LoCoMoTe - A Framework for Classification of Natural Locomotion in VR by Task, Technique and Modality.

Area of Science:

• Human-Computer Interaction
• Virtual Reality (VR) Research
• Human Navigation and Behavior Modeling

Background:

• Virtual reality (VR) research extensively covers locomotion techniques, user experience, and machine learning for redirection algorithms.
• Understanding human navigation and behavior in VR is crucial for developing effective redirection algorithms.
• The growing volume of VR research makes it challenging to identify, select, and compare relevant studies without a structured framework.

Purpose of the Study:

• To facilitate the structuring and comparison of VR-based natural walking literature.
• To provide a standardized framework for researchers to utilize in organizing VR experimental methodologies.
• To aid in identifying research gaps and fostering discussions within the VR natural walking research field.

Main Methods:

• A thematic analysis was applied to the methodology descriptions of 140 VR-based research papers featuring live-user experiments.
• The analysis focused on understanding how experimental conditions were described and implemented.
• This led to the development of the LoCoMoTe framework.

Main Results:

• The LoCoMoTe framework was developed, comprising three core themes: navigational decisions, technique implementation, and modalities.
• This framework offers a standardized approach for structuring and comparing experimental conditions in VR natural walking studies.
• The framework categorizes and systematizes knowledge, aiding in the identification of research gaps.

Conclusions:

• The LoCoMoTe framework provides a valuable tool for organizing and comparing VR natural walking research.
• Continuous updating of the framework is recommended to maintain its relevance and comprehensiveness.
• It supports systematic knowledge accumulation and facilitates targeted future research in VR locomotion.