3D Upper Limb Motion Modeling and Estimation Using Wearable Micro-sensors

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.