Eye motion triggered self-powered mechnosensational communication system using triboelectric nanogenerator

By Xianjie Pu, Hengyu Guo, Jie Chen, Xue Wang, Y. Xi, Chenguo Hu, Zhong Lin Wang. Science Advances, 2017

554 citations recorded by the source corpus.

A triboelectric eye blinking sensor with robustly high sensitivity achieves prominence as a human-machine interface. Mechnosensational human-machine interfaces (HMIs) can greatly extend communication channels between human and external devices in a natural way. The mechnosensational HMIs based on biopotential signals have been developing slowly owing to the low signal-to-noise ratio and poor stability. In eye motions, the corneal-retinal potential caused by hyperpolarization and depolarization is very weak. However, the mechanical micromotion of the skin around the corners of eyes has never been considered as a good trigger signal source. We report a novel triboelectric nanogenerator (TENG)–based micromotion sensor enabled by the coupling of triboelectricity and electrostatic induction. By using an indium tin oxide electrode and two opposite tribomaterials, the proposed flexible and transparent sensor is capable of effectively capturing eye blink motion with a super-high signal level (~750 mV) compared with the traditional electrooculogram approach (~1 mV). The sensor is fixed on a pair of glasses and applied in two real-time mechnosensational HMIs—the smart home control system and the wireless hands-free typing system with advantages of super-high sensitivity, stability, easy operation, and low cost. This TENG-based micromotion sensor is distinct and unique in its fundamental mechanism, which provides a novel design concept for intelligent sensor technique and shows great potential application in mechnosensational HMIs.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/3a705a0d01e70edb20ac17990188f168c172584e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Chengkuo Lee
2. Xinge Guo
3. Long Liu
4. Tianyiyi He
5. Qiongfeng Shi
6. Feng Wen
7. Jianjun Luo
8. Bowei Dong

In Cognitive Neuroscience

1. Chengkuo Lee
2. Xinge Guo
3. Long Liu
4. Tianyiyi He
5. Feng Wen
6. Qiongfeng Shi
7. Jianxiong Zhu
8. Bowei Dong

In Human-Computer Interaction

1. Xinge Guo
2. Long Liu
3. Chengkuo Lee

Citations in the corpus

• Promoting smart cities into the 5G era with multi-field Internet of Things (IoT) applications powered with advanced mechanical energy harvesters (2021, methodology): “6(h), Pu et al. proposed an eye motion-triggered self-powered mechnosensational communication system using a TENG-based sensor [361].”