Publication: Bidirectional Gate Recurrent Unit Neural Network for Recognizing Face Touching Activities using Smartwatch Sensors
Issued Date
2021-01-01
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2-s2.0-85125201886
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Mahidol University
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SCOPUS
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ICSEC 2021 - 25th International Computer Science and Engineering Conference. (2021), 454-458
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Sakorn Mekruksavanich, Ponnipa Jantawong, Narit Hnoohom, Anuchit Jitpattanakul Bidirectional Gate Recurrent Unit Neural Network for Recognizing Face Touching Activities using Smartwatch Sensors. ICSEC 2021 - 25th International Computer Science and Engineering Conference. (2021), 454-458. doi:10.1109/ICSEC53205.2021.9684642 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/76704
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Title
Bidirectional Gate Recurrent Unit Neural Network for Recognizing Face Touching Activities using Smartwatch Sensors
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Abstract
Globally, the COVID-19 pandemic has caused dev-Astation and continues to do so even a year after its first outbreak. Behavioral modifications could help to mitigate a mechanism for acquiring and spreading illnesses. Using wearable devices such as smartwatches to recognize face contact has the opportunity to decrease face touching and, therefore, the spread of respiratory disease through fomite transmission. The purpose of this paper is to demonstrate how we can utilize accelerometer data from wristwatch sensors to identify face touching actions using deep learning techniques. We proposed the BiGRU deep learning model for the high-performance recognition of hand-To-face actions. The Face Touching dataset is used as a benchmark for evaluating the recognition accuracy of deep learning networks, including our network model. The experimental findings indicate that the BiGRU surpasses other baseline deep learning models regarding accuracy (98.56%) and F1-score (98.56%).