Detecting Face Touching with Dynamic Time Warping on Smartwatches: A Preliminary Study

ABSTRACTRespiratory diseases such as the novel coronavirus (COVID-19) can be transmitted through people's face-touching behaviors. One of the official recommendations for protecting ourselves from such viruses is to avoid touching our eyes, nose, or mouth with unwashed hands. However, prior work has found that people touch their face 23 times per hour on average without realizing it. Therefore, in this Late-Breaking Work, we explore a possible approach to help users avoid touching their face in daily life by alerting them through a smartwatch application every time a face-touching behavior occurs. We selected 10 everyday activities including several that should be easy to distinguish from face touching and several that should be more challenging. We recruited 10 participants and asked them to perform each activity repeatedly for 3 minutes at their own pace while wearing a Samsung smartwatch. Based on the collected accelerometer data, we used dynamic time warping (DTW) to distinguish between the two groups of activities (i.e., face-touching and non-face-touching), which is a method well-suited for small datasets. Our findings show that the DTW-based classifier is capable of classifying the activities into two groups with high accuracy (i.e., 99.07% for the user-dependent scenario). We demonstrated that smartwatches have the potential to detect face-touching behaviors with the proposed methodology. Future work can explore other classification approaches, collect larger datasets, and consider other sensors to increase the robustness of our results.