# Model-Based Real-Time Motion Tracking using Dynamical Inverse Kinematics on SO(3)

arXiv: https://arxiv.org/abs/1909.07669 · HTML: https://mlsanity.com/paper/1909.07669/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [Online Action Recognition for Human Risk Prediction with Anticipated Haptic Alert via Wearables](https://mlsanity.com/paper/2401.05365/) (2023)

## Abstract (arXiv)

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.