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.
A Literature Study to Explore Empirically: What Is the Scientific Discipline of Human Factors and What Makes It Distinct from Other Related Fields
