A model for estimating tactile sensation by machine learning based on vibration information obtained while touching an object

Fumiya Ito, Kenjiro Takemura

研究成果: Article査読

抄録

The tactile sensation is an important indicator of the added value of a product, and it is thus important to be able to evaluate this sensation quantitatively. Sensory evaluation is generally used to quantitatively evaluate the tactile sensation of an object. However, statistical evaluation of the tactile sensation requires many participants and is, thus, time-consuming and costly. Therefore, tactile sensing technology, as opposed to sensory evaluation, is attracting attention. In establishing tactile sensing technology, it is necessary to estimate the tactile sensation of an object from information obtained by a tactile sensor. In this research, we developed a tactile sensor made of two-layer silicone rubber with two strain gauges in each layer and obtained vibration information as the sensor traced an object. We then extracted features from the vibration information using deep autoencoders, following the nature of feature extraction by neural firing due to vibrations perceived within human fingers. We also conducted sensory evaluation to obtain tactile scores for different words from participants. We finally developed a tactile sensation estimation model for each of the seven samples and evaluated the accuracy of estimating the tactile sensation of unknown samples. We demonstrated that the developed model can properly estimate the tactile sensation for at least four of the seven samples.

本文言語English
論文番号7772
ジャーナルSensors
21
23
DOI
出版ステータスPublished - 2021 12月 1

ASJC Scopus subject areas

  • 分析化学
  • 情報システム
  • 原子分子物理学および光学
  • 生化学
  • 器械工学
  • 電子工学および電気工学

フィンガープリント

「A model for estimating tactile sensation by machine learning based on vibration information obtained while touching an object」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル