A robust vision-based technique for human arm kinematics identification

Omid Talakoub; Farrokh Janabi Sharifi

doi:10.1117/12.686229

12 October 2006 A robust vision-based technique for human arm kinematics identification

Proceedings Volume 6374, Optomechatronic Actuators, Manipulation, and Systems Control; 63740Q (2006) https://doi.org/10.1117/12.686229
Event: Optics East 2006, 2006, Boston, Massachusetts, United States

Abstract

Motion, independent of forces, is described by kinematic parameters (usually using Denavit-Hartenberg convention) that have been used widely in biomechanical fields. Examples of these fields include robotics, human motion studies, and biomechanical structures' design and control; e. g. for exoskeleton and artificial human arm. A common way to precisely measure the joints movements is by using a motion capture system. Until now, the most successful motion capture technology is optical motion capture; this is due to its highly accurate measurement of small reflective markers that are attached to some relevant body landmarks. This paper addresses the problem of estimating the human arm kinematics parameters from video or captured images of human arms. We introduce a new robust framework that leads to reliable and accurate estimation of shoulder and elbow center of rotations along with the arm kinematics parameters.

Citation Download Citation

Omid Talakoub and Farrokh Janabi Sharifi "A robust vision-based technique for human arm kinematics identification", Proc. SPIE 6374, Optomechatronic Actuators, Manipulation, and Systems Control, 63740Q (12 October 2006); https://doi.org/10.1117/12.686229

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available