Abstract: A contextualized sensor system (CSS) is provided comprising one or more sensors, one or more memory elements, an alerting library stored in the one or more memory elements, one or more processors, and the one or more memory elements including instructions that, when executed, cause the one or more processors to perform operations comprising: receiving from one of the one or more sensors one or more sensor data, comparing the first sensor data to the alerting library to determine whether an alert situation has occurred, and communicating an alert if the alert situation has occurred. In some embodiments, the CSS further comprises removable sensor modules and component blocks to provide multiple configurations of CSS components. In some embodiments, the component blocks comprise sensors or power sources.

Abstract: Disclosed are systems and methods of communicating an attribute of an artificial intelligence (AI) agent system through an interface. The methods comprise receiving input data, determining attribute values of attributes to the input data over temporal periods, determining interface attribute values representing the interface attributes and communicating the interface attribute values to a dynamic interface. Also disclosed is a contextualized system comprising a contextualizer module and a user interface configured to communicate a recommended data to a user. In some embodiments, the contextualizer module selects a most pertinent data as the recommended data based on an activity of the user.

Abstract: Computer based systems and methods for estimating a user state are disclosed. In some embodiments, the methods comprise inputting a first input at an intermittent interval and a second input at a frequent interval into a user state estimation model to estimate the user state. In some embodiments, the first inputs are enhanced by injecting a noise input to create a plurality of enhanced first inputs whereby the plurality of enhance first inputs correspond to the plurality of second inputs at the frequent interval. In some embodiments, the first input comprises a self-reported input and the second inputs comprise a physiological input, a performance input or a situational input. In some embodiments, a machine learning algorithm creates the state estimation model. In some embodiments, the state estimation model estimates a future user state. In some embodiments, a computer based system for estimating a user state is provided.

Abstract: 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.

Abstract: A contextualized sensor system is provided comprising one or more sensors, one or more memory elements, a library of alert rules stored in the one or more memory elements, one or more processors, and the one or more memory elements including instructions that, when executed, cause the one or more processors to perform operations comprising: receiving from one of the one or more sensors one or more sensor data, comparing the first sensor data to a library of alert rules to determine whether an alert situation has occurred, and communicating an alert if the alert situation has occurred. In some embodiments, the operations further comprise contextualizing an environmental data, a location data, a physiological data, a behavior data and an orientation data.

Abstract: Computer based systems and methods for estimating a user state are disclosed. In some embodiments, the methods comprise inputting a first input at an intermittent interval and a second input at a frequent interval into a user state estimation model to estimate the user state. In some embodiments, the first inputs are enhanced by injecting a noise input to create a plurality of enhanced first inputs whereby the plurality of enhance first inputs correspond to the plurality of second inputs at the frequent interval. In some embodiments, the first input comprises a self-reported input and the second inputs comprise a physiological input, a performance input or a situational input. In some embodiments, a machine learning algorithm creates the state estimation model. In some embodiments, the state estimation model estimates a future user state. In some embodiments, a computer based system for estimating a user state is provided.

Abstract: Computer based systems and methods for estimating a user state are disclosed. In some embodiments, the methods comprise inputting a first input at an intermittent interval and a second input at a frequent interval into a user state estimation model to estimate the user state. In some embodiments, the first inputs are enhanced by injecting a noise input to create a plurality of enhanced first inputs whereby the plurality of enhance first inputs correspond to the plurality of second inputs at the frequent interval. In some embodiments, the first input comprises a self-reported input and the second inputs comprise a physiological input, a performance input or a situational input. In some embodiments, a machine learning algorithm creates the state estimation model. In some embodiments, the state estimation model estimates a future user state. In some embodiments, a computer based system for estimating a user state is provided.

Abstract: Computer based systems and methods for estimating a user state are disclosed. In some embodiments, the methods comprise inputting a first input at an intermittent interval and a second input at a frequent interval into a user state estimation model to estimate the user state. In some embodiments, the first inputs are enhanced by injecting a noise input to create a plurality of enhanced first inputs whereby the plurality of enhance first inputs correspond to the plurality of second inputs at the frequent interval. In some embodiments, the first input comprises a self-reported input and the second inputs comprise a physiological input, a performance input or a situational input. In some embodiments, a machine learning algorithm creates the state estimation model. In some embodiments, the state estimation model estimates a future user state. In some embodiments, a computer based system for estimating a user state is provided.

Abstract: A vertical display unit for mounting to a vertical post of an associated structure that has apertures formed therein. First and second brackets include fastening mechanisms that are used to fasten the spine to the brackets and securing mechanisms that are used to secure the brackets to the associated structure. The brackets include first and second spacers that are positioned between the spine and the associated structure so that a portion of the spine is spaced from a portion of the associated structure. Any combination of first and second wings or spine attachments are attached to the spine. Wing attachments may also be fastened to the first and second wings. The wing attachments, first and second wings, and/or the spine attachments can accommodate goods to be displayed.