Brokoslaw Laschowski's picture
Real name: 
Brokoslaw Laschowski
First Name: 
Brokoslaw
Last Name: 
Laschowski
Affiliation: 
Toronto Rehabilitation Institute
Job Title: 
Research Scientist
Expertise: 
Machine Learning, Computational Neuroscience, Neural Networks, Artificial Intelligence, Brain-Machine Interfaces
External links: 
Toronto Rehabilitation Institute
News article, Building Robot Brains
Computational Neuroscience Lab
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 as an Assistant Professor at the University of Toronto, where he leads a multidisciplinary research lab that explores 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 the 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 robots via intent recognition. However, most machine learning algorithms used to decode EMG signals have been trained on small datasets with limited subjects, impacting their generalization across different users and tasks. Here we developed EMGNet, a large-scale dataset for EMG neural decoding of human movements. EMGNet combines 7 open-source datasets with processed EMG signals for 132 healthy subjects (152 GB total size).

Categories:
Artificial Intelligence
Machine Learning
1411 Views

StairNet: A Computer Vision Dataset for Stair Recognition

Vision is important for transitions between different locomotor controllers (e.g., level-ground walking to stair ascent) by sensing the environment prior to physical interactions. Here we developed StairNet to support the development and comparison of deep learning models for visual recognition of stairs. 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
3106 Views

ExoNet: Egocentric Images of Walking Environments

Egocentric vision is important for environment-adaptive control and navigation of humans and robots. Here we developed ExoNet, the largest open-source dataset of wearable camera images of real-world walking environments. The dataset contains over 5.6 million RGB images of indoor and outdoor environments, which were collected during summer, fall, and winter. 923,000 of the images were human-annotated using a 12-class hierarchical labelling architecture.

Categories:
Artificial Intelligence
Machine Learning
Computer Vision
6176 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
619 Views