Force-Aware Interface via Electromyography for Natural VR/AR Interaction Article Swipe
Yunxiang Zhang
,
Benjamin Liang
,
Boyuan Chen
,
Paul M. Torrens
,
S. Farokh Atashzar
,
Dahua Lin
,
Qi Sun
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1145/3550454.3555461
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1145/3550454.3555461
While tremendous advances in visual and auditory realism have been made for virtual and augmented reality (VR/AR), introducing a plausible sense of physicality into the virtual world remains challenging. Closing the gap between real-world physicality and immersive virtual experience requires a closed interaction loop: applying user-exerted physical forces to the virtual environment and generating haptic sensations back to the users. However, existing VR/AR solutions either completely ignore the force inputs from the users or rely on obtrusive sensing devices that compromise user experience. By identifying users' muscle activation patterns while engaging in VR/AR, we design a learning-based neural interface for natural and intuitive force inputs. Specifically, we show that lightweight electromyography sensors, resting non-invasively on users' forearm skin, inform and establish a robust understanding of their complex hand activities. Fuelled by a neural-network-based model, our interface can decode finger-wise forces in real-time with 3.3% mean error, and generalize to new users with little calibration. Through an interactive psychophysical study, we show that human perception of virtual objects' physical properties, such as stiffness, can be significantly enhanced by our interface. We further demonstrate that our interface enables ubiquitous control via finger tapping. Ultimately, we envision our findings to push forward research towards more realistic physicality in future VR/AR.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3550454.3555461
- OA Status
- green
- Cited By
- 25
- References
- 92
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4302305752Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3550454.3555461Digital Object Identifier
- Title
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Force-Aware Interface via Electromyography for Natural VR/AR InteractionWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-11-30Full publication date if available
- Authors
-
Yunxiang Zhang, Benjamin Liang, Boyuan Chen, Paul M. Torrens, S. Farokh Atashzar, Dahua Lin, Qi SunList of authors in order
- Landing page
-
https://doi.org/10.1145/3550454.3555461Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2210.01225Direct OA link when available
- Concepts
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Computer science, Human–computer interaction, Virtual reality, Interface (matter), Haptic technology, Perception, Closing (real estate), Artificial intelligence, Law, Bubble, Biology, Neuroscience, Parallel computing, Political science, Maximum bubble pressure methodTop concepts (fields/topics) attached by OpenAlex
- Cited by
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25Total citation count in OpenAlex
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2025: 9, 2024: 12, 2023: 4Per-year citation counts (last 5 years)
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92Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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