Frequent facial self-touches, primarily during outbreaks, have the potential to be a mechanism of contracting and transmitting respiratory diseases such as the novel coronavirus (COVID-19). According to the Centers of Disease Control and Prevention (CDC), droplets coming from coughing or sneezing transmit many of the germs that cause respiratory illness. These germs usually spread through close contact with an infected person, or through touching contaminated surfaces and then touching mucosal areas such as the mouth, nose, or eyes. In this talk, I will present our work that utilized the functionality and the spread of smartwatches to develop a smartwatch application to detect movement patterns of face touching. This is highly significant during respiratory infection outbreaks, as it has a great potential to refrain people from touching their faces and potentially reduce the spread of the virus. I will present our results from the developed machine learning and dynamic timeseries similarity techniques that are used to distinguish face touching movements.
Dr. Mamoun Mardini is an Assistant Professor in the Department of Aging and Geriatric Research in the College of Medicine. He is also affiliated with the Department of Health Outcomes and Biomedical Informatics. Dr. Mardini is a computer scientist by training with research expertise in applied data science in healthcare and wearable technology. He finished his bachelor and master’s degrees in Computer Engineering from the Jordan University of Science and Technology and the American University of Sharjah, respectively. He finished his doctoral degree in Computer Science from the University of North Carolina, Charlotte. Dr. Mardini is currently involved in several interdisciplinary projects that combine biomedical science, data science, and wearable technology. He has recently received a scholarship from the National Institute on Aging Claude D. Pepper Older American Independence Center to develop an EHR-based frailty index using machine learning approaches. He has also received a seed funding from UF Informatics Institute to develop a smartwatch application to detect face touching and mitigate the spread of COVID-19.