Brokoslaw Laschowski's picture
Congratulations!  You have been automatically subscribed to IEEE DataPort and can access all datasets on IEEE DataPort!
First Name: 
Last Name: 
Toronto Rehabilitation Institute
Job Title: 
Research Scientist
Deep Learning, Wearable Robotics, Computer Vision, Rehabilitation Robotics
Short Bio: 
Dr. Brokoslaw Laschowski is a research scientist with the Artificial Intelligence and Robotics in Rehabilitation Team at the Toronto Rehabilitation Institute and an assistant professor (status) in the Department of Mechanical and Industrial Engineering at the University of Toronto. He also works as an instructor in the Department of Computer Science and as an affiliate faculty member in the Robotics Institute. He specializes in the field of biomechatronics (i.e., the integration of humans with machines), with an emphasis on the design and control of wearable robotic systems for human-robot interaction and legged locomotion. Applications of his research include neural engineering, rehabilitation robotics, and assistive technology (e.g., bionic prosthetics and exoskeletons) for older adults and/or persons with physical disabilities. Other applications include the design and integration of 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.

Datasets & Competitions

Computer vision can be used by robotic leg prostheses and exoskeletons to improve transitions between different locomotion modes (e.g., level-ground walking and stair ascent) via prediction of oncoming environmental states. 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.


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.