Augmented Multisensory Interface Design (AMID): A Human-Centric Approach to Unisensory and Multisensory Augmented Reality Design

Abstract

This study demonstrates that the use of augmented reality interfaces, designed based on a theoretically driven human-centered framework, can significantly improve task performance. The domain of unmanned ground vehicle landmine/ unexploded ordnance detection and disposal was selected because it is a real-world example of a complex human-robot interaction task. Participants in this study were assigned to one of seven experimental conditions. A new method (the augmented multisensory interface design, or AMID, method) was used to identify two unisensory and one multisensory performance-enhancing augmented reality (AR) interface solutions, along with three less appropriate interface solutions. All participants were trained in the operation of the AR devices they were assigned and also in telerobotic landmine detection operations. After completing the training, participants were given the task of clearing a minefield. Results show that participants in the AMID-compliant solution groups significantly outperformed the control group and their non-AMIDcompliant group counterparts in terms of the numbers of landmines correctly identified. The AMID-compliant multisensory condition was found to result in significantly increased landmine detection, increased sensitivity, and decreased task time.

Augmented Reality Technology Adoption in Tourism Using Structural Equation Model

“…As a direct consequence of this, the concept of AR has been subjected to a variety of adaptations, the specifics of which shift depending on the environment or the approach taken to implementing it (Van Krevelen and Poelman 2010). Stone et al (2009) outlined a set of universally accepted essential characteristics that must be incorporated into AR in order to establish a connection between the virtual and real environments, associate with local environment, and link real with simulated objects. Employing this idea as a base for his definition of AR, Rouse (2015) characterized it as the combination of metadata with the actual footage of the consumer's surroundings.…”