Simulation based training system for measurement of team cognitive load to automatically customize simulation content
Inventors
Beaubien, Jeffrey • Feeney, John J. • DePriest, William N. • Pappada, Scott M.
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Assignees
Member
Aptima, Inc.Aptima, Inc. is a leader in optimizing and improving human performance in mission-critical, technology-intensive environments. Since 1995, Aptima has combined social science principles with advanced computational methods to deliver innovative, human-centered engineering solutions for defense, intelligence, aviation, law enforcement, and healthcare sectors. The company is recognized for its multidisciplinary approach, integrating expertise from engineering, data science, psychology, and cognitive science to address complex challenges. Aptima is committed to advancing research and development in artificial intelligence, machine learning, data analytics, and simulation, with a strong focus on enhancing human-machine teaming, learning, and readiness. Their mission is to ensure synergy between humans, technology, and organizations, accelerating learning and productivity in the age of AI. Aptima's culture is rooted in innovation, impact, and empathy, fostering an inclusive and supportive environment for its diverse team of experts.
Aptima, Inc. is a leader in optimizing and improving human performance in mission-critical, technology-intensive environments. Since 1995, Aptima has combined social science principles with advanced computational methods to deliver innovative, human-centered engineering solutions for defense, intelligence, aviation, law enforcement, and healthcare sectors. The company is recognized for its multidisciplinary approach, integrating expertise from engineering, data science, psychology, and cognitive science to address complex challenges. Aptima is committed to advancing research and development in artificial intelligence, machine learning, data analytics, and simulation, with a strong focus on enhancing human-machine teaming, learning, and readiness. Their mission is to ensure synergy between humans, technology, and organizations, accelerating learning and productivity in the age of AI. Aptima's culture is rooted in innovation, impact, and empathy, fostering an inclusive and supportive environment for its diverse team of experts.
Publication Number
US-10783801-B1
Publication Date
2020-09-22
Expiration Date
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. Some embodiments further automatically customize the simulation content by automatically selecting content based on the CL of the individuals and teams.
Core Innovation
In one example embodiment, a simulation based training system is provided that unobtrusively collects objective data for individuals and teams experiencing training content; 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, and some embodiments are further configured to include sociometric data 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.
The background identifies problems with prior art assessment methods that still use observer-based rating scales with anchors that range from 'low' to 'high' and the resulting lack of granularity, which can mask differences in team capacity and affect learner experience. The disclosure frames additional technical problems including how a simulator captures and quantifies indirect measures of the student that may not be directly related to performance but are tied to the ability to learn, how to objectively measure a cognitive load on the user, how to measure team cognitive load, and how the simulator selects the next scenario or simulation content from a large database to maximize training effect.
To address these technical challenges, the disclosed systems and methods define a data format to translate raw sensor data in real-time into objectively estimated cognitive load measures that can be used to provide feedback to the simulator for real-time customization of the training simulation and to improve post-training After Action Reviews (AARs). Team cognitive load models are disclosed that provide objective cognitive loads of the teams using the real-time cognitive load measures from team members, and embodiments compare the cognitive load of the user or team to a target effectiveness measure, including an objective 'Zone of Proximal Development' (ZPD), to determine the next simulation content to keep the user and simulation within the target effectiveness measure.
Claims Coverage
Two independent claims were identified. The following eight inventive features are extracted from those independent claims.
Processor based simulator comprising subsystems
A processor based simulator comprising: a user interface, a cognitive load measuring subsystem, and a simulation content customization subsystem.
Sensors capturing raw input from team members
A first sensor comprising a first physiological sensor configured to capture a first raw input from the first user and a second sensor comprising a second physiological sensor configured to capture a second raw input from the second user and communicate the raw inputs to the cognitive load measuring subsystem.
Input translator for compliant input values
An input translator configured to translate the first raw input to a first compliant input value and translate the second raw input to a second compliant input value.
Predefined individual cognitive load model
A predefined individual cognitive load model configured to determine a first cognitive load value from the first compliant input value and determine a second cognitive load value from the second compliant input value.
Team cognitive load model and communication
A team cognitive load model configured to determine a team cognitive load value from the first cognitive load value and the second cognitive load value, and the cognitive load measuring subsystem configured to communicate the team cognitive load value to the simulation content customization subsystem.
Simulation content customization based on cognitive load
The simulation content customization subsystem configured to determine a second simulation content based on the team cognitive load value and communicate the second simulation content as a media file to the user interface, and the processor based simulator configured to communicate the second simulation content to the user interface as a customized simulation content.
Zone of Proximal Development comparison
The cognitive load measuring subsystem further comprises a predefined range of values for a Zone of Proximal Development (ZPD) for the team, and the simulation content customization subsystem determines the second simulation content based on a comparison of the team cognitive load value to the ZPD for the team whereby a second team cognitive load value for the second simulation content is more likely to be within the ZPD.
Relative cognitive load model
A relative cognitive load model configured to determine a relative cognitive load value from the team cognitive load value and one of the first cognitive load value or the second cognitive load value, wherein the second simulation content is also determined based on the relative cognitive load value.
The independent claims cover a processor based simulator with integrated physiological or sociometric sensing, translation of raw inputs to compliant values, predefined individual and team cognitive load models (including relative cognitive load), communication of team cognitive load to a simulation content customization subsystem, and selection of simulation content based on comparisons to a predefined ZPD.
Stated Advantages
Unobtrusively collects objective data to determine individual and team cognitive states in real or near-real time.
Time-synchronizes various data streams and automatically calculates measures of team cognitive load (CL), cognitive load balance (CLB) and relative cognitive load (RCL).
Automatically customizes or dynamically modifies training simulation content in real time based on cognitive load measures to keep learners within an objective Zone of Proximal Development (ZPD).
Provides real-time alerts to instructors and supports dynamic modification of team training scenarios on the fly.
Enhances post-training After Action Reviews (AARs) by providing objective measures of cognitive load to explain team performance.
Permits objective selection of simulation content from a learning management system to maximize the effectiveness of training.
Documented Applications
Simulation Based Training (SBT) in medicine including emergency medicine, surgery, cardiology, and pediatrics, with examples such as Resusci® Anne, Noelle™, SimNewB™, Multiple Amputee Trauma Trainer™, LapTrainer™, Lap Mentor™, and patient-specific anatomical rehearsal systems.
Measurement and monitoring of individual and team workload during simulation-based training and real-time individual and team training.
Use of cognitive load measures to guide After Action Reviews (AARs) for training exercises.
Objective selection or automatic modification of the next training scenario or simulation content from a large database or learning management system to maximize training effectiveness.
Health monitoring and individual and team workload monitoring outside of simulators as noted for wider applicability.
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