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IATSL develops assistive technology that is adaptive, flexible, and intelligent, enabling users to participate fully in their daily lives. Learn more about our research

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Andrew Garrett Kurbis

Photo of AndrewMasters Candidate

BME
University of Toronto

garrett.kurbis@utoronto.ca

 

 




Biography

Garrett is a MASc student in Biomedical Engineering under the supervision of Dr. Brokoslaw Laschowski and Dr. Alex Mihailidis. His thesis research focuses on using deep learning and large-scale data to support the development of human-robotic control systems using computer vision and noisy high-dimensional biological signals (e.g., Electromyography).

Garrett joined the IATSL lab in January 2022, starting as an Undergraduate Research Assistant. He notably worked on the StairNet project, focusing on the development of vision-based environmental recognition systems for automated high-level control of robotic prostheses, exoskeletons, and other assistive technologies for legged locomotion.

He received his Bachelor of Applied Science in Biomedical Engineering at the University of Waterloo in 2023. Garrett's research interests lie primarily in the application of AI in Biomedical Engineering, particularly in areas such as computer vision, deep learning, neuroscience, wearable robotics, medical imaging, and diagnostic technologies.

Garrett presented his poster at ICAIR 2023.


Publications

A. G. Kurbis, D. Kuzmenko, B. Ivanyuk-Skulskiy, A. Mihailidis, and B. Laschowski, “StairNet: Visual recognition of stairs for human-robot locomotion,” BioMedical Engineering OnLine, vol. 23, no. 1, Feb. 2024. doi:10.1186/s12938-024-01216-0.

A. G. Kurbis, A. Mihailidis and B. Laschowski, "Development and Mobile Deployment of a Stair Recognition System for Human–Robot Locomotion," in IEEE Transactions on Medical Robotics and Bionics, vol. 6, no. 1, pp. 271-280, Feb. 2024, doi: 10.1109/TMRB.2024.3349602.

B. Ivanyuk-Skulskiy, A. G. Kurbis, A. Mihailidis, and B. Laschowski, "Sequential Image Classification of Human- Robot Walking Environments using Temporal Neural Networks," in bioRxiv, 2023, preprint doi: 10.1101/2023.11.10.566555.

D. Kuzmenko, O. Tsepa, A. G. Kurbis, A. Mihailidis and B. Laschowski, "Efficient Visual Perception of Human-Robot Walking Environments Using Semi-Supervised Learning," in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA, 2023, pp. 6544-6549, doi: 10.1109/ IROS55552.2023.10341654.

A. G. Kurbis, B. Laschowski and A. Mihailidis, "Stair Recognition for Robotic Exoskeleton Control using Computer Vision and Deep Learning," 2022 International Conference on Rehabilitation Robotics (ICORR), Rotterdam, Netherlands, 2022, pp. 1-6, doi: 10.1109/ICORR55369.2022.9896501.