Hierarchical information fusion for human upper limb motion capture

Motion capture serves as a key technology in a wide spectrum of applications, including interactive game and learning, animation, film special effects, health-care and navigation. The existing human motion capture techniques, which use structured multiple high resolution cameras in the dedicated studio, are complicated and expensive. As rapid development of micro inertial sensors-on-chip, ubiquitous, real-time, and low cost human motion capture system using micro-inertial-sensors (MMocap) becomes possible. This paper presents a novel motion estimation algorithm by hierarchical fusion of sensor data and constraints of human dynamic model for human upper limb motion capture. Our method represents orientations of upper limb segments in quaternion, which is computationally effective and able to avoid singularity problem. To address the nonlinear human body segment motion, a particle filter is proposed to fuse 3D accelerometer and 3D micro-gyroscope sensor data to estimate upper limb motion recursively. Drift is the most challenging issue in motion estimation using inertial sensors. We present a novel solution by modeling the geometrical constraints in elbow joint and fuse these constraints to the particle filter process to compensate drift and improve the estimation accuracy. The experimental results have shown the feasibility and effectiveness of the proposed motion capture and analysis algorithm.