HCMG: Human-Capacitance based Micro Gesture for VR/AR

Hand-tracking technology is a pivotal input method in augmented and virtual reality environments, providing enhanced interaction accuracy through micro-gesture recognition. This allows users to control devices with minimal knuckle movements, ensuring privacy and accessibility for individuals with mobility impairments. Building on the foundation of human capacitance, this paper introduces a novel approach termed human capacitance-based micro gesture (HCMG) recognition. This system employs capacitive sensors integrated within the inner lining of a wrist guard, capable of detecting subtle changes in skin-to-electrode contact caused by finger joint movements. Our approach leverages the inherent properties of human capacitance to facilitate accurate and efficient micro-gesture recognition. HCMG achieves recognition of five common micro gestures with an accuracy of 95.0%, providing a promising solution to address the limitations of existing techniques.

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Publication Details

Published
2024-09-22
DOI
https://doi.org/10.1145/3675094.3678386
Citations
4
Primary Topic
Tactile and Sensory Interactions
Type
article
Field-Weighted Citation Impact
1.37
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article

HCMG: Human-Capacitance based Micro Gesture for VR/AR

Yu Lu, Dian Ding, Guangtao Xue, Ran Wang
4 citations
Tactile and Sensory Interactions
1.37
article

HCMG: Human-Capacitance based Micro Gesture for VR/AR

Yu Lu, Dian Ding, Guangtao Xue, Ran Wang
article en
4 citations

Abstract

Hand-tracking technology is a pivotal input method in augmented and virtual reality environments, providing enhanced interaction accuracy through micro-gesture recognition. This allows users to control devices with minimal knuckle movements, ensuring privacy and accessibility for individuals with mobility impairments. Building on the foundation of human capacitance, this paper introduces a novel approach termed human capacitance-based micro gesture (HCMG) recognition. This system employs capacitive sensors integrated within the inner lining of a wrist guard, capable of detecting subtle changes in skin-to-electrode contact caused by finger joint movements. Our approach leverages the inherent properties of human capacitance to facilitate accurate and efficient micro-gesture recognition. HCMG achieves recognition of five common micro gestures with an accuracy of 95.0%, providing a promising solution to address the limitations of existing techniques.

Shanghai Jiao Tong University (CN)
Affordable and clean energy
Openalex Percentile: Top 20%
Tactile and Sensory Interactions
1.37
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HCMG: Human-Capacitance based Micro Gesture for VR/AR — Yu Lu, Dian Ding, et al. · (2024) | TGRS Research Map | TGRS