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About

Manon Kok is a leading researcher in inertial sensor-based motion tracking and probabilistic signal processing, with particular expertise in human kinematics, robotic systems, and indoor positioning. Her work addresses one of the most persistent challenges in wearable motion capture: accurately estimating joint and segment motion outside controlled laboratory settings without relying on magnetic field measurements, which are frequently corrupted by environmental disturbances. Her most influential contributions include developing drift-free methods for long-term joint kinematics estimation using inertial measurement units (IMUs), garnering 65 citations, and pioneering robust plug-and-play joint axis estimation techniques that simplify sensor-to-segment calibration in both human and robotic limbs (31 citations). Kok has also made significant theoretical advances in establishing observability conditions for magnetometer-free inertial tracking in complex kinematic chains, work that is foundational for reliable exoskeleton and prosthetic control applications. Beyond biomechanics, her research extends into Gaussian process-based modeling of ambient magnetic fields for indoor navigation, demonstrating a breadth of expertise in Bayesian nonparametric methods. Across her body of work, Kok consistently bridges rigorous mathematical theory with practical, real-world applicability — making her research highly relevant to clinicians, roboticists, and engineers working at the intersection of sensing, estimation, and human movement science.