  This paper contributes towards the development of motion tracking algorithms
for time-critical applications, proposing an infrastructure for solving
dynamically the inverse kinematics of highly articulate systems such as humans.
We present a method based on the integration of differential kinematics using
distance measurement on SO(3) for which the convergence is proved using
Lyapunov analysis. An experimental scenario, where the motion of a human
subject is tracked in static and dynamic configurations, is used to validate
the inverse kinematics method performance on human and humanoid models.
Moreover, the method is tested on a human-humanoid retargeting scenario,
verifying the usability of the computed solution for real-time robotics
applications. Our approach is evaluated both in terms of accuracy and
computational load, and compared to iterative optimization algorithms.
