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 enhanced exoskeleton system is disclosed comprising an exoskeleton, a base layer comprising at least one sensor, an exoskeleton actuator configured to actuate the exoskeleton, a control subsystem comprising one or more processors, and memory elements including instructions that, when executed, cause the processors to perform operations comprising: receiving sensor data from the sensor, determining a future movement intent of a user of the exoskeleton, determining a command for an exoskeleton actuator based on the future movement intent, and communicating the command to the exoskeleton actuator whereby the exoskeleton is actuated by the exoskeleton actuator. In some embodiments, the sensor data comprises an anticipatory data value from an anticipatory sensor. In some embodiments, the sensor data comprises an anticipatory data value from an electromyography (EMG) sensor.

Abstract: An interactive planning system is provided to allow a user to create and manage plans in online, offline, and intermittent connectivity environments. In some embodiments, the interactive planning system comprises a mobile, web-based application with an in-browser database configured to allow the user to create and manage plans in an offline environment and synchronize the plan when online connectivity is restored. In some embodiments, the interactive planning system comprises a fitness planning system configured to allow users to create and manage physical fitness plans in online, offline, and intermittent connectivity environments; to provide instructors the ability to enter fitness plan attributes, detail focus areas and timeframe for fitness plans; to provide instructors the ability to highlight macro-level phases to inform plan analytics; and to provide an ability to share programs to groups.

Abstract: Methods of and systems to provide performance measurement are provided utilizing an architecture configured to efficiently merge and monitor different types of performance data. Connectors are provided to receive and translate different types of performance data from different sources. The performance data is translated into and stored in a common data model format. In some embodiments, key attributes are defined for each of the performance data sources that uniquely characterizes each relevant performance data so that is can be parsed into separate processing streams to increase system performance. The key attributes also act as cues to organize the performance data as it is being merged so that it can be accessed without requiring a specific source data linkage. Using model listeners, determinations can be quickly made regarding when performance data is changed to reduce calculations necessary to determine measure values. Some embodiments merge different types of performance data in real-time.

Abstract: Methods to infer user behavior are disclosed comprising a process of predefining one or more activities for a user application, providing a processor based device and user interface configured to operate with the device to support the user with tasks using the user application, the user application communicating an intent message to a transformative power management (TPM) application and the TPM configured to define and output an instruction for the application given the intention and the predefined activities. In some embodiments, the methods are implemented on a processor based device. In some embodiments, the systems and methods apply pattern recognition algorithms and pattern learning algorithms to manage the power allocation to power consuming devices.

Abstract: Example embodiments of systems and methods for network pattern matching provide the ability to match hidden networks from noisy data sources using probabilistic matching analysis. The algorithms may map roles and patterns to observed entities. The outcome is a set of plausible network models. The pattern-matching methodology of these systems and methods may enable the solution of three challenges associated with social network analysis, namely network size and complexity, uncertain and incomplete data, and dynamic network structure.

Abstract: A communications training system is provided having a user interface, a computer-based simulator and a performance measurement database. The user interface is configured to receive a speech communication input from the user based on a training content and the computer-based simulator is configured to transform the speech communication to a text data whereby the text data can be aligned to performance measurement database values to determine a performance measure of the speech communication. The format of the text data and the performance measurement database values enable the speech communication to be aligned with predefined performance measurement database values representing expected speech communications for that training content.

Abstract: A processor based input system is provided for automatically determining the cognitive assessment of a human as an input source to a processor based automated system configured for use in a human-machine team. In some embodiments, the processor based input system comprises an input device, a cognitive assessment input system (CAIS) and a processor based automated system. In some embodiments the CAIS comprises an interaction data acquisition module configured to receive input data from the input device as interaction data, a cognitive indicators and work patterns analysis module configured to determine a cognitive measure from the interaction data, an intervention building module configured to determine automation directives for a processor based automated system from the cognitive measure and the automated system is configured to receive and execute automation directives from the CAIS as an input source to the processor based automated system.

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: Systems and methods for supporting medical therapy decisions are disclosed that utilize predictive models and electronic medical records (EMR) data to provide predictions of health conditions over varying time horizons. Embodiments also determine a 0-100 health risk index value that represents the “risk” for a patient to acquire a health condition based on a combination of real-time and predicted EMR data. The systems and methods receive EMR data and use the predictive models to predict one or more data values from the EMR data as diagnostic criteria. In some embodiments, the health condition trying to be avoided is Sepsis and the health risk index is a Sepsis Risk Index (SRI). In some embodiments, the predictive models are neural network models such as time delay neural networks.

