Adaptique: Multi-objective and Context-aware Online Adaptation of Selection Techniques in Virtual Reality

TL;DR

Adaptique is an adaptive system for virtual reality that intelligently switches between selection techniques based on context and user needs, improving performance and user preference.

Contribution

It introduces a novel model that dynamically selects optimal selection techniques in VR considering context and multiple objectives, enhancing usability and efficiency.

Findings

Adaptique outperforms single techniques in user preference and performance.

The system effectively balances speed, accuracy, comfort, and familiarity.

Demonstrated versatility in a practical VR application.

Abstract

Selection is a fundamental task that is challenging in virtual reality due to issues such as distant and small targets, occlusion, and target-dense environments. Previous research has tackled these challenges through various selection techniques, but complicates selection and can be seen as tedious outside of their designed use case. We present Adaptique, an adaptive model that infers and switches to the most optimal selection technique based on user and environmental information. Adaptique considers contextual information such as target size, distance, occlusion, and user posture combined with four objectives: speed, accuracy, comfort, and familiarity which are based on fundamental predictive models of human movement for technique selection. This enables Adaptique to select simple techniques when they are sufficiently efficient and more advanced techniques when necessary. We show that Adaptique is more preferred and performant than single techniques in a user study, and demonstrate Adaptique's versatility in an application.