Digital Twins for Early Resolution of Human Factors Issues: A Review of the State of the Art

Digital twins have recently gained momentum as a mechanism to inform, monitor, and predict system performance and health. A digital twin has four characteristics: data, model, dynamic evolution, and personalization. Although digital twins have been conceptualized for many applications, such as manufacturing processes and aircraft systems, in many domains they are still in the early research stage. Many companies and academic researchers have highlighted the use of digital twins for systems and their processes, but to date less progress has been made on applying the concept to modeling human factors. Just as digital twins can be used to predict system performance and health, they can also be used to predict human-system response and behavior based on human-system interactions. Some applications of the digital twin concept in human factors engineering include human-robot collaborative manufacturing assembly, sports and athletics, and human combatants in the military. Continued research and development of digital twins for humans should explore cognition models, human condition and physique data, human factors digital twin ecosystems, and digital twin hierarchies. Digital twins may be a promising tool for human factors evaluation in the system lifecycle. However, challenges related to development cost and time, model sensitivity and intractability, and availability of data may make digital twins difficult to implement and use.