Abstract: Embodiments of the subject invention comprise a computer based system and methods to collect and compare the attributes of a group of entities using data representing topic data of the entity and interaction data between entities. Embodiments of the invention comprise using minimally invasive means to automatically collect and model both an entity's attributes such as their knowledge/work/interest as well as model the social interactions of the entity together with a means to identify opportunities to influence changes in the entity attributes. Minimally invasive means to collect and model attributes include semantic analysis and topic modeling techniques. Means to model social interactions include social network analysis techniques that can incorporate location data of the entity. Embodiments of the invention further provide a sharable index of the attributes of the entities and the group of entities.

Abstract: Systems and methods for supporting medical therapy decisions are disclosed that utilize predictive models and electronic medical records (EMR) data to provide predictions of health conditions over varying time horizons. Embodiments also determine a 0-100 health risk index value that represents the “risk” for a patient to acquire a health condition based on a combination of real-time and predicted EMR data. The systems and methods receive EMR data and use the predictive models to predict one or more data values from the EMR data as diagnostic criteria. In some embodiments, the health condition trying to be avoided is Sepsis and the health risk index is a Sepsis Risk Index (SRI). In some embodiments, the predictive models are neural network models such as time delay neural networks.

Abstract: Embodiments of the subject invention comprise a computer based system and methods to collect and compare the attributes of a group of entities using data representing topic data of the entity and interaction data between entities. Embodiments of the invention comprise using minimally invasive means to automatically collect and model both an entity's attributes such as their knowledge/work/interest as well as model the social interactions of the entity together with a means to identify opportunities to influence changes in the entity attributes. Minimally invasive means to collect and model attributes include semantic analysis and topic modeling techniques. Means to model social interactions include social network analysis techniques that can incorporate location data of the entity. Embodiments of the invention further provide a sharable index of the attributes of the entities and the group of entities.

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: Methods for selecting treatment paths are disclosed generally comprising the steps of: (a) discovering a set of treatment path clusters based on latent patterns in historical patient trace data, (b) building a set of binary classifiers based on historical patient trace data, historical patient data and target outcomes, and (c) given the treatment path clusters, actual patient data, and a selected target outcome, applying the binary classifiers to predict a treatment path for a new patient that optimizing the selected target outcome. Processor based systems to implement the methods are also disclosed.

Abstract: In one embodiment of the invention, a training model for students is provided that models how to present training items to students in a computer based adaptive trainer. The training model receives student performance data and uses the training model to infer underlying student skill levels throughout the training sequence. Some embodiments of the training model also comprise machine learning techniques that allow the training model to adapt to changes in students skills as the student performs on training items presented by the training model. Furthermore, the training model may also be used to inform a training optimization model, or a learning model, in the form of a Partially Observable Markov Decision Process (POMDP).

Abstract: Systems and methods to define a scenario of conditions comprising the steps of defining at least one condition for at least one educational objective, the at least one condition being represented by a constraint and scheduling the conditions into a scenario of conditions. In some embodiments, the scheduling is performed by analyzing the constraints using constraint programming. In some embodiments, the constraints comprise mathematical or computational constraints representing a range of variables. Also disclosed are systems and methods to monitor a scenario of conditions.

Abstract: Embodiments of this invention comprise modeling a team's state and the influence of training treatments, or actions, on that state to create a training policy. Both state and effects of actions are modeled as probabilistic using Partially Observable Markov Decision Process (POMDP) techniques. Utilizing this model and the resulting training policy with teams creates an effective decision aid for instructors to improve learning relative to a traditional scenario selection strategy.

Abstract: Methods of and systems to provide performance measurement are provided utilizing an architecture configured to efficiently merge and monitor different types of performance data. Connectors are provided to receive and translate different types of performance data from different sources. The performance data is translated into and stored in a common data model format. In some embodiments, key attributes are defined for each of the performance data sources that uniquely characterizes each relevant performance data so that is can be parsed into separate processing streams to increase system performance. The key attributes also act as cues to organize the performance data as it is being merged so that it can be accessed without requiring a specific source data linkage. Using model listeners, determinations can be quickly made regarding when performance data is changed to reduce calculations necessary to determine measure values. Some embodiments merge different types of performance data in real-time.

Abstract: Systems and methods to define a scenario of conditions comprising the steps of defining at least one condition for at least one educational objective, the at least one condition being represented by a constraint and scheduling the conditions into a scenario of conditions. In some embodiments, the scheduling is performed by analyzing the constraints using constraint programming. In some embodiments, the constraints comprise mathematical or computational constraints representing a range of variables. Also disclosed are systems and methods to monitor a scenario of conditions.