Papers
7
Total Citations
105
H-Index
5
About
Shrey Pareek is at the forefront of intelligent robotic rehabilitation, pioneering systems that adapt to a patient’s real-time physical and mental state. His research seamlessly integrates reinforcement learning, wearable biosensors, and machine learning to create more effective, personalized therapy. Pareek’s major contributions include AR3n, a novel reinforcement learning-based controller that provides adaptive, “assist-as-needed” support during robotic therapy, moving beyond rigid, patient-specific models. He also developed MyoTrack, a real-time system using surface electromyography (sEMG) and inertial measurement units (IMUs) to objectively track patient participation, ensuring active engagement during rehabilitation. His work on iART employs LSTM-based learning from demonstration to mimic a therapist’s nuanced assistance. Notably, his research on using passive Brain-Computer Interfaces (BCI) to gauge mental engagement represents a paradigm shift in the field. With his most-cited papers accumulating over 100 citations, Pareek’s work is highly influential, particularly his timely 2021 study on home-based robotic rehabilitation during the COVID-19 outbreak, which addressed the critical need for remote therapy. His innovations are shaping the future of accessible, intelligent, and patient-centered neurorehabilitation.
Research Focus
Key Achievements
Top Papers
- 1Upper Limb Home-Based Robotic Rehabilitation During COVID-19 Outbreak26 citations · 2021
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- 4iART: Learning From Demonstration for Assisted Robotic Therapy Using LSTM15 citations · 2019
- 5Adaptation of Rehabilitation System Based on User’s Mental Engagement7 citations · 2015
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