論文

査読有り
2017年10月

Fingertip-Based Feature Analysis for the Push and Stroke Manipulation of Elastic Objects

IEEE TRANSACTIONS ON HAPTICS
  • Megumi Nakao
  • ,
  • Masayuki Senoo
  • ,
  • Tetsuya Matsuda

10
4
開始ページ
523
終了ページ
532
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1109/TOH.2017.2720598
出版者・発行元
IEEE COMPUTER SOC

In this study, to quantitatively understand finger operations used to manipulate elastic objects, we explore robust fingertip-based feature descriptors that are invariant to operator, finger position, and target object. To measure the tactile information generated when an object is directly touched by a fingertip, we used a wearable system that enables the simultaneous measurement of fingertip position and strain without inhibiting the operator's sense of touch. This paper focuses on the quantitative classification of the push and stroke operations of a single finger, and conducted user experiments to obtain time-series fingertip position and strain from 10 subjects touching nine types of elastic objects. The recognition rate was investigated by binary classification using a support vector machine and cross validation. The results show that the two-dimensional features obtained from fingertip position and strain within a 0.9-s time frame can stably recognize push and stroke operations on elastic bodies of different shapes, stiffnesses, and thicknesses at a higher recognition rate.

リンク情報
DOI
https://doi.org/10.1109/TOH.2017.2720598
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000418416000007&DestApp=WOS_CPL
URL
http://orcid.org/0000-0002-2339-1521
ID情報
  • DOI : 10.1109/TOH.2017.2720598
  • ISSN : 1939-1412
  • eISSN : 2329-4051
  • Web of Science ID : WOS:000418416000007

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