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Intelligent wearable olfactory interface for latency-free mixed reality and fast olfactory enhancement.

Authors :
Liu, Yiming
Jia, Shengxin
Yiu, Chun Ki
Park, Wooyoung
Chen, Zhenlin
Nan, Jin
Huang, Xingcan
Chen, Hongting
Li, Wenyang
Gao, Yuyu
Song, Weike
Yokota, Tomoyuki
Someya, Takao
Zhao, Zhao
Li, Yuhang
Yu, Xinge
Source :
Nature Communications; 5/25/2024, p1-15, 15p
Publication Year :
2024

Abstract

Olfaction feedback systems could be utilized to stimulate human emotion, increase alertness, provide clinical therapy, and establish immersive virtual environments. Currently, the reported olfaction feedback technologies still face a host of formidable challenges, including human perceivable delay in odor manipulation, unwieldy dimensions, and limited number of odor supplies. Herein, we report a general strategy to solve these problems, which associates with a wearable, high-performance olfactory interface based on miniaturized odor generators (OGs) with advanced artificial intelligence (AI) algorithms. The OGs serve as the core technology of the intelligent olfactory interface, which exhibit milestone advances in millisecond-level response time, milliwatt-scale power consumption, and the miniaturized size. Empowered by robust AI algorithms, the olfactory interface shows its great potentials in latency-free mixed reality (MR) and fast olfaction enhancement, thereby establishing a bridge between electronics and users for broad applications ranging from entertainment, to education, to medical treatment, and to human machine interfaces.Liu et al. developed AI-driven, wearable olfactory interfaces with odor generators for realizing latency-free mixed-reality and fast olfaction recovery. This technology enables personalized olfactory feedback and enhanced virtual environments for various applications including education, and clinical treatment. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20411723
Database :
Complementary Index
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
177552057
Full Text :
https://doi.org/10.1038/s41467-024-48884-z