Robotic-based methodology for synthetic seizure dataset generation for machine learning-driven medical devices.

This research project aims to improve epilepsy treatment by developing a robotic-based method for testing wearable seizure detection devices. The project will create a robotic system that can simulate seizures, providing realistic data to help refine and test machine learning algorithms for detecting seizures more accurately. The goal is to address the limitations of current wearable devices and enhance the effectiveness of seizure detection and neurostimulation. By improving these devices, the project intends to make epilepsy treatment more accessible and enhance the quality of life for millions of people affected by this neurological condition.

Faculty Supervisor:

Xilin Liu

Student:

Partner:

NerveX Neurotechnologies, Inc.

Discipline:

Engineering

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Toronto

Program: