Emel Demircan
Stanford University, California State University, Long Beach
Papers
7
Total Citations
228
H-Index
6
About
Emel Demircan is a leading researcher at the intersection of robotics, biomechanics, and human motion science. Her work focuses on understanding and synthesizing human movement, with a particular emphasis on using robotic principles—such as operational space control and task dynamics—to analyze and reconstruct complex motions. She has made foundational contributions to the robotics-based synthesis of human motion, as evidenced by her most cited paper (103 citations), which explores how robotic frameworks can generate anthropomorphic movement. Her 2016 survey on anthropomorphic movement analysis and synthesis (61 citations) remains a key reference for researchers studying the redundant and underactuated nature of the human body. Demircan has also pioneered methods for reconstructing human motion from marker trajectories and for analyzing muscle force transmission during elite athletic performances, such as golf swings (17 citations). More recently, she has integrated machine learning and electromyography (EMG) pattern classification to advance human-robot interaction and rehabilitation, including robot-assisted reaching tasks (8 citations). Her work has significant implications for assistive robotics, rehabilitation engineering, and sports science, establishing her as a pivotal figure in translating robotic theory into practical insights for human movement understanding and enhancement.
Research Focus
Key Achievements
Top Papers
- 1Robotics-based synthesis of human motion103 citations · 2009
- 2
- 3Human Motion Reconstruction by Direct Control of Marker Trajectories28 citations · 2008
- 4
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- 6Machine learning for human movement understanding7 citations · 2020
- 7