Title

Assessing Engagement In Simulation-Based Training Systems For Virtual Kinesic Cue Detection Training

Keywords

Engagement; Kinesic cues; Simulation-Based Training

Abstract

Combat Profiling techniques strengthen a Warfighter's ability to quickly react to situations within the operational environment based upon observable behavioral identifiers. One significant domain-specific skill researched is kinesics, or the study of body language. A Warfighter's ability to distinguish kinesic cues can greatly aid in the detection of possible threatening activities or individuals with harmful intent. This paper describes a research effort assessing the effectiveness of kinesic cue depiction within Simulation-Based Training (SBT) systems and the impact of engagement levels upon trainee performance. For this experiment, live training content served as the foundation for scenarios generated using Bohemia Interactive's Virtual Battlespace 2 (VBS2). Training content was presented on a standard desktop computer or within a physically immersive Virtual Environment (VE). Results suggest that the utilization of a highly immersive VE is not critical to achieve optimal performance during familiarization training of kinesic cue detection. While there was not a significant difference in engagement between conditions, the data showed evidence to suggest decreased levels of engagement by participants using the immersive VE. Further analysis revealed that temporal dissociation, which was significantly lower in the immersive VE condition, was a predictor of simulation engagement. In one respect, this indicates that standard desktop systems are suited for transitioning existing kinesic familiarization training content from the classroom to a personal computer. However, interpretation of the results requires operational context that suggests the capabilities of high-fidelity immersive VEs are not fully utilized by existing training methodologies. Thus, this research serves as an illustration of technology advancements compelling the SBT community to evolve training methods in order to fully benefit from emerging technologies. © 2013 Springer-Verlag Berlin Heidelberg.

Publication Date

1-1-2013

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

8021 LNCS

Issue

PART 1

Number of Pages

211-220

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-642-39405-8_25

Socpus ID

84884838468 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84884838468

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