Gelareh Hajian
University of New Brunswick, Queen's University, University of Toronto
Papers
4
Total Citations
51
H-Index
4
About
Gelareh Hajian is a researcher at the forefront of human-machine interaction, specializing in the estimation of human force and torque from physiological signals. Her work is critical for advancing powered exoskeletons, intuitive prosthetics, and robotic rehabilitation devices. Hajian’s key contribution lies in developing robust, multimodal models that can accurately predict endpoint forces during both quasi-dynamic and dynamic muscle contractions—a notoriously difficult problem due to changing joint angles and movement speeds. Her 2022 paper on this topic has garnered 17 citations, establishing a foundation for her subsequent innovations. To address the challenge of model generalization across new users, Hajian pioneered the use of transfer learning with EMG and IMU data, a breakthrough detailed in her 2024 work (13 citations). She has also demonstrated expertise in feature selection and ensemble methods for isometric force estimation (14 citations). By integrating deep learning and classical machine learning, Hajian is systematically solving the core challenge of making force estimation practical for real-world, dynamic applications, paving the way for more responsive and adaptive assistive technologies.
Research Focus
Key Achievements
Top Papers
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