World Health Organization (WHO) reported that viruses, including COVID-19, can be transmitted by touching the face with contaminated hands and advised people to avoid touching their face, especially the mouth, nose, and eyes. However, according to recent studies, people touch their faces unconsciously in their daily lives, and it is difficult to avoid such activities. Although many activity recognition methods have been proposed over the years, none of them target the prediction of face-touch (rather than detection) with other daily life activities. To address to problem, we propose a system that automatically predict the occurrence of face-touch activity and warn the user before its occurrence.
Related Papers
- Hamada Rizk, Tatsuya Amano, Hirozumi Yamaguchi, Moustafa Youssef, "Smartwatch-based Face-touch Prediction Using Deep Representational Learning", Proceedings of the18th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services
- H. Rizk, T. Amano, H. Yamaguchi and M. Youssef, "Cross-Subject Activity Detection for COVID-19 Infection Avoidance Based on Automatically Annotated IMU Data," in IEEE Sensors Journal, doi: 10.1109/JSEN.2022.3176291.
