Papers
7
Total Citations
419
H-Index
5
About
Gabriele Bleser is a leading researcher at the intersection of wearable sensing, human motion analysis, and cognitive robotics. Her work centers on developing robust, real-time systems for capturing and interpreting human movement, with a particular focus on upper limb motion tracking using commercial inertial measurement units (IMUs). Her landmark 2017 survey on IMU-based motion tracking, with over 355 citations, has become a foundational reference for researchers seeking cost-effective alternatives to optical motion capture, especially in applications where line-of-sight is limited. Bleser’s contributions extend to human-robot collaboration, where she has pioneered adaptive control frameworks for seamless handover tasks—work that bridges motion prediction and real-time robot adaptation. Her technical innovations include the use of marginalised particle filters for visual-inertial sensor fusion, advancing camera pose estimation in SLAM applications. More recently, her JointTracker system (2025) demonstrates ongoing leadership in real-time inertial kinematic chain tracking with joint position estimation. Through her work on wearable sensor networks and cognitive robotics systems, Bleser continues to shape how machines perceive, predict, and safely interact with human motion in dynamic environments.
Research Focus
Key Achievements
Top Papers
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- 3On-line Motion Prediction and Adaptive Control in Human-Robot Handover Tasks18 citations · 2019
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- 6Cognitive Robotics Systems3 citations · 2015
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