Research Article
Smartwatch-Based Face-Touch Prediction Using Deep Representational Learning
@INPROCEEDINGS{10.1007/978-3-030-94822-1_29, author={Hamada Rizk and Tatsuya Amano and Hirozumi Yamaguchi and Moustafa Youssef}, title={Smartwatch-Based Face-Touch Prediction Using Deep Representational Learning}, proceedings={Mobile and Ubiquitous Systems: Computing, Networking and Services. 18th EAI International Conference, MobiQuitous 2021, Virtual Event, November 8-11, 2021, Proceedings}, proceedings_a={MOBIQUITOUS}, year={2022}, month={2}, keywords={COVID-19 Face touch Activity recognition Smartwatch}, doi={10.1007/978-3-030-94822-1_29} }- Hamada Rizk
Tatsuya Amano
Hirozumi Yamaguchi
Moustafa Youssef
Year: 2022
Smartwatch-Based Face-Touch Prediction Using Deep Representational Learning
MOBIQUITOUS
Springer
DOI: 10.1007/978-3-030-94822-1_29
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 proposeTouchAlert: a system that automatically predict the occurrence of face-touch activity and warn the user before its occurrence. Specifically,TouchAlertutilizes 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%.
