Estimation and Observability Analysis of Human Motion on Lie Groups
Autor: | Kevin Westermann, Vladimir Joukov, Ivan Petrović, Josip Cesic, Ivan Marković, Dana Kulic |
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Rok vydání: | 2019 |
Předmět: |
Kinematic chain
0209 industrial biotechnology Computer science Movement Posture Euclidean group 02 engineering and technology Kinematics Extended Kalman filter Acceleration symbols.namesake 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering State space Humans Observability Electrical and Electronic Engineering Human Body Kinematics Motion Estimation on Lie Groups Marker Measurements IMUs Observability Analysis Lie group Kalman filter Robotics Models Theoretical Computer Science Applications Biomechanical Phenomena Human-Computer Interaction Euler angles Control and Systems Engineering symbols 020201 artificial intelligence & image processing Algorithm Software Algorithms Information Systems |
Zdroj: | IEEE transactions on cybernetics. 50(3) |
ISSN: | 2168-2275 |
Popis: | This article proposes a framework for human-pose estimation from the wearable sensors that rely on a Lie group representation to model the geometry of the human movement. Human body joints are modeled by matrix Lie groups, using special orthogonal groups SO(2) and SO(3) for joint pose and special Euclidean group SE(3) for base-link pose representation. To estimate the human joint pose, velocity, and acceleration, we develop the equations for employing the extended Kalman filter on Lie groups (LG-EKF) to explicitly account for the non-Euclidean geometry of the state space. We present the observability analysis of an arbitrarily long kinematic chain of SO(3) elements based on a differential geometric approach, representing a generalization of kinematic chains of a human body. The observability is investigated for the system using marker position measurements. The proposed algorithm is compared with two competing approaches: 1) the extended Kalman filter (EKF) and 2) unscented KF (UKF) based on the Euler angle parametrization, in both simulations and extensive real-world experiments. The results show that the proposed approach achieves significant improvements over the Euler angle-based filters. It provides more accurate pose estimates, is not sensitive to gimbal lock, and more consistently estimates the covariances. |
Databáze: | OpenAIRE |
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