Papers
16
Total Citations
434
H-Index
9
About
Jyotindra Narayan is a prominent robotics and rehabilitation engineering researcher whose work sits at the intersection of assistive technology, control systems, and machine learning. He is best known for his systematic investigations into robotic rehabilitation devices, with his 2020 review on active lower limb orthoses and exoskeletons accumulating nearly 200 citations, establishing him as a key reference point in the field. His complementary 2021 review on upper limb rehabilitation robots further cemented his authority across the full spectrum of assistive robotics. Beyond survey contributions, Narayan has made significant technical advances in intelligent control strategies for rehabilitation systems. His development of event-triggered adaptive controllers for upper-limb robots and adaptive backstepping frameworks for pediatric gait exoskeletons reflects a deep commitment to human-cooperative, patient-centered design — work that addresses real clinical challenges such as input delay, partial muscle strength, and child-specific biomechanics. He has also contributed meaningfully to dual-arm manipulator planning and to neural network-based inverse kinematics solutions for SCARA and multi-DoF robotic systems. His "Glove-Net" framework demonstrates an expanding interest in multimodal sensing for prosthetics and grasp classification. Collectively, his publications have garnered over 400 citations, marking him as an influential voice shaping the future of intelligent rehabilitation robotics.
Research Focus
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