Jakob Ziegler
Papers
5
Total Citations
118
H-Index
5
About
Jakob Ziegler’s research lies at the intersection of human motion analysis, wearable robotics, and sensor-based tracking, with a focus on advancing rehabilitation technologies. His most cited work, “Classification of Gait Phases Based on Bilateral EMG Data Using Support Vector Machines” (57 citations), introduces robust machine learning methods for decoding user intent from muscle signals, directly enabling more responsive and wearable robotic systems for movement disorder therapy. Ziegler also pioneered hybrid motion capture techniques, as seen in his 2011 paper on combining IMU and laser tracking for accurate large-area human motion capture (34 citations), which has applications ranging from film animation to clinical gait labs. His recent 2024 study on the relationship between gait speed and cycle duration (11 citations) provides foundational models for objective walking assessment and rehabilitation device design. Additionally, his work on automated depth-sensor-based object detection and path planning for robot-aided 3D scanning (7 citations) demonstrates versatility in robotics. With a career spanning over a decade, Ziegler’s contributions are shaping how engineers and clinicians capture, interpret, and assist human movement, making him a key figure in the evolution of intelligent rehabilitation systems.
Research Focus
Key Achievements
Top Papers
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