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Conference Papers Year : 2020

Introducing Mental Workload Assessment for the Design of Virtual Reality Training Scenarios

Abstract

Training is one of the major use cases of Virtual Reality (VR) due to the flexibility and reproducibility of VR simulations. However, the use of the user's cognitive state, and in particular mental workload (MWL), remains largely unexplored in the design of training scenarios. In this paper, we propose to consider MWL for the design of complex training scenarios involving multiple parallel tasks in VR. The proposed approach is based on the assessment of the MWL elicited by each potential task configuration in the training application. Following the assessment, the resulting model is then used to create training scenarios able to modulate the user's MWL over time. This approach is illustrated by a VR flight training simulator based on the Multi-Attribute Task Battery II, which solicits different cogni-tive resources, able to generate 12 different tasks configurations. A first user study (N = 38) was conducted to assess the MWL for each task configuration using self-reports and performance measurements. This assessment was then used to generate three training scenarios in order to induce different levels of MWL over time. A second user study (N = 14) confirmed that the proposed approach was able to induce the expected mental workload over time for each training scenario. These results pave the way to further studies exploring how MWL modulation can be used to improve VR training applications.
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Dates and versions

hal-03007485 , version 1 (16-11-2020)

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Tiffany Luong, Ferran Argelaguet Sanz, Nicolas Martin, Anatole Lécuyer. Introducing Mental Workload Assessment for the Design of Virtual Reality Training Scenarios. VR 2020 - IEEE Conference on Virtual Reality and 3D User Interfaces, Mar 2020, Virtual, United States. pp.662-671, ⟨10.1109/VR46266.2020.00089⟩. ⟨hal-03007485⟩
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