Brokoslaw Laschowski's picture
Real name: 
Brokoslaw Laschowski
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First Name: 
Brokoslaw
Last Name: 
Laschowski
Affiliation: 
Toronto Rehabilitation Institute
Job Title: 
Research Scientist
Expertise: 
Computational neuroscience, Artificial intelligence, Brain-machine interfaces
External links: 
Toronto Rehabilitation Institute
News Article, Building Robot Brains
Short Bio: 
Dr. Brokoslaw Laschowski is a computational neuroscientist. He works as a Research Scientist and Principal Investigator at the Toronto Rehabilitation Institute, Canada’s largest rehabilitation hospital, and an Assistant Professor at the University of Toronto, where he leads a multidisciplinary research lab exploring the intersection of neuroscience and artificial intelligence. His research focuses on the development of new mathematical, computational, and machine learning models to reverse engineer and/or interface with the brain. In addition to advancing our scientific understanding of intelligence in biological and artificial systems, one of the practical applications of his models is to control robotic and neuroprosthetic technologies to assist patients with physical disabilities, ranging from autonomous control using brain-inspired algorithms to neural control using brain-machine interfaces.

Datasets & Competitions

EMGNet: An EMG Dataset for Neural Decoding

Surface electromyography (EMG) can be used to interact with and control robotic systems via intent recognition. However, most machine learning algorithms used to decode EMG signals have been trained on relatively small datasets with limited subjects, which can affect their widespread generalization across different users and activities. Motivated by these limitations, we developed EMGNet - a large-scale dataset to support research and development in EMG neural decoding, with an emphasis on human locomotion.

Categories:
Artificial Intelligence
Machine Learning
874 Views

StairNet: A Computer Vision Dataset for Stair Recognition

Visual systems 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 StairNet to support the development of vision-based stair recognition systems for robotic leg control. The dataset builds on ExoNet – the largest open-source dataset of egocentric images of real-world walking environments.

Categories:
Artificial Intelligence
Machine Learning
Wearable Sensing
Computer Vision
2726 Views

ExoNet: Egocentric Images of Walking Environments

Computer vision can be used for environment-adaptive control of robotic exoskeletons and prostheses. However, small-scale and private training datasets have impeded the development of image classification algorithms (e.g., convolutional neural networks) to recognize the walking environment. To address these limitations, we developed ExoNet, a large-scale dataset of wearable camera images (i.e., egocentric perception) of real-world walking environments.

Categories:
Artificial Intelligence
Machine Learning
Computer Vision
5852 Views

Wearable Images of Human Walking Environments

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.

Categories:
Artificial Intelligence
Machine Learning
Wearable Sensing
Computer Vision
595 Views