MOBAIL

MOBAIL's Research Objectives

The use of mobile devices and artificial intelligence in consumer style well-being applications. We intend to study physiological signals that can be collected with wearable and mobile devices as well as optimizing their processing requirements.

Mobile and Wearable Devices

Mobile and wearable devices are embedded with heterogenous sensing features useful for vital sign monitoring.

Physiological Signal Processing

Signal processing is essential to remove the noise and interference present inside the physiological signals.

Artificial Intelligence

By utilizing the AI technology, we can retrieve useful feature from the post-processed physiological signals.

Tutorials

To learn more about sensing modalities, physiological signal processing and AI, check out our blogs.

How mobile and wearable sensing define our life?

Mobile and wearable devices, undoubtedly, has became an integral part of our daily life. By manipulating the rich sensing features available on these devices, we can constantly monitor the vital sign related to health and fitness issues of our beloved family members. Visit our Github to learn more about our projects.

View Our Projects

Dae Yon Hwang

Ph.D. Candidate, University of Toronto

Dae Yon Hwang is the Ph.D. candidate at University of Toronto. His research interests are signal processing, computer visions, and machine learning.

Prof. Dimitrios Hatzinakos

Professor, University of Toronto

Prof. Hatzinakos's research interests and expertise are in the areas of Multimedia Signal Processing, Multimedia Security, Multimedia Communications and Biometric Systems.

Prof. Konstantinos N. Plataniotis

Professor, University Toronto

Prof. Plataniotis's research interests are machine learning, adaptive systems and pattern recognition, image & signal processing, communications systems, and big data analytics.

Sherif Seha

Ph.D. Candidate, University of Toronto

Sherif Seha is a Ph.D. candidate at the University of Toronto. His research interests include signal processing, machine learning and biometrics.

Prof. Jamshid Abouei

Associate Professor, Yazd University

Prof. Abouei is currently an Associate Professor in the Department of Electrical Engineering, at Yazd University, Yazd, Iran where he leads the research group at the Wireless Networking Laboratory.

Lili Zhu

Ph.D. Candidate, University of Guelph

Lili is the Ph.D. candidate at University of Guelph. Her research interests including machine learning on IoT, mobile and wearable devices.

Prof. Petros Spachos

Associate Professor, University of Guelph

Prof. Spachos's'research interests span primarily in the area of mobile systems and the Internet of Things (IoT), focusing on the design, simulation, and evaluation of smart systems.

Pai Chet Ng

Postdoc, University of Toronto

Pc Ng is a recent graduate from Hong Kong University of Science and Technology. Her research interests including IoT and mobile computing with Artificial Intelligence techniques.

Our Skills.

Our projects leverage the deep learning framework to learn useful features from noisy physiological signals.

Working with embedded devices is a fundamental step to design an efficient signal acquisition system for further analysis.

Deep Learninig

90%

Python and Matlab

95%

Embedded Programming

85%

"The signal is the truth. The noise is what Distracts us from the truth." - Nate Silver -

CONTACT

Lets get in touch. Send us a message:

University of Toronto, Canada Phone: +1 (416) 946-5605 Email: kostas@ece.utoronto.ca