Abstract
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 TouchAlert: a system that automatically predict the occurrence of face-touch activity and warn the user before its occurrence. Specifically, TouchAlert utilizes commodity wearable devices’ sensors to train a deep learning-based model for predicting the variable length face-touching of different users at an early stage of its occurrence. Our experimental results show high accuracy of F1-score of 0.98 and prediction accuracy of 97.9%.
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Notes
- 1.
Even experts cannot avoid face touch activity: https://youtu.be/mA1wqjaeKj0.
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Acknowledgement
The work was supported by “Research and Development of Information and Communication Technologies that Contribute to Countermeasures against Infectious Diseases (222-C03)”, the Commissioned Research of National Institute of Information and Communications Technology (NICT), JAPAN.
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Rizk, H., Amano, T., Yamaguchi, H., Youssef, M. (2022). Smartwatch-Based Face-Touch Prediction Using Deep Representational Learning. In: Hara, T., Yamaguchi, H. (eds) Mobile and Ubiquitous Systems: Computing, Networking and Services. MobiQuitous 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-94822-1_29
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DOI: https://doi.org/10.1007/978-3-030-94822-1_29
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