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Ubiquitous Human Upper-Limb Motion Estimation using Wearable Sensors

Title
Ubiquitous Human Upper-Limb Motion Estimation using Wearable Sensors
Author
Zhiqiang ZHANGZhiqiang ZHANGZhiqiang ZHANGZhiqiang ZHANG
Keywords
Joints, Humans; Elbow; Mathematical model; Kalman filters; Kinematics; Estimation
Issue Date
2011-08
Publisher
IEEE
Citation
IEEE Journal of Biomedical and Health Informatics, 2011, 15(4), P.513-521
Abstract
Human motion capture technologies have been widely used in a wide spectrum of applications, including interactive game and learning, animation, film special effects, health care, navigation, and so on. The existing human motion capture techniques, which use structured multiple high-resolution cameras in a dedicated studio, are complicated and expensive. With the rapid development of microsensors-on-chip, human motion capture using wearable microsensors has become an active research topic. Because of the agility in movement, upper-limb motion estimation has been regarded as the most difficult problem in human motion capture. In this paper, we take the upper limb as our research subject and propose a novel ubiquitous upper-limb motion estimation algorithm, which concentrates on modeling the relationship between upper-arm movement and forearm movement. A link structure with 5 degrees of freedom (DOF) is proposed to model the human upper-limb skeleton structure. Parameters are defined according to Denavit-Hartenberg convention, forward kinematics equations are derived, and an unscented Kalman filter is deployed to estimate the defined parameters. The experimental results have shown that the proposed upper-limb motion capture and analysis algorithm outperforms other fusion methods and provides accurate results in comparison to the BTS optical motion tracker.
URI
http://ieeexplore.ieee.org/document/5872045/
ISSN
1089-7771; 1558-0032
DOI
10.1109/TITB.2011.2159122
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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