Brokoslaw Laschowski's picture
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First Name: 
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Toronto Rehabilitation Institute
Job Title: 
Research Scientist
Artificial Intelligence, Robotics, Brain-Computer Interfaces, Machine Learning
Short Bio: 
Dr. Brok Laschowski is a Research Scientist and Principal Investigator with the Artificial Intelligence and Robotics in Rehabilitation Team at the Toronto Rehabilitation Institute, Canada’s largest rehabilitation hospital, and an Assistant Professor (status) in the Department of Mechanical and Industrial Engineering at the University of Toronto. He also works as a Core Faculty Member in the University of Toronto Robotics Institute. His fields of expertise include computer vision, machine learning, human-robot interaction, computational neuroscience, optimization, deep learning, and brain-computer interfaces. Overall, his research aims to improve health and performance by integrating humans with robotics and artificial intelligence.

Datasets & Competitions

Visual perception can improve transitions between different locomotion mode controllers (e.g., level-ground walking to stair ascent) by sensing the walking environment prior to physical interactions. Here we developed the "StairNet" dataset to support the development of vision-based stair recognition systems. The dataset builds on ExoNet – the largest open-source dataset of egocentric images of real-world walking environments.


Advances in computer vision and deep learning are enabling 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 like convolutional neural networks to recognize the human walking environment. To address these limitations, we developed "ExoNet" - the first open-source, large-scale hierarchical database of wearable camera images (i.e., egocentric perception) of real-world walking environments.


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.