Adaptive EMG-Driven Control for Multi-DOF Robotic Exoskeletons

This project aims to develop intelligent control systems for robotic exoskeletons that can better assist human movement. Using artificial intelligence, specifically deep reinforcement learning, the project will train controllers in advanced computer simulations that model both the human body and the robotic device. Muscle activity signals (EMG) will be incorporated so that the exoskeleton can adapt its assistance based on the user’s physical effort. The trained controllers will then be tested and refined on a real multi-joint exoskeleton. The project will benefit the participating institutions by strengthening their collaboration in AI and assistive robotics, and enhancing expertise in adaptive robotic systems for healthcare and rehabilitation applications.

Faculty Supervisor:

Mojtaba Ahmadi

Student:

Partner:

Technická univerzita v Liberci

Discipline:

Engineering

Sector:

Health and Related Sciences and Technology; Artificial Intelligence

University:

Carleton University

Program: