Souha Baklouti
Papers
2
Total Citations
4
H-Index
2
About
Souha Baklouti is a researcher advancing the field of human movement analysis through wearable inertial measurement units (IMUs). Her primary research areas include sensor fusion, biomechanics, and upper limb kinematics estimation. Baklouti’s major contribution is the development of a novel magnetometer-free orientation estimation approach that addresses the critical challenge of accurate joint angle monitoring in indoor environments, where magnetic interference often degrades sensor performance. Her refined Kalman filter (KF) method, presented in her most-cited 2025 study, enables robust and precise upper limb kinematic tracking without reliance on magnetometers—a significant step forward for clinical and rehabilitation applications. In related work (2022), she compared complementary and double-stage Kalman filter data fusion techniques for serial manipulator joint angle monitoring, further demonstrating her expertise in sensor integration. Though early in her career, Baklouti’s work has already garnered attention, with her top papers cited twice each, signaling growing impact. Her research holds promise for advancing wearable technology in healthcare, robotics, and human–machine interaction, offering practical solutions for real-world motion analysis.
Research Focus
Key Achievements
Top Papers
- 1
- 2