Research Description
In addition to human neuromusculoskeletal models, we have developed dynamic models and optimal controllers for upper-limb stroke rehabilitation robotics, which provide assistive/resistive loads according to patient-specific needs. We are now gamifying the robotic system to enhance patient experience and are using artificial intelligence to learn patient-specific characteristics. The stroke rehabilitation robot is currently being tested at Grand River Hospital.
Student Researchers
• Ali Nasr (Alumnus)
• Jason Hunter (Alumnus)
• Arash Hashemi (Alumnus)
• Parya Khoshroo (Alumnus)
• Reza Sharif Razavian (Alumnus)
• Borna Ghannadi (Alumnus)
Keywords and Themes
• Stroke Rehabiliation
• Physical Human-Robot Interaction
• Biomechatronic Systems Modelling and Control
• Rehabiliation Robotics
• Upper-Limb Musculoskeletal Modelling
Related Publications
• Ghannadi B, Razavian RS, and McPhee J. (2018). Configuration-Dependent Optimal Impedance Control of an Upper Extremity Stroke Rehabilitation Manipulandum. Frontiers in Robotics and AI. DOI: 10.3389/frobt.2018.00124.
• Ghannadi B, Mehrabi N, Razavian RS, and McPhee J. (2017). Nonlinear Model Predictive Control of an Upper Extremity Rehabilitation Robot using a Two-Dimensional Human-Robot Interaction Model. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). DOI: 10.1109/IROS.2017.8202200.
• Ghannadi B and McPhee J. (2015). Optimal Impedance Control of an Upper Limb Stroke Rehabilitation Robot. ASME 2015 Dynamic Systems and Control Conference. DOI: 10.1115/DSCC2015-9689.