A highly sensitive, self-powered triboelectric auditory sensor for social robotics and hearing aids

By Hengyu Guo, Xianjie Pu, Jie Chen, Y. Meng, Min-Hsin Yeh, Guanlin Liu, Qian Tang, Baodong Chen, Di Liu, Song Qi and 4 more. Science Robotics, 2018

692 citations recorded by the source corpus.

A self-powered triboelectric auditory sensor is designed for human-robot interactions. The auditory system is the most efficient and straightforward communication strategy for connecting human beings and robots. Here, we designed a self-powered triboelectric auditory sensor (TAS) for constructing an electronic auditory system and an architecture for an external hearing aid in intelligent robotic applications. Based on newly developed triboelectric nanogenerator (TENG) technology, the TAS showed ultrahigh sensitivity (110 millivolts/decibel). A TAS with the broadband response from 100 to 5000 hertz was achieved by designing the annular or sectorial inner boundary architecture with systematic optimization. When incorporated with intelligent robotic devices, TAS demonstrated high-quality music recording and accurate voice recognition for realizing intelligent human-robot interaction. Furthermore, the tunable resonant frequency of TAS was achieved by adjusting the geometric design of inner boundary architecture, which could be used to amplify a specific sound wave naturally. On the basis of this unique property, we propose a hearing aid with the TENG technique, which can simplify the signal processing circuit and reduce the power consuming. This work expresses notable advantages of using TENG technology to build a new generation of auditory systems for meeting the challenges in social robotics.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Polymers and Plastics

View Paper: https://www.semanticscholar.org/paper/2bb86b3f2eaea4a4d6b45e19f943118bbc08752a

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

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 Polymers and Plastics

  1. Chengkuo Lee
  2. Xinge Guo
  3. Long Liu
  4. Tianyiyi He
  5. Qiongfeng Shi
  6. Haixia Zhang
  7. Feng Wen
  8. Philippe Vachon

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