Multi-sensor fusion for continuous vitals monitoring, sleep characterization and fall detection

The proposed research project focuses on multi-sensor such as heart rate, body temperature, oxygen saturation level, and inertial sensor data-based sleep stage classification, and tremor detection in real-time for preventive health devices. The goal of the proposed research is to build robust and energy-efficient machine learning and deep learning-based approaches that extract and analyze the significant information from the multisensor data coming from wrist-based health devices to help Parkinson’s patients with tremors and an individual with sleep quality tracking. Further, the developed algorithms will be deployed on an iMD Research Zenzer bracelet to enhance its capability for monitoring sleep quality and detecting tremors.

Faculty Supervisor:

Kristiina Valter-Mai;Sridhar Krishnan;Reza Samavi

Student:

Partner:

iMD Research

Discipline:

Engineering

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

Toronto Metropolitan University

Program: