面向机器识别-人类感知的联合振动触觉编码

房颖; 徐艺文; 赵铁松

doi:10.11959/j.issn.1000-436x.2023099

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面向机器识别-人类感知的联合振动触觉编码

专题：多/跨模态语义通信 | 更新时间：2024-06-06

- 面向机器识别-人类感知的联合振动触觉编码
- Joint vibrotactile coding for machine recognition and human perception
- 通信学报 2023年44卷第5期页码：42-51
- 作者机构：
  
  福州大学福建省媒体信息智能处理与无线传输重点实验室，福建福州 350001
- 作者简介：
  
  [ "房颖（1981- ），女，福建福州人，博士，福州大学讲师，主要研究方向为多媒体通信、计算机触觉" ]
  [ "徐艺文（1976- ），男，福建漳州人，博士，福州大学教授、硕士生导师，主要研究方向为媒体信息智能处理与传输" ]
  [ "赵铁松（1984- ），男，河北衡水人，博士，福州大学教授、博士生导师，主要研究方向为多媒体通信、计算机视觉、计算机触觉等" ]
- 基金信息：
  
  国家自然科学基金资助项目(62171134);福建省自然科学基金资助项目(2022J02015)
- DOI：10.11959/j.issn.1000-436x.2023099
  中图分类号： TP37
- 网络出版日期：2023-05，
  
  纸质出版日期：2023-05-25
- 稿件说明：
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房颖, 徐艺文, 赵铁松. 面向机器识别-人类感知的联合振动触觉编码[J]. 通信学报, 2023,44(5):42-51.

Ying FANG, Yiwen XU, Tiesong ZHAO. Joint vibrotactile coding for machine recognition and human perception[J]. Journal on communications, 2023, 44(5): 42-51.
房颖, 徐艺文, 赵铁松. 面向机器识别-人类感知的联合振动触觉编码[J]. 通信学报, 2023,44(5):42-51. DOI： 10.11959/j.issn.1000-436x.2023099.

Ying FANG, Yiwen XU, Tiesong ZHAO. Joint vibrotactile coding for machine recognition and human perception[J]. Journal on communications, 2023, 44(5): 42-51. DOI： 10.11959/j.issn.1000-436x.2023099.

摘要

为了精确地传输信号内容含义，实现智能识别与信号重建，针对振动触觉信号，提出了一种面向机器识别-人类感知的联合编码方案。在编码端，将三维振动信号转化为一维信号，采用短时傅里叶变换提取信号的语义信息，并实现语义信息高效压缩与表征。在解码端，基于语义信息采用全卷积神经网络实现触觉的智能识别；同时，将原始信号与基于语义信息的重构信号的残差值作为语义信息的补偿，逐步提高重构信号的质量，满足人类感知需求。实验结果表明，所提方案用较低比特率的语义信息实现触觉识别，同时在满足人类感知需求情况下，触觉数据的压缩效率有所提高。

Abstract

In order to accurately transmit the content meaning of vibrotactile signals and achieve intelligent recognition and signal reconstruction

a joint vibrotactile coding scheme for machine recognition and human perception was proposed.At the encoding end

the original three-dimensional vibrotactile signals were converted into one-dimensional signals.Then the semantic information of the signals was extracted using a short-time Fourier transform before being effectively compressed and transmitted.At the decoding end

a fully convolutional neural network was used to intelligently recognize based on the semantic information.The difference between the original signals and the reconstructed signals based on semantic information was used as compensation for the semantic information

and the quality of the reconstructed signals was gradually improved to meet human perceptual needs.The experimental results show that the proposed scheme achieve tactile recognition with semantic information at a lower bit rate while improving the compression efficiency of tactile data

thus satisfying human perceptual needs.

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Keywords

references

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