Paper Info
Title
3D Upper Limb Motion Modeling and Estimation Using Wearable Micro-sensors
Abstract
Human motion capture technologies are widely used in interactive game and learning, animation, film special effects, health-care and navigation. Because of the agility, upper limb motion estimation is the most difficult in human motion capture. Traditional methods always assume that the movements of upper arm and forearm are independent and estimate their movements separately; therefore, the estimated motion are always with serious distortion. In the paper, we proposed a novel ubiquitous upper limb motion estimation method using wearable micro-sensors, which concentrated on modeling the relationship of the movements between upper arm and forearm. Exploration of the skeleton structure of upper limb as a link structure with 5 degrees of freedom was firstly proposed to model human upper limb motion. After that, parameters were defined according to Denavit-Hartenberg convention, forward kinematic equations of upper limb were derived, and an Unscented Kalman filter was invoked to estimate the defined parameters. The experimental results have shown the feasibility and effectiveness of the proposed upper limb motion capture and analysis algorithm.
Year
DOI
Venue
2010
10.1109/BSN.2010.14
BSN
Keywords
Field
DocType
upper limb,biomechanics,movements,biosensors,human motion capture technology,unscented kalman filter,wearable micro-sensors,upper limb motion estimation,ubiquitous upper limb motion estimation,kalman filters,forearm,3d upper limb motion modeling,biomedical measurement,proposed upper limb motion,microsensors,upper limb motion modeling,medical signal processing,3d upper limb motion estimation,body sensor networks,forward kinematic equation,motion estimation,estimated motion,wearable microsensors,ubiquitous motion modeling and estimation,ubiquitous computing,human upper limb motion,denavit-hartenberg convention,kinematics,novel ubiquitous upper limb,forward kinematic equations,upper arm,motion estimation method,human motion,animation,parameter estimation,navigation,motion capture,mathematical model,estimation,skeleton,degree of freedom,algorithm design and analysis,health care
Computer vision,Motion capture,Kinematics,Computer science,Kalman filter,Forearm,Artificial intelligence,Animation,Estimation theory,Motion analysis,Motion estimation
Conference
ISBN
Citations 
PageRank 
978-1-4244-5817-2
2
0.39
References 
Authors
11
3
Name
Order
Citations
PageRank
Zhiqiang Zhang1226.43
Lawrence W. C. Wong291.96
Jiankang Wu357679.80