Citation: Moon, J.; Jeong, M.; Oh, S.;
Laine, T.H.; Seo, J. Data Collection
Framework for Context-Aware Virtual
Reality Application Development in
Unity: Case of Avatar Embodiment.
Sensors 2022, 22, 4623. https://
doi.org/10.3390/s22124623
Academic Editors: Zhihan Lv, Kai Xu
and Zhigeng Pan
Received: 9 May 2022
Accepted: 13 June 2022
Published: 19 June 2022
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Article
Data Collection Framework for Context-Aware Virtual Reality
Application Development in Unity: Case of Avatar Embodiment
Jiyoung Moon
1
, Minho Jeong
1
, Sangmin Oh
1
, Teemu H. Laine
1,
* and Jungryul Seo
2
1
Department of Digital Media, Ajou University, Suwon 16499, Korea; jiyoungmoon55@gmail.com (J.M.);
minho6428@naver.com (M.J.); ohsm1226@naver.com (S.O.)
2
Smart Mobility Lab, B2B Advanced Technology Center, LG Electronics, Seoul 07796, Korea; jrseojr@naver.com
* Correspondence: teemu@ubilife.net; Tel.: +82-31-2191851
Abstract:
Virtual Reality (VR) has been adopted as a leading technology for the metaverse, yet most
previous VR systems provide one-size-fits-all experiences to users. Context-awareness in VR enables
personalized experiences in the metaverse, such as improved embodiment and deeper integration
of the real world and virtual worlds. Personalization requires context data from diverse sources.
We proposed a reusable and extensible context data collection framework, ManySense VR, which
unifies data collection from diverse sources for VR applications. ManySense VR was implemented in
Unity based on extensible context data managers collecting data from data sources such as an eye
tracker, electroencephalogram, pulse, respiration, galvanic skin response, facial tracker, and Open
Weather Map. We used ManySense VR to build a context-aware embodiment VR scene where the
user’s avatar is synchronized with their bodily actions. The performance evaluation of ManySense
VR showed good performance in processor usage, frame rate, and memory footprint. Additionally,
we conducted a qualitative formative evaluation by interviewing five developers (two males and
three females; mean age: 22) after they used and extended ManySense VR. The participants expressed
advantages (e.g., ease-of-use, learnability, familiarity, quickness, and extensibility), disadvantages
(e.g., inconvenient/error-prone data query method and lack of diversity in callback methods), future
application ideas, and improvement suggestions that indicate potential and can guide future devel-
opment. In conclusion, ManySense VR is an efficient tool for researchers and developers to easily
integrate context data into their Unity-based VR applications for the metaverse.
Keywords:
Virtual Reality; Unity; context data collection; framework; embodiment; context-awareness;
metaverse; formative evaluation
1. Introduction
As global interest toward Virtual Reality (VR) is increasing, there have been attempts
to utilize VR in various fields such as second chance tourism [
1
], mental health therapy [
2
],
and education [
3
]. The recent proliferation of VR applications and services can be partly
explained by affordable head-mounted displays (HMDs), such as Meta Quest 2 and HTC
VIVE, that are capable of delivering highly immersive interaction experiences to users.
At the same time, popular game development platforms, such as Unreal Engine and
Unity, have contributed to the growing popularity of VR technology through provision of
powerful development tools for creating VR applications.
VR experiences are traditionally built upon VR equipment comprising a HMD and
hand-worn motion-tracking controllers that enable the user to be visually and aurally
immersed in a virtual environment. However, a shortcoming of this approach is that
it typically provides one-size-fits-all experiences to users with little consideration for
their diverse contexts. Thus, context-aware VR—which reaches beyond the basic sensing
capabilities of VR hardware—has emerged as an interesting research thread that attempts
to bring the affordances of context-aware computing (e.g., high personalization) into virtual
Sensors 2022, 22, 4623. https://doi.org/10.3390/s22124623 https://www.mdpi.com/journal/sensors
