SoarTech (United States)

This abstract is a companion piece to the ninth iteration of the “Me and My VE” interactive session. The purpose of this alternative format session is to allow human factors researchers to demonstrate how they use virtual reality, mixed reality, and virtual environments to support end-user research. The session begins with each presenter providing a brief introduction of their virtual reality, mixed reality, or virtual environment application. After this introduction, attendees engage with the presenters and their virtual environments in an interactive demonstration period. This year’s demonstrations include applications ranging from designing virtual environments for accessibility to using virtual and extended reality to support medical and aviation training, content validation, and human-computer interaction studies. The goal of the session is to allow attendees to experience how human factors professionals use virtual environments to support human factors-oriented research and to learn about the exciting work being conducted in this field.

Background Patient safety and improved outcomes are core priorities in healthcare, and effective handoffs are essential to these priorities. Validating handoff tools using simulation is a novel approach. Methods The construct validity and instrument reliability of the I-BIDS© tool were tested. In Phase I, construct validity was substantiated with a convenience sample of 21 healthcare providers through an electronic survey. Content Validity Ratio (CVR) was tabulated using Lawshe’s CVR. Interrater reliability was tested in a simulated handoff scenario, in Phase II, with graduate nursing students and two raters, and simulation effectiveness was assessed by students. Results Construct validity was evaluated, and 17 of the 25 items were found significant at the critical level (0.42). Items scoring below were removed, and the tool was reduced by one category. Weighted kappa (Kw) with quadratic weights was run from the scenario data to determine if there was an agreement between raters of handoff performance. There was a statistically significant agreement between the two raters, Kw = .627 (95% CI: .549–.705), p < .001) with good strength of the agreement. SET-M Total mean was 55.64 ( SD = 2.46). Discussion The tool showed beginning validity and interrater reliability. The SET-M Learning subscale showed the widest range of scores which suggests the most opportunity for improvement. Use of the tool in simulated scenarios may be one way to test the items further. Conclusions Simulation was effective in facilitating the evaluation of the tool.

Frequently, the human element is overlooked and regarded as an afterthought. For instance, training programs may teach workarounds for design issues rather than fixing them before building a system. However, diagnosing and identifying issues beforehand by looking at the whole system is difficult. In the Vision for Naval Aviation 2025, Live, Virtual and Constructive (LVC) training is stated as fundamental to fleet readiness when faced with increasing threat capabilities and increasing cost to operate live aircraft. Further, the Chief Naval Office’s navigation plan cites LVC training as the goal for the PB-16 program to ensure sailors are “prepared, confident and proficient.” Thus, the issue of current training technologies not addressing the increasing threat has been identified. To address this, and include the human element, the Office of Naval Research (ONR) initiated the LVC Training Fidelity (LVC TF) program, specifically focused on the safety, fidelity and technological needs for LVC.

A number of researchers have attempted to model human driving or flying skills (e.g., acceleration, steering, vehicle following, etc) in an effort to develop robotic or simulated driver models. In these applications, validation consists of comparing the source data to the model output; thus, it is assumed that model fidelity is correlated with similarity to true human performance. This paper reviews some of the validation metrics found in the literature and discusses the limitations of these metrics. It also presents an alternative metric designed to mitigate these limitations and the test-bed designed to derive this metric. Finally, it illustrates the application of this metric to a number of different trajectory models built through a variety of modeling techniques (e.g., Kalman filters, neural networks, and Newtonian equations).