Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhiqiang ZHANG | - |
dc.contributor.author | Zhiqiang ZHANG | - |
dc.contributor.author | Zhiqiang ZHANG | - |
dc.contributor.author | Zhiqiang ZHANG | - |
dc.date.accessioned | 2018-03-08T01:38:08Z | - |
dc.date.available | 2018-03-08T01:38:08Z | - |
dc.date.issued | 2011-08 | - |
dc.identifier.citation | IEEE Journal of Biomedical and Health Informatics, 2011, 15(4), P.513-521 | en_US |
dc.identifier.issn | 1089-7771 | - |
dc.identifier.issn | 1558-0032 | - |
dc.identifier.uri | http://ieeexplore.ieee.org/document/5872045/ | - |
dc.description.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. | en_US |
dc.description.sponsorship | Manuscript received August 21, 2010; revised February 23, 2011; accepted May 31, 2011. Date of publication June 9, 2011; date of current version July 15, 2011. This work was supported by the China-Singapore Institute of Digital Media under Project CSIDM-200802, and in part by the National Research Foundation administered by the Media Development Authority of Singapore. It was also supported by the National Natural Science Foundation of China under Grant 60932001. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.subject | Joints, Humans | en_US |
dc.subject | Elbow | en_US |
dc.subject | Mathematical model | en_US |
dc.subject | Kalman filters | en_US |
dc.subject | Kinematics | en_US |
dc.subject | Estimation | en_US |
dc.title | Ubiquitous Human Upper-Limb Motion Estimation using Wearable Sensors | en_US |
dc.type | Article | en_US |
dc.relation.no | 4 | - |
dc.relation.volume | 15 | - |
dc.identifier.doi | 10.1109/TITB.2011.2159122 | - |
dc.relation.page | 513-521 | - |
dc.relation.journal | IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE | - |
dc.contributor.googleauthor | Zhi-Qiang Zhang | - |
dc.contributor.googleauthor | Wai-Choong Wong | - |
dc.contributor.googleauthor | Jian-Kang Wu | - |
dc.relation.code | 2011214109 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF COMPUTER SCIENCE | - |
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