Research Article
CovidAlert - A Wristwatch-Based System to Alert Users from Face Touching
@INPROCEEDINGS{10.1007/978-3-030-99194-4_30, author={Mrinmoy Roy and Venkata Devesh Reddy Seethi and Pratool Bharti}, title={CovidAlert - A Wristwatch-Based System to Alert Users from Face Touching}, proceedings={Pervasive Computing Technologies for Healthcare. 15th EAI International Conference, Pervasive Health 2021, Virