Robust pose estimation algorithm for wrist motion tracking

F. Cordella, F. Di Corato, G. Loianno, B. Siciliano, L. Zollo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The wrist plays a fundamental role in reaching and grasping actions, i.e. it guides the hand to the grasp position and adjusts its orientation on the basis of the grasping type and task. This paper proposes a novel, low-cost method for wrist pose estimation by using the Asus Xtion Pro Live motion sensing device and a robust marker-based tracking approach based on Unscented Kalman Filter (UKF). The hand palm kinematic model is also considered. The applicability of the approach to evaluate some interesting kinematics parameters, such as position, orientation, Range Of Motion, angular and linear velocity and trajectory has been proved. In particular, since the nature of the paper is to present a novel approach for wrist pose estimation, only initial validation for wrist kinematic measurement will be reported.

Original languageEnglish (US)
Title of host publicationIROS 2013
Subtitle of host publicationNew Horizon, Conference Digest - 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
Pages3746-3751
Number of pages6
DOIs
StatePublished - 2013
Event2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013 - Tokyo, Japan
Duration: Nov 3 2013Nov 8 2013

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Other

Other2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013
CountryJapan
CityTokyo
Period11/3/1311/8/13

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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