimum norm of the perturbation for that physiological brain signal.

Adjustment of training protocols in virtual reality (VR) or augmented reality (AR) environments based on biofeedback are provided. In various embodiments, motion data is collected for a user while the user performs a training protocol in a virtual environment. A biometric measurement is collected for the user while the user performs the training protocol. The motion data and the biometric measurement are provided to a learning system at a remote server. The learning system determines an adjustment to the training protocol based on the motion data and the biometric measurement. The adjustment is provided by the learning system and is applied to the training protocol. In various embodiments, the adjustment, the motion data, and/or the biometric measurement may be logged in an electronic health record.

A system and method are described for controlling a vehicle interior environmental condition. A biometric sensor senses a biometric condition of a vehicle seat occupant and generates a sensed biometric condition value. A controller receives the sensed biometric condition value, a sensed interior environmental condition value, and a sensed exterior environmental condition value. Each of multiple exterior environmental condition values has an associated biometric condition value defined as optimal for the vehicle occupant. The controller determines the optimal biometric condition value associated with the sensed exterior environmental condition value, compares the optimal biometric condition value to the sensed biometric condition value, and in response to a difference between the optimal biometric condition value and the sensed biometric condition value, generates a control signal to control an actuator to control the controllable interior environmental condition to reduce the difference between sensed biometric condition value and the optimal biometric condition value.

Systems and methods are described for treatment of neurological conditions in which transcranial illumination using infrared, near-infrared and/or red wavelengths of light are delivered into the brain of a patient using a portable head wearable device. Arrays of light emitters are simultaneously controlled to provide a selected power density and distribution during a therapeutic session to treat a condition of the patient. An external controller such as a touchscreen tablet or laptop computer can be used to select illumination parameters for each patient. The system can be used to provide treatment to children such as those having autism spectrum disorder (ASD).

A fatigue relief method using smart footwear and an operation method for a user terminal are provided. The fatigue relief method using smart footwear comprises: receiving the provision of pressure data or acceleration data measured while a user wears smart footwear and performs an exercise, wherein the smart footwear has at least one pressure sensor or acceleration sensor and a tactile element, which are installed therein; estimating the fatigue of the user by using the provided pressured data or acceleration data; selecting one of a plurality of vibration solutions on the basis of the estimated fatigue; and allowing the tactile element of the smart footwear to vibrate according to the selected vibration solution.

There is described a system for providing an interface with activity recommendations by monitoring user activity in which user data relating to a user is received at a user device. The user data comprises at least image data, text input, biometric data, and audio data, and may have been captured using one or more sensors on the user device. The user data is processed using at least: facial analysis; body analysis; eye tracking; voice analysis; behavioural analysis; social network analysis; location analysis; user's activities analysis; and text analysis. Based on the user data, one or more states of one or more cognitive-affective competencies of the user may be determined. An emotional signature of the user is determined, based on the one or more states of the one or more cognitive-affective competencies of the user. Based on the emotional signature, one or more recommendations for improving the emotional signature may be recommended.