Safa Mefteh
Papers
1
Total Citations
2
H-Index
1
About
Safa Mefteh is a researcher specializing in robotics, sensor fusion, and real-time monitoring systems for articulated mechanisms. Her work centers on enhancing the precision and reliability of joint angle estimation in serial manipulators, a critical challenge in industrial automation and robotic control. In her most-cited study, "IMU Based Serial Manipulator Joint Angle Monitoring: Comparison of Complementary and Double Stage Kalman Filter Data Fusion" (2022), she systematically evaluates two prominent data fusion techniques—complementary filtering and double-stage Kalman filtering—for inertial measurement unit (IMU)-based tracking. By comparing their performance in mitigating sensor drift and noise, Mefteh provides a practical framework for selecting optimal filtering strategies in dynamic robotic environments. This contribution has garnered 2 citations, reflecting its relevance to engineers seeking cost-effective, accurate monitoring solutions. Her research bridges theoretical signal processing with applied robotics, offering insights that improve the safety and efficiency of automated systems. Mefteh’s work is particularly valuable for students and practitioners exploring low-cost, IMU-driven alternatives to traditional encoders, advancing the field of real-time kinematic analysis.
Research Focus
Key Achievements
Top Papers
- 1