Bidirectional Gate Recurrent Unit Neural Network for Recognizing Face Touching Activities using Smartwatch Sensors
25th International Computer Science and Engineering Conference, ICSEC 2021
; : 454-458, 2021.
Article
in English
| Scopus | ID: covidwho-1722922
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%). © 2021 IEEE.
accelerometer; deep learning; face touching activity; human activity recognition; smartwatch data; Face recognition; Recurrent neural networks; Wearable computers; Wearable sensors; Accelerometer data; Behavioral modifications; Face contacts; Learning models; Neural-networks; Wearable devices; Accelerometers
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
25th International Computer Science and Engineering Conference, ICSEC 2021
Year:
2021
Document Type:
Article
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