Design, Implementation, and Experimental Results of a Quaternion-Based Kalman Filter for Human Body Motion Tracking

By X. Yun, E. Bachmann. IEEE Transactions on robotics, 2006

624 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Estimating Three-Dimensional Orientation of Human Body Parts by Inertial/Magnetic Sensing (2011, neutral): “, [20-25].”
Wearable soft sensing suit for human gait measurement (2014, background): “, 2003; Luinge and Veltink, 2005), but these sensors require extensive filtering (Yun and Bachmann, 2006) or sensor fusion with external systems (Tao et al.”
Complementary Observer for Body Segments Motion Capturing by Inertial and Magnetic Sensors (2014, positive): “Consequently, human motion estimation can be tracked outdoor using smaller ambulatory measurement systems.”
Quaternion Convolutional Neural Networks (2018, neutral): “, quaternion Fourier transform (QFT) [29], quaternion wavelet transform (QWT) [1,35], and quaternion Kalman filter [2,39].”
Stronger, Smarter, Softer: Next-Generation Wearable Robots (2014, background): “• IEEE ROBOTICS & AUTOMATION MAGAZINE • DECEMBER 2014 28 extensive work has been done to properly measure human kinematics with IMUs, these systems require additional sensors or aggressive filtering techniques to avoid problems related to integration drift [38], [39].”
Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion (2017, methodology): “[97] 2006 attitude computer graphics link ori method 1 acc, 1 gyro, 1 mag quat ori KF params EKF, QUEST 1, tilt table roll pitch yaw 2.”