Creating wearable technologies to track patients’ health in real time
Mohamad Forouzanfar, professor in the Department of Systems Engineering
Tuesday, February 23, 2021
Mohamad Forouzanfar, a professor-researcher in the Department of Systems Engineering, is passionate about the design of non-invasive technologies for health care management, particularly portable, easy-to-use physiological monitors, commonly known as wearable health monitors or WHMs.
His research activities focus on the creation of new technologies for measuring vital signs and the development of signal processing and machine learning algorithms for automatic analysis and interpretation of biomedical data.
“Most current physiological monitors rely on active interaction with a user or a health care professional to function. This interaction can be disruptive to both the subject and the results. Most conventional medical measurement and monitoring methods have evolved very little since their inception and are still generally used within health care facilities,” explains the professor. In addition, highly qualified personnel currently devote long hours analyzing and interpreting the physiological data from these devices.
There is therefore a need for portable, discreet, and inexpensive technologies that can record vital data during the patient’s daily activities, whether at work, at home or when practicing sports.
He is particularly interested in sleep physiological monitoring. Sleep provides a window into the functioning of the human body at rest, which can help detect disease. It is known, for example, that blood pressure and heart rate profile during sleep are often associated with cardiovascular events, independently of their daytime profiles. In addition, many health-related events, such as strokes, can occur during sleep, resulting in death or delays in diagnosis or treatment.
Professor Forouzanfar will use artificial intelligence to develop medical devices and imaging systems that will continuously monitor a patient’s physiological data. He hopes to develop methods for processing and modeling biological signals and images. His research will also focus on machine learning algorithms to automatically analyze the physiological data collected and to detect or predict disease.
His leitmotif? To have an impact on the world’s population, to transform his ideas into tangible and effective products. He also wants to encourage young researchers to look to the future to anticipate needs.
A project in collaboration with Professor Jérémie Voix and the company EERS
Mohamad Forouzanfar’s team implemented a project using digital technologies designed by Professor Jérémie Voix’s team.
The project aims to design in-ear plugs capable of monitoring human health in real time using different audio signal processing and machine learning algorithms. These algorithms will make it possible to process physiological and non-physiological signals obtained from portable hearing sensors, and to extract vital signs considered essential for assessing human health (heart rate, blood pressure, respiratory rate, blood oxygen saturation and body temperature).
His academic background
Prior to joining the ÉTS, Mohamad Forouzanfar was a researcher at the Stanford Research Institute in Menlo Park, California. He previously held postdoctoral research positions at Stanford University, Harvard University and the University of Ottawa, where he received his PhD in Electrical and Computer Engineering in 2014.
His research to date has focused on the design and development of new portable and non-contact physiological measurement techniques and instrumentation, biological modeling and signal processing methods, and machine learning and deep learning algorithms.
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