Papers
10
Total Citations
163
H-Index
7
About
Sangram Redkar is a leading researcher at the intersection of wearable robotics, nonlinear dynamics, and data-driven control. His work focuses on enhancing human performance through innovative phase oscillator designs for exoskeletons and powered prosthetics. Redkar’s seminal 2014 paper on using limit cycles to “pump energy” into human motion at resonance has garnered 61 citations, demonstrating how cyclic tasks can be performed with reduced metabolic cost. He further advanced the field with a comprehensive 2017 review on inertial tracking for lower-limb exoskeletons (42 citations), establishing cost-effective alternatives to traditional motion capture. Redkar has pioneered robust, phase-based controllers that synchronize robotic assistance with human biomechanics, and more recently, he has applied Koopman theory and deep neural networks to control bio-inspired robots, including worm robots and MEMS gyroscopes. His work on EEG-based human-computer interfaces and volitional control of prosthetic ankles underscores his commitment to translating theoretical advances into practical assistive technologies. With over 160 citations across his most-cited works, Redkar continues to shape the future of human-robot interaction and rehabilitation engineering.
Research Focus
Key Achievements
Top Papers
- 1Limit Cycles to Enhance Human Performance Based on Phase Oscillators61 citations · 2014
- 2
- 3A robust phase oscillator design for wearable robotic systems16 citations · 2020
- 4Nonlinear, Phase-Based Oscillator to Generate and Assist Periodic Motions10 citations · 2017
- 5Robot Manipulator Control Using a Robust Data-Driven Method9 citations · 2023
- 6Deep neural data-driven Koopman fractional control of a worm robot8 citations · 2024
- 7Optimal control of a MEMS gyroscope based on the Koopman theory7 citations · 2023
- 8
- 9Optimal DMD Koopman Data-Driven Control of a Worm Robot3 citations · 2024
- 10Volitional control of an active prosthetic ankle: a survey2 citations · 2018