Paper Info
Title
A data-driven kinematic model of the human hand with soft-tissue artifact compensation mechanism for grasp synergy analysis
Abstract
This paper presents a methodology to accurately record human finger postures during grasping. The main contribution consists of a kinematic model of the human hand reconstructed via magnetic resonance imaging of one subject that (i) is fully parameterized and can be adapted to different subjects, and (ii) is amenable to in-vivo joint angle recordings via optical tracking of markers attached to the skin. The principal novelty here is the introduction of a soft-tissue artifact compensation mechanism that can be optimally calibrated in a systematic way. The high-quality data gathered are employed to study the properties of hand postural synergies in humans, for the sake of ongoing neuroscience investigations. These data are analyzed and some comparisons with similar studies are reported. After a meaningful mapping strategy has been devised, these data could be employed to define robotic hand postures suitable to attain effective grasps, or could be used as prior knowledge in lower-dimensional, real-time avatar hand animation.
Year
DOI
Venue
2013
10.1109/IROS.2013.6696890
Intelligent Robots and Systems
Keywords
Field
DocType
biomechanics,kinematics,principal component analysis,avatar hand animation,data-driven kinematic model,grasp synergy analysis,hand postural synergy,human finger posture,human hand,joint angle recordings,magnetic resonance imaging,mapping strategy,optical tracking,soft-tissue artifact compensation mechanism
Computer vision,Data-driven,GRASP,Kinematics,Computer science,Optical tracking,Artificial intelligence,Animation,Novelty,Avatar,Principal component analysis
Conference
ISSN
Citations 
PageRank 
2153-0858
12
0.78
References 
Authors
6
4
Name
Order
Citations
PageRank
Marco Gabiccini112413.28
Georg Stillfried2293.19
Hamal Marino3313.72
Matteo Bianchi427647.56