Papers
11
Total Citations
156
H-Index
8
About
Arash Arami is a leading researcher at the intersection of biomechanics, robotics, and machine learning, with a primary focus on human locomotion and rehabilitation technologies. His work centers on understanding and enhancing human movement through computational modeling and robotic assistance. Arami has made major contributions to the development of musculoskeletal models for natural walking, pioneering the use of deep reinforcement learning and inverse optimal control to simulate and analyze gait optimality, as seen in his highly cited 2021 and 2022 papers (37 and 15 citations, respectively). He has also advanced the clinical assessment of spasticity through quantitative modeling (35 citations), offering new tools for treatment and rehabilitation. In the realm of human-robot interaction, Arami has developed methods for estimating interaction forces in exoskeletons like the Indego (13 citations) and introduced novel approaches for evaluating co-adaptation between humans and lower limb exoskeletons (9 citations). His work on IMU-based real-time gait phase estimation using multi-resolution neural networks (11 citations) highlights his commitment to practical, wearable technologies. With over 150 citations across his top papers, Arami’s research is shaping the future of assistive robotics and personalized rehabilitation.
Research Focus
Key Achievements
Top Papers
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- 4Human–Exoskeleton Interaction Force Estimation in Indego Exoskeleton13 citations · 2023
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- 7Human–exoskeleton interaction portrait9 citations · 2024
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