Search arXivSearch

arXiv · 2306.14210

Towards Conducting Effective Locomotion Through Hardware Transformation in Head-Mounted-Device -- A Review Study

Abstract

Immersiveness is the main characteristic of Virtual Reality(VR) applications. Precise integration between hardware design and software are necessary for providing a seamless virtual experience. Allowing the user to navigate the VR scene using locomotion techniques is crucial for making such experiences `immersive'. Locomotion in VR acts as a motion tracking unit for the user and simulates their movement in the virtual scene. These movements are commonly rotational, axial or translational based on the Degree-of-Freedom (DOF) of the application. To support effective locomotion, one of the primary challenges for VR practitioners is to transform their hardware from 3-DOF to 6-DOF or vice versa. We conducted a systematic review on different motion tracking methods employed in the Head-Mounted-Devices (HMD) to understand such hardware transformation. Our review discusses the fundamental aspects of the hardware-based transformation of HMDs to conduct virtual locomotion. Our observations led us to formulate a taxonomy of the tracking methods based on system design, which can eventually be used for the hardware transformation of HMDs. Our study also captures different metrics that VR practitioners use to evaluate the hardware based on the context, performance, and significance of its usage.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Y Pawan Kumar Gururaj, Raghav Mittal, Sai Anirudh Karre, Y. Raghu Reddy, Syed Azeemuddin. 2023-06-25. Towards Conducting Effective Locomotion Through Hardware Transformation in Head-Mounted-Device -- A Review Study. https://doi.org/10.1109/vrw55335.2022.00181

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Are explainable AI (XAI) evaluation strategies aligned? Comparing subjective, objective, and mathematical evaluation measures using saliency maps

The evaluation of explainable AI (XAI) approaches often relies on three families of methods: subjective measures (e.g., questionnaires on trust or satisfaction), objective measures (e.g., task performance metrics), and mathematical metrics (e.g., for faithfulness). Yet, it remains unclear how these families align or diverge in practice. In a{preregistered} between-subjects study (N=166), we use three established saliency map techniques (LIME, Grad-CAM, Guided Backpropagation) as a testbed to examine this issue. We find that each family of methods leads to different conclusions: participants reported no differences in trust or satisfaction, Grad-CAM improved user performance, while mathematical metrics favored Guided Backpropagation. At the same time, mathematical metrics were only partially related to user performance, and these relationships were sometimes counterintuitive. Our findings highlight the methodological importance of comparing subjective, objective, and mathematical approaches when evaluating XAI, illustrating both tensions and aspects that are aligned. We discuss implications for XAI evaluation frameworks.

cs.HC

Mind Your Ps and Qs: Positive Moderation Practice in the Positive Queue

Online communities rely on volunteer moderators to maintain order. Despite their key role, moderators are given a toolbox of punishments and far less support for encouraging contributions they want to see more of. We introduce the Positive Queue as a positive counterpart to Reddit's modqueue: a dedicated space for moderators to discover contributions and behaviors they want to encourage and positively reinforce. With five moderators, four with 6-14 years of experience, we use the Positive Queue to examine how moderators operationalize positive reinforcement. Moderators combined predicted community reception, observed engagement, and their own judgment; used prediction-engagement mismatches to identify overlooked content; and repurposed positive features for punitive and retrospective work. These findings surface tensions around labor, attribution, and community fit. We contribute the Positive Queue as a working system and conceptualize positive moderation as recognition infrastructure that shapes what moderators notice, whose judgment becomes visible, and how recognition reaches contributors.

cs.HC

PILOT: Control Surfaces for Authoring Social Media Feeds

Personalized social media feeds infer preferences from behavior, leaving people little direct control over what they see. Existing controls range from post-level reactions to rules and natural language, but little is known about how people use them together or how added expressiveness changes effort. We built PILOT, a Bluesky feed-authoring system that turns in-feed actions into explicit preferences, deterministic ranking, and inspectable outcomes. A study with seven participants informed an expanded implementation, which we organized into three nested control surfaces that ten participants compared within subjects. Participants assigned controls to distinct jobs: broad controls set direction, in-post actions refined results, and filters removed content. Richer surfaces did not necessarily feel more effortful, and participants' experiences depended more on whether they could verify and repair outcomes. Our findings show that usable feed control requires not simply more controls, but orchestration across mechanisms that support expression, inspection, repair, and episodic use.

cs.HC