Pegah Shojaii
Papers
1
Total Citations
3
H-Index
1
About
Pegah Shojaii’s research lies at the intersection of biomechanics, motor control, and rehabilitation robotics, with a focus on how humans adapt to force perturbations during movement. Her most cited work, “Muscle co-contraction patterns in robot-mediated force field learning to guide specific muscle group training” (2017), investigates how the nervous system employs co-contraction—a strategy of simultaneously activating opposing muscles—to stabilize movement in response to external forces. This study is particularly significant for designing robot-assisted therapies for neuropathological populations, such as stroke survivors, where impaired coordination often leads to excessive co-contraction. By analyzing how movement direction influences these patterns, Shojaii’s research provides a foundation for developing targeted training protocols that retrain specific muscle groups. Though her citation count is modest, her work contributes to a growing body of knowledge on adaptive motor learning and human-robot interaction, offering practical insights for rehabilitation engineers and clinicians seeking to optimize robotic devices for personalized therapy. Her findings highlight the potential of robot-mediated environments to guide muscle group training, advancing both basic science and applied neurorehabilitation.
Research Focus
Key Achievements
Top Papers
- 1