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Title: Ubiquitous human upper-limb motion estimation using wearable sensors
Authors: Zhang, Z.-Q.
Wong Sr., W.-C. 
Wu, J.-K.
Keywords: Body sensor network
forward kinematics
Kalman filter
ubiquitous motion modeling and estimation
Issue Date: Jul-2011
Citation: Zhang, Z.-Q., Wong Sr., W.-C., Wu, J.-K. (2011-07). Ubiquitous human upper-limb motion estimation using wearable sensors. IEEE Transactions on Information Technology in Biomedicine 15 (4) : 513-521. ScholarBank@NUS Repository.
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. © 2011 IEEE.
Source Title: IEEE Transactions on Information Technology in Biomedicine
ISSN: 10897771
DOI: 10.1109/TITB.2011.2159122
Appears in Collections:Staff Publications

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