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A Predictive Performance Model for Immersive Interactions in Mixed Reality


The design of immersive interaction for mixed reality based on headmounted displays (HMDs), hereafter referred to as Mixed Reality (MR), is still a tedious task which can hinder the advent of such devices. Indeed, the effects of the interface design on task performance are difficult to anticipate during the design phase: the spatial layout of virtual objects and the interaction techniques used to select those objects can have an impact on task completion time. Besides, testing such interfaces with users in controlled experiments requires considerable time and efforts. To overcome this problem, predictive models, such as the Keystroke-Level Model (KLM), can be used to predict the time required to complete an interactive task at an early stage of the design process. However, so far these models have not been properly extended to address the specific interaction techniques of MR environments. In this paper we propose an extension of the KLM model to interaction performed in MR. First, we propose new operators and experimentally determine the unit times for each of them with a HoloLens v1. Then, we perform experiments based on realistic interaction scenarios to consolidate our model. These experiments confirm the validity of our extension of KLM to predict interaction time in mixed reality environments.
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hal-03382871 , version 1 (18-10-2021)



Florent Cabric, Emmanuel Dubois, Marcos Serrano. A Predictive Performance Model for Immersive Interactions in Mixed Reality. IEEE International Symposium on Mixed and Augmented Reality (ISMAR 2021), IEEE Computer Society; IEEE VGTC; ACM SIGGRAPH, Oct 2021, Bari, Italy. ⟨10.1109/ISMAR52148.2021.00035⟩. ⟨hal-03382871⟩
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