Noncontact 3D gesture recognition enabled VR human–machine interface via electret-nanofiber-based triboelectric sensor

Virtual reality (VR) is an emerging communication means and creates extensive opportunities in interacting scenarios such as remote collaboration and metaverse. Human-machine interfaces (HMIs) play important roles in VR as they provide interaction platforms between users and virtual environments. However, traditional VR HMIs based on handheld devices or keyboards cannot recognize diverse three-dimensional (3D) gestures, which results in limited freedom of VR interactions. Here, we report a noncontact VR HMI enabled by an electret-nanofiber-based triboelectric sensor (ETS), which is fabricated by the electrospun polylactic acid/thermoplastic polyurethane (PLA/TPU) electret nanofiber film. The nanofiber structure of PLA/TPU electret enhanced the charge retention ability of triboelectric sensor and thus significantly improved its signal strength and stability. Integrated with a deep learning-based multilayer perceptron neural network, the ETS realizes the recognition of 18 different types of 3D gestures with a high average accuracy of 97.3%. An intelligent noncontact VR interactive system based on the ETS is further developed, which is used to manipulate game characters for performing different actions by 3D gestures. Compared with traditional VR HMIs, the proposed VR HMI based on PLA/TPU electret nanofiber film can detect various 3D gestures and offers a superior interaction freedom. This work for the first time introduces the triboelectric 3D gesture recognition method to the VR HMIs, and could make the interaction between human and virtual environments become more efficient and fascinating.

Authors

Publication Details

Journal
Nano Research
Published
2025-08-20
DOI
https://doi.org/10.26599/nr.2025.94907924
Citations
30
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
58.09
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Noncontact 3D gesture recognition enabled VR human–machine interface via electret-nanofiber-based triboelectric sensor

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30 citations
Nano Research
Hand Gesture Recognition Systems
58.09
article

Noncontact 3D gesture recognition enabled VR human–machine interface via electret-nanofiber-based triboelectric sensor

Baojin Chen, Yan Zhu, Yanchao Mao, Chang Liu, Yujie Zhang, Lijun Lu, Jing Wu, Yun Yan Hu
article en
30 citations

Abstract

Virtual reality (VR) is an emerging communication means and creates extensive opportunities in interacting scenarios such as remote collaboration and metaverse. Human-machine interfaces (HMIs) play important roles in VR as they provide interaction platforms between users and virtual environments. However, traditional VR HMIs based on handheld devices or keyboards cannot recognize diverse three-dimensional (3D) gestures, which results in limited freedom of VR interactions. Here, we report a noncontact VR HMI enabled by an electret-nanofiber-based triboelectric sensor (ETS), which is fabricated by the electrospun polylactic acid/thermoplastic polyurethane (PLA/TPU) electret nanofiber film. The nanofiber structure of PLA/TPU electret enhanced the charge retention ability of triboelectric sensor and thus significantly improved its signal strength and stability. Integrated with a deep learning-based multilayer perceptron neural network, the ETS realizes the recognition of 18 different types of 3D gestures with a high average accuracy of 97.3%. An intelligent noncontact VR interactive system based on the ETS is further developed, which is used to manipulate game characters for performing different actions by 3D gestures. Compared with traditional VR HMIs, the proposed VR HMI based on PLA/TPU electret nanofiber film can detect various 3D gestures and offers a superior interaction freedom. This work for the first time introduces the triboelectric 3D gesture recognition method to the VR HMIs, and could make the interaction between human and virtual environments become more efficient and fascinating.

Nano ResearchVol. 18(11)
Affordable and clean energy
Openalex Percentile: Top 0%
Hand Gesture Recognition Systems
58.09
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