Brokoslaw Laschowski's picture
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Temerty Faculty of Medicine, University of Toronto
Job Title: 
Postdoctoral Research Fellow
Deep Learning, Wearable Robotics, Computer Vision, Neural Engineering
Short Bio: 
Dr. Brokoslaw Laschowski is a postdoctoral research fellow in the Temerty Faculty of Medicine at the University of Toronto, where he works in the Intelligent Assistive Technology and Systems Laboratory. He also holds an affiliation with the Artificial Intelligence and Robotics in Rehabilitation Lab at the Toronto Rehabilitation Institute. As a research scientist, he specializes in the design and control of wearable robotic systems and technologies using a "biomechatronic" systems engineering approach. Applications of his research include rehabilitation robotics, neural engineering, human-computer interaction, and wearable assistive devices (e.g., robotic prosthetic legs and exoskeletons). His clinical research focuses on assisting individuals with mobility impairments due to aging and/or physical disabilities such as stroke, cerebral palsy, osteoarthritis, Parkinson’s disease, amputation, and spinal cord injury. His research also includes the design and integration of wearable sensory feedback systems (e.g., smart glasses and haptics) for persons with visual impairments. Overall, he develops technologies that interface with humans in order to improve health and performance. Dr. Laschowski received his PhD from the Department of Systems Design Engineering, with a specialization in biomedical engineering, at the University of Waterloo and the Waterloo Artificial Intelligence Institute. His PhD research focused on 1) mathematical modelling and computer simulation of human-exoskeleton systems with energy-efficient actuators, and 2) computer vision and deep learning for autonomous exoskeleton control and decision making during legged locomotion. He received his MASc from the Department of Mechanical and Mechatronics Engineering also at the University of Waterloo, where he developed a physics-based computer model of human motor control based on forward dynamics and optimal control theory. Dr. Laschowski has published in many leading scientific journals and conferences, including the IEEE Transactions on Medical Robotics and Bionics, the Frontiers in Neurorobotics, and the IEEE International Conference on Biomedical Robotics and Biomechatronics. He previously served on the executive committee of the Canadian Society for Biomechanics and worked at the Holland Bloorview Kids Rehabilitation Hospital and as a biomechanics lecturer at Humber College. To date, he has earned over $254,000 in scholarships and awards (e.g., the Natural Sciences and Engineering Research Council of Canada) and co-authored grant proposals that received over $197,000 in research and infrastructure funding (e.g., the Canada Foundation for Innovation). He has presented at many national and international conferences and was a Best Paper Award finalist at the 2019 IEEE International Conference on Rehabilitation Robotics. His award-winning research has been featured on mainstream media networks like BBC World News, CBC radio, Forbes, and Maclean’s magazine.

Datasets & Competitions

Computer vision can be used by robotic leg prostheses and exoskeletons to improve high-level transitions between different locomotion modes (e.g., level-ground walking to stair ascent) through the prediction of future environmental states. Here we developed the StairNet dataset to support research and development in vision-based automated stair recognition.


Abstract: Recent advances in computer vision and deep learning are allowing researchers to develop automated environment recognition systems for robotic leg prostheses and exoskeletons. However, small-scale and private training datasets have impeded the widespread development and dissemination of image classification algorithms (e.g., convolutional neural networks) for recognizing the human walking environment.


*Details on the ExoNet database are provided in the references above. Please email Brokoslaw Laschowski ( for any additional questions and/or technical assistance. 


Previous studies of robotic leg prostheses and exoskeletons with regenerative actuators have focused almost exclusively on level-ground walking applications. Here we analyzed the lower-limb joint mechanical work and power during stand-to-sit movements using inverse dynamics to estimate the biomechanical energy theoretically available for electrical energy regeneration and storage. Nine subjects performed 20 sitting and standing movements while lower-limb kinematics and ground reaction forces were experimentally measured.


Reference: Laschowski B, McNally W, McPhee J, and Wong A. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 868-873. DOI: 10.1109/ICORR.2019.8779540.