Utilizing Steering Entropy as a Measure of Performance in Head-mounted Augmented Reality

The next phase of augmented reality (AR) technologies suggest that as both the hardware and software continue to improve, we can expect that AR will become more commonly used as a tool for a variety of applications in complex operational contexts (i.e. training, manufacturing, mission planning). As new applications are designed and developed within these contexts, there is a necessity to be able to measure the effectiveness of these systems and to understand their impact on human performance and workload, so that only the most appropriate designs are selected for use, growing the technology in usefulness, not novel hindrances. A unique opportunity presented by the Microsoft HoloLens platform, as an example of head-worn AR systems, is the ability to collect positional and movement data, which lends itself to the computation of behavioral (or steering) entropy data, which can be related to human workload and performance within the system environment. However, little reference exists to be able to verify the accuracy of tracking of the device with regards to the output data available for collection. Within this practitioner-oriented paper, we extend current entropy measurement theory typically used in control settings within a heads-up display type ‘controls' environment. Our findings indicate that in-situ measurements of entropy utilizing the onboard sensors within the AR platform are more accurate to those collected within a Motion Capture Facility. Extending this work, these measurements can be used as a correlate of performance.