7 papers
Calibration-free IMU-based Relative Orientation Estimation under Dynamic Human Motion
Ruiyuan Li, Hassan Osman, Ajay Seth +1
Recent magnetometer-free inertial measurement unit (IMU) methods can reduce drift in joint motion estimation using rigid-body constraints, but they typically require pre-calibrated…
Inertial Human Motion Capture: From Biomechanics to Recent Sensor Fusion Methods and Back
Manon Kok, Ive Weygers, Hassan Osman +4
Inertial measurement units (IMUs) are a promising means to capture human motion, yet obtaining meaningful biomechanical quantities from IMU measurements remains non-trivial. This t…
Physics-Guided Recurrent State-Space Neural Networks for Multi-Step Prediction
Ruiyuan Li, Ajay Seth, Manon Kok
State-space models are traditionally based on physical knowledge, but multi-step predictions from these physical models can be poor due to model inaccuracy. Black-box deep learning…
A Tightly Coupled IMU-Based Motion Capture Approach for Estimating Multibody Kinematics and Kinetics
Hassan Osman, Daan de Kanter, Jelle Boelens +2
Inertial Measurement Units (IMUs) enable portable, multibody motion capture (MoCap) in diverse environments beyond the laboratory, making them a practical choice for diagnosing mob…
Optimal-state Dynamics Estimation for Physics-based Human Motion Capture from Videos
Cuong Le, Viktor Johansson, Manon Kok +1
Human motion capture from monocular videos has made significant progress in recent years. However, modern approaches often produce temporal artifacts, e.g. in form of jittery motio…
Tensor network square root Kalman filter for online Gaussian process regression
Clara Menzen, Manon Kok, Kim Batselier
The state-of-the-art tensor network Kalman filter lifts the curse of dimensionality for high-dimensional recursive estimation problems. However, the required rounding operation can…
Projecting basis functions with tensor networks for Gaussian process regression
Clara Menzen, Eva Memmel, Kim Batselier +1
This paper presents a method for approximate Gaussian process (GP) regression with tensor networks (TNs). A parametric approximation of a GP uses a linear combination of basis func…