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 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: An electronic assembly and digital subscriber line splitter. The splitter includes a subrack housing, a plurality of splitter port cards, a connector card and an edge card. The splitter port cards each include a plurality of splitter ports and are disposed within the housing between, and in substantially perpendicular relation to, the first side and the second side. The connector card is disposed adjacent, and in substantially parallel relation, to the back of the housing and includes a plurality of external connectors that extend through the openings through the back of the housing. The edge card is disposed in substantially parallel relation to the connector card, includes a plurality of internal connectors disposed in electrical communication with the external connectors of the connector card, and is removably attached to each of the splitter port cards such that each splitter port is in electrical communication with one of the external connectors of the connector card.