Improved performance research integration tool (IMPRINT): Human performance modeling for improved system design

2010 ◽  
Author(s):  
Charneta Samms
Keyword(s):  
System Design ◽  
Human Performance ◽  
Performance Modeling ◽  
Research Integration ◽  
Improved Performance ◽  
Performance Research

Performing System Tradeoff Analyses Using Human Performance Modeling

2016 ◽  
Vol 60 (1) ◽  
pp. 734-734
Author(s):  
Michael E. Watson ◽  
Christina F. Rusnock ◽  
Michael E. Miller ◽  
John M. Colombi
Keyword(s):  
Human Factors ◽  
System Design ◽  
System Performance ◽  
Task Analysis ◽  
Human Performance ◽  
Human Subjects ◽  
Performance Data ◽  
Performance Requirement ◽  
Trade Study ◽  
Improved Performance

Humans perform critical functions in nearly every system, making them vital to consider during system development. Human Systems Integration (HSI) would ideally permit the human’s impact on system performance to be effectively accounted for during the systems engineering (SE) process, but effective processes are often not applied, especially in the early design phases. Failure to properly account for human capabilities and limitations during system design may lead to unreasonable expectations of the human. The result is a system design that makes unrealistic assumptions about the human, leading to an overestimation of the human’s performance and thus the system’s performance. This research proposes a method of integrating HSI with SE that allows human factors engineers to apply Systems Modeling Language (SysML) and human performance simulation to describe and communicate human and system performance. Using these models, systems engineers can more fully understand the system’s performance to facilitate design decisions that account for the human. A scenario is applied to illustrate the method, in which a system developer seeks to redesign an example system, Vigilant Spirit, by incorporating system automation to improve overall system performance. The example begins by performing a task analysis through physical observation and analysis of human subjects’ data from 12 participants employing Vigilant Spirit. This analysis is depicted in SysML Activity and Sequence Diagrams. A human-in-the-loop experiment is used to study performance and workload effects of humans applying Vigilant Spirit to conduct simulated remotely-piloted aircraft surveillance and tracking missions. The results of the task analysis and human performance data gathered from the experiment are used to build a human performance model in the Improved Performance Research Integration Tool (IMPRINT). IMPRINT allows the analyst to represent a mission in terms of functions and tasks performed by the system and human, and then run a discrete event simulation of the system and human accomplishing the mission to observe the effects of defined variables on performance and workload. The model was validated against performance data from the human-subjects’ experiment. In the scenario, six different scan algorithms, which varied in terms of scan accuracy and speed, were simulated. These algorithms represented different potential system trades as factors such as various technologies and hardware architectures could influence algorithm accuracy and speed. These automation trades were incorporated into the system’s block definition (BDD), requirements, and parametric SysML diagrams. These diagrams were modeled from a systems engineer’s perspective; therefore they originally placed less emphasis on the human. The BDD portrayed the structural aspect of Vigilant Spirit, to include the operator, automation, and system software. The requirements diagram levied a minimum system-level performance requirement. The parametric diagram further defined the performance and specification requirements, along with the automation’s scan settings, through the use of constraints. It was unclear from studying the SysML diagrams which automation setting would produce the best results, or if any could meet the performance requirement. Existing system models were insufficient by themselves to evaluate these trades; thus, IMPRINT was used to perform a trade study to determine the effects of each of the automation options on overall system performance. The results of the trade study revealed that all six automation conditions significantly improved performance scores from the baseline, but only two significantly improved workload. Once the trade study identified the preferred alternative, the results were integrated into existing system diagrams. Originally system-focused, SysML diagrams were updated to reflect the results of the trade analysis. The result is a set of integrated diagrams that accounts for both the system and human, which may then be used to better inform system design. Using human performance- and workload-modeling tools such as IMPRINT to perform tradeoff analyses, human factors engineers can attain data about the human subsystem early in system design. These data may then be integrated into existing SysML diagrams applied by systems engineers. In so doing, additional insights into the whole system can be gained that would not be possible if human factors and systems engineers worked independently. Thus, the human is incorporated into the system’s design and the total system performance may be predicted, achieving a successful HSI process.

Human Performance Modeling in the Army: A Long and Winding Road

2005 ◽  
Vol 49 (12) ◽  
pp. 1191-1195 ◽  
Author(s):  
Laurel Allender ◽  
Troy Kelley ◽  
John Lockett ◽  
Sue Archer
Keyword(s):  
Decision Making ◽  
System Design ◽  
Cognitive Modeling ◽  
Human Performance ◽  
Performance Modeling ◽  
Cognitive Models ◽  
History Of ◽  
Modeling Practices ◽  
The U.S ◽  
Early Influences

The history of human performance modeling (HPM) in the U.S. Army is described, the early influences and technological events that made it possible. Highlights of significant milestones are presented, including HPM efforts that were influential in influencing the U.S. Army's modeling practices and in changing system design. The latest challenges in cognitive modeling, advanced decision making, stressors, and the particular challenges of distributed and linked simulations are discussed as well as the prospect of using methods from neuroscience for validation of cognitive models.

scholarly journals Informing System Design Using Human Performance Modeling

2017 ◽  
Vol 20 (2) ◽  
pp. 173-187 ◽  
Author(s):  
Michael Watson ◽  
Christina Rusnock ◽  
Michael Miller ◽  
John Colombi
Keyword(s):  
System Design ◽  
Human Performance ◽  
Performance Modeling

Modeling Human Performance with Environmental Stressors: A Case Study of the Effect of Vehicle Motion

2007 ◽  
Vol 51 (12) ◽  
pp. 780-783
Author(s):  
Josephine Q. Wojciechowski
Keyword(s):  
Human Performance ◽  
Performance Modeling ◽  
Environmental Stressors ◽  
Modeling Tools ◽  
Moving Vehicle ◽  
Task Time ◽  
Improved Performance ◽  
And Performance ◽  
The Impact

Human performance modeling tools are used to predict mission performance as a function of human performance. The U.S. Army Research Laboratory has developed a human performance modeling tool, the Improved Performance Research Integration Tool (IMPRINT), for investigation of the impact on a Soldier's performance when the Soldier subjected to environmental stressors such as heat and cold. IMPRINT has the capability to create user-defined stressors to study the stressors' effect on human performance and therefore system performance. This case study used data from literature to create a user-defined stressor in IMPRINT to predict the effect of riding in a moving vehicle on task time and performance. This capability can provide useful information to system designers.

Cognitive Human Performance Modeling by Demonstration

2005 ◽  
Vol 49 (12) ◽  
pp. 1153-1156
Author(s):  
Bonnie E. John
Keyword(s):  
User Interface ◽  
Human Computer Interaction ◽  
System Design ◽  
Human Performance ◽  
Performance Modeling ◽  
Cognitive Architecture ◽  
User Centered Design ◽  
Performance Models ◽  
Rich History ◽  
Computer Interaction

Cognitive human performance models have enjoyed a rich history in human-computer interaction but have yet to make a widespread impact in system design, possibly because they are difficult to construct. We employed user-centered design techniques to develop a new tool that is easier to use than previous methods or tools. CogTool combines a familiar method of prototyping, modeling by demonstration, and the ACT-R cognitive architecture to enable user interface designers to make valid human performance models with little effort.

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