Full-body motion estimation of a human through wearable sensing technologies is challenging in the absence of position sensors. This paper contributes to the development of a model-based whole-body kinematics estimation algorithm using wearable distributed inertial and force-torque sensing. This is done by extending the existing dynamical optimization-based Inverse Kinematics (IK) approach for joint state estimation, in cascade, to include a center of pressure-based contact detector and a contact-aided Kalman filter on Lie groups for floating base pose estimation. The proposed method is tested in an experimental scenario where a human equipped with a sensorized suit and shoes performs walking motions. The proposed method is demonstrated to obtain a reliable reconstruction of the whole-body human motion.
@article{arxiv.2205.07835,
title = {Whole-Body Human Kinematics Estimation using Dynamical Inverse Kinematics and Contact-Aided Lie Group Kalman Filter},
author = {Prashanth Ramadoss and Lorenzo Rapetti and Yeshasvi Tirupachuri and Riccardo Grieco and Gianluca Milani and Enrico Valli and Stefano Dafarra and Silvio Traversaro and Daniele Pucci},
journal= {arXiv preprint arXiv:2205.07835},
year = {2022}
}