Force skill training with a hybrid trainer model

Hasan Esen, Ke N.Ichi Yano, Martin Buss

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

In this work, we present novel VR training strategies that incorporate a hybrid trainer model to train force. For modeling the trainer skill, weighted K-means algorithm in parameter space with LS optimization is implemented. The efficiency of the training strategies is verified via user tests in frame of a bone drilling training application. An objective evaluation method based on n dimensional Euclidean distances is introduced to assess user tests results. It is shown that the proposed strategies improve the student skill and accelerate force learning.

Original languageEnglish
Title of host publicationProceedings of the 17th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN
Pages9-14
Number of pages6
DOIs
StatePublished - 2008
Event17th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN - Munich, Germany
Duration: 1 Aug 20083 Aug 2008

Publication series

NameProceedings of the 17th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN

Conference

Conference17th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN
Country/TerritoryGermany
CityMunich
Period1/08/083/08/08

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