kalman filter core (xkfcore) sensor fusion algorithm  (Xsens North America Inc)

Journal: Communications Engineering

Article Title: A multi-method framework for establishing an angular acceleration reference in sensor calibration and uncertainty quantification

doi: 10.1038/s44172-025-00384-8

Figure Lengend Snippet: Comparison of error measures

Article Snippet: For the comparison with respect to the reference angular output \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varphi (t;{{\boldsymbol{\mathfrak{P}}}}^{* })$$\end{document} φ ( t ; P * ) , the data obtained via the sensor fusion algorithm (XKF3) from the single IMUs was equally good, and the residuals of the IMC were even better, again by a factor 2, compared to the post-processed and filtered MOCAP angular predictions.

Techniques: Comparison

Journal: Data in Brief

Article Title: Gait data from 51 healthy participants with motion capture, inertial measurement units, and computer vision

doi: 10.1016/j.dib.2024.110841

Figure Lengend Snippet:

Article Snippet: The IMUs estimated their orientations from accelerometer, gyroscope, and magnetometer data using the proprietary Xsens Kalman filter sensor fusion algorithm.

Techniques: Marker, Generated, Software, Magnetic Resonance Imaging