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A proof of concept virtual reality system is presented that integrates ultrasonic feedback sensations to provide a demonstrative virtual reality exposure therapy environment containing multiple scenarios with virtual spiders. This system and environment were utilised to conduct a study containing 35 participants with the goal of investigating the effect the environment could have on their level of anxiety. This level of anxiety was measured in three different forms: changes in frontal asymmetry analysis of EEG data, changes in skin conductance levels and subjective units of distress. The Fear of Spiders Questionnaire was used to determine which participants in the study reported to be moderately afraid of spiders. For these participants all three measurement forms for anxiety showed statistically significant increases in a comparison between baseline and scenarios with the virtual spiders. A statistically significant correlation between scores on the Fear of Spiders Questionnaire and changes in anxiety shows the system to have had a greater effect on the anxiety levels of those who were more afraid of spiders, than those who were not. There was also a statistically significant correlation discovered between immersion and increase in anxiety, highlighting the significance of immersion in future virtual reality exposure therapy applications.

Designing human-machine interfaces (HMIs) for defence systems (such as vehicle-based weapon terminals) faces numerous challenges such as limited workspace, operation in extreme environments and essential protective gear for operators and equipment. This paper presents a study designed to investigate the HMI and teaming for ground-based air defence systems and reports the findings. The objective of the first stage of the study is to assess the usability, satisfaction, ease of use and design of the current systems to identify avenues for improvement in future system development. The main findings confirm some of the issues identified in the early human factors analysis, such as the lack of space, task complexity and the level of expertise required for efficient system operation. The reported findings do not just help identify the areas for improvement in the HMI, but also identify tasks that could be assisted by Artificial Intelligence (AI) integrated within current system functions, to improve human performance whilst also reducing cognitive load, errors and the risk of poor decision-making.