REFERENCE TO SEQUENCE LISTING, A TABLE, OR A COMPUTER PROGRAM LISTING COMPACT DISC APPENDIX

Not Applicable.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates to training simulators, in particular a computer-based training simulator with tools to assess the cognitive load of one or more users.

2. Description of the Prior Art

Heart rate variability, oculomotor activity (EOG), pupilometry, functional near infrared imaging (fNIR) and galvanic skin response have been employed to detect cognitive state changes; however, the electroencephalogram (EEG) is the only physiological signal that has been shown to accurately reflect subtle shifts in alertness, attention and workload that can be identified and quantified on a second-by-second time-frame. Significant correlations between EEG indices of cognitive state changes and performance have been reported based on studies conducted in laboratory, simulation and operational environments.

Simulation Based Training (SBT) is used in many branches of medicine—including emergency medicine, surgery, cardiology, and pediatrics, among others—and there are simulation platforms for initial skill acquisition and ongoing maintenance of certification. For example, in emergency medicine there are simulators for practicing basic lifesaving skills (Resusci® Anne); for performing “mock codes” on expectant mothers (Noelle™) and their newborns (SimNewB™); and for practicing advanced combat lifesaving skills on wounded Soldiers (Multiple Amputee Trauma Trainer™). In surgery, there are box trainers for practicing basic skills such as cutting and suturing (LapTrainer™); virtual reality (VR) systems for practicing routine surgical procedures such as laparoscopic cholecystectomy (Lap Mentor™); and patient-specific anatomical models that allow surgeons to rehearse a patient's specific case prior to conducting it in vivo (Surgical Theater™).

Although much progress has been made with regard to skill training, substantially less progress has been made with regard to skill and performance assessment. Many assessment methods still use observer-based rating scales with anchors that range from “low” (1) to “high” (5). One problem with this approach is its level of granularity. For example, one could imagine two medical teams performing the same SBT exercise. While both teams could receive identical numerical ratings, the first team could be performing at the extreme upper bound of its capability, while the second could have a substantial amount of reserve capacity that remains unexercised. It is further possible that despite receiving acceptable scores, both teams could walk away from the exercise feeling demoralized. Specifically, the first team might feel overwhelmed by the workload; they might leave doubting their capabilities as healthcare providers. The second team might feel that they were not sufficiently challenged; they might leave questioning the organization's commitment to providing a quality learning experience. Both outcomes must be avoided at all costs.

BRIEF SUMMARY OF THE INVENTION

The following summary is included only to introduce some concepts discussed in the Detailed Description below. This summary is not comprehensive and is not intended to delineate the scope of protectable subject matter, which is set forth by the claims presented at the end.

Given the prior art solutions, modern simulators that are configured to maximize the training effect in real-time, how do they account for real-time data from various sensors to maximize the effect of that training? In particular, how does a simulator capture and quantify indirect measures of the student that may not be directly related to the user's performance but may be very directly tied to the user's ability to learn from the simulation? In particular, how does the simulator use the data from sensors to objectively measure a cognitive load on the user and how does the simulator use that measure to customize the simulation content to maximize the effect of the simulator? For training simulations that train teams of users, the complexity of the problem is compounded by the many possible ways to measure a team of simulator users.

In addition, given the user measures from the simulator, a technical problem also exists in how the simulator selects the next scenario or simulation content for the user. In particular, how does the simulator select the specific scenario or simulation content, from a large database of scenarios or simulation content, which maximizes the effect of the simulator on the user?

To address these technical challenges, the disclosed systems and methods provide a solution that uses sensitive and diagnostic measures of individual and team states to provide data that can be used to assess the cognitive state of individuals and teams in simulators and maximize the effectiveness of simulation-based training. The technical solution defines a data format to be used to translate the raw data from sensors in real-time so that objectively estimates of a cognitive load of the user can be made in real-time. This real-time quantified cognitive load data can then be used to provide feedback to the simulator to allowing for real-time customization of the training simulation. The objective measures may also allow simulators and trainers to increase the effectiveness of post-training After Action Reviews (AARs).

For the challenges presented when measuring the cognitive load of a team of users, team cognitive load models are disclosed that provide objective cognitive loads of the teams given the real-time cognitive load measures from team members.

Some embodiments of the disclosed solution also address the technical challenge of maximizing the effectiveness of the simulation by comparing the cognitive load of the user to a target effectiveness measure and determining the next simulation content to keep the user and simulation within the target effectiveness measure. These effectiveness measures may be used keep the user within an objective “Zone of Proximal Development” to maximize the training effectiveness given the cognitive load of the user.

Some embodiments of the disclosed solution also address the technical challenge of how to maximize the effectiveness of the simulation by objectively selecting content from a learning management system.

In one example embodiment, a processor based method of assessing a workload of a user in a simulator is provided wherein the method comprises determining a first individual cognitive load measure from the first data with an individual cognitive load measure model. In some embodiments, team cognitive load measures are determined.

In one example embodiment, the present system comprises a processor based cognitive load assessment system that unobtrusively collects objective data that can be used to determine individual and team cognitive states; time-synchronizes the various data streams; and automatically calculates measures of team cognitive load (CL), cognitive load balance (CLB) and/or relative cognitive load (RCL) in real or near real time. Data is unobtrusively gathered through physiological or other activity sensors such as electroencephalogram (EEG), electrocardiogram (ECG), and accelerometry sensors. Some embodiments are further configured to include data about team communications—such as conversational turn-taking, interruptions, and dysfluencies using audio sensors and/or sociometric badges—when determining cognitive loads. Some embodiments may also use sensors that detect heart rate variability, oculomotor activity (EOG), pupilometry, functional near infrared imaging (fNIR) or galvanic skin response.

Some embodiments are further configured to send alerts to a user interface, such as a handheld device, if a threshold of cognitive load is met. Using this real or near-real time assessment and/or alerts, instructors can dynamically modify their team training scenarios on the fly. For example, if the team is overwhelmed (in a “Zone of Confusion”), the instructor or training system could reduce the complexity of the problem (through the training scenario or content) or expand the team size, for example by calling in additional team members. Alternatively, if the team is underwhelmed (in a “Zone of Boredom”), the instructor could increase the complexity of the problem, or reduce the size of the team, for example by having one of the team members unexpectedly leave the simulation or by adding another simultaneous task into the simulation. Until recently, this capability has not been possible. Rather, the common method of modifying training was to help the instructor select the next most appropriate training scenario. Given that scenario-based training—especially team training—can be extremely costly, the approach of automatically assessing cognitive load and dynamically modifying scenarios in real time holds a great deal of promise for the training community-at-large.

