logo
ResearchBunny Logo
Frequency-encoded eye tracking smart contact lens for human-machine interaction

Engineering and Technology

Frequency-encoded eye tracking smart contact lens for human-machine interaction

H. Zhu, H. Yang, et al.

Discover a groundbreaking smart contact lens designed for seamless eye tracking and wireless interaction, developed by a team of researchers from Nanjing University and Nanjing Medical University. This innovative lens utilizes frequency encoding for precise eye movement detection, enabling applications ranging from gaming to robot control, all while ensuring biocompatibility.

00:00
00:00
Playback language: English
Abstract
This paper proposes a miniature, imperceptible, and biocompatible smart contact lens for in situ eye tracking and wireless eye-machine interaction. Employing frequency encoding, the chip-free and battery-free lens detects eye movement and closure. A time-sequential eye tracking algorithm achieves high angular accuracy (<0.5°). Applications demonstrated include eye-drawing, gaming, web interaction, camera control, and robot vehicle control. Biocompatibility tests show low cytotoxicity and irritation.
Publisher
Nature Communications
Published On
Apr 27, 2024
Authors
Hengtian Zhu, Huan Yang, Siqi Xu, Yuanyuan Ma, Shugeng Zhu, Zhengyi Mao, Weiwei Chen, Zizhong Hu, Rongrong Pan, Yurui Xu, Yifeng Xiong, Ye Chen, Yanqing Lu, Xinghai Ning, Dechen Jiang, Songtao Yuan, Fei Xu
Tags
smart contact lens
eye tracking
biocompatibility
frequency encoding
wireless interaction
angular accuracy
cytotoxicity

Related Publications

Explore these studies to deepen your understanding of the subject.

A skin-conformal and breathable humidity sensor for emotional mode recognition and non-contact human-machine interface
Engineering and Technology

A skin-conformal and breathable humidity sensor for emotional mode recognition and non-contact human-machine interface

T. Li, T. Zhao, et al.

The concept of "interaction" in debates on human-machine interaction
Interdisciplinary Studies

The concept of "interaction" in debates on human-machine interaction

S. Schleidgen, O. Friedrich, et al.

Detection of eye contact with deep neural networks is as accurate as human experts
Computer Science

Detection of eye contact with deep neural networks is as accurate as human experts

E. Chong, E. Clark-whitney, et al.

Tracking and tracing water consumption for informed water sensitive intervention through machine learning approach
Environmental Studies and Forestry

Tracking and tracing water consumption for informed water sensitive intervention through machine learning approach

A. T. Abraha, T. A. Woldeamanuel, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny