Low-Cost Real-Time Mental Load Adaptation for Augmented Reality Instructions - A Feasibility Study

Abstract

Since the introduction of augmented reality (AR) technology, in-situ instructions for manual tasks have been a central use case for a large body of previous work. However, most implementations provide identical sets of instructions to each user disregarding the user’s current mental load. This is a major issue since previous work has shown the importance and potential of an adapted instruction fidelity for manual tasks such as playing an instrument. To implement a low-cost mental load adaptation for AR instructions, we evaluated a mobile off-the-shelf electroencephalographic (EEG) device for its suitability and feasibility to measure mental load while wearing a video see-through AR head-mounted display (HMD). In a first user experiment (n=12), data of EEG power band values and proprietary performance metrics of the manufacturer were collected and analysed regarding their validity to estimate the user’s mental load. Our results indicate that our setup successfully induced different levels of mental effort. The proprietary performance metrics, however, only partially reflected the participants’ current mental effort and require further analysis.

Tobias Wagner

PhD Student in HCI

My research interests include gaze-assisted systems for teaching and learning, eye-tracking, and gaze-based interaction