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.