Research Approach

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We use Machine Learning Techniques, System Identification and Mathematical Models to better understand the neural control of our movements and our neuromechanics.

Using the identified neuromechanics, control theory and the recent findings in the field of  human motor control and movement neuroscience, we design assistive controllers for robotic systems which will assist human subjects in a collaborative manner. 

Machine Learning techniques are widely used in our robotic system control to provide intention decoding and adaptation to the needs of each subject.

We benefit from wearable systems and in-field movement analysis to evaluate the effect of our assistive and rehabilitation robotic systems.