Raviraj Nataraj

Assistant Professor

Stevens Institute of Technology

United States

Research Interests

Explore related searches

Professors in United States

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Raviraj Nataraj is an Assistant Professor at the Stevens Institute of Technology in the United States. His research areas include motor rehabilitation using virtual reality, augmented feedback for functional training, and the application of machine learning in rehabilitation contexts. Recently, he has contributed to studies focusing on EEG theta power, visual feedback complexities, and the cognitive aspects of virtual reality rehabilitation following neurotraumas.

Articles (12)

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2021

Year:

2021

Year:

2021

Year:

2021

Year:

2020

Year:

2020

Collaborators (1)

Noam Harel

Associate Professor

Icahn School of Medicine at Mount Sinai

UNITED STATES

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Raviraj Nataraj

Research Interests

Explore related searches

Contact this professor

About

Articles (12)

Error Induction to Improve Performance with Increased EEG Theta Power for a Motor Rehabilitation Task

Joint Moment Responses to Different Modes of Augmented Visual Feedback of Joint Kinematics during Two-Legged Squat Training

The cognitive basis for virtual reality rehabilitation of upper-extremity motor function after neurotraumas

Augmented feedback modes during functional grasp training with an intelligent glove and virtual reality for persons with traumatic brain injury

Muscle model control of knee joint torque in a powered lower-body orthosis

Investigating features in augmented visual feedback for virtual reality rehabilitation of upper-extremity function through isometric muscle control

Effects of Visual Feedback Complexity on the Performance of a Movement Task for Rehabilitation

Performance potential of classical machine learning and deep learning classifiers for isometric upper-body myoelectric control of direction in virtual reality with reduced muscle inputs

Training with Agency-Inspired Feedback from an Instrumented Glove to Improve Functional Grasp Performance

Concurrent Continuous Versus Bandwidth Visual Feedback With Varying Body Representation for the 2-Legged Squat Exercise

Disproportionate positive feedback facilitates sense of agency and performance for a reaching movement task with a virtual hand

Agency and Performance of Reach-to-Grasp With Modified Control of a Virtual Hand: Implications for Rehabilitation

Collaborators (1)

Noam Harel

Make your next move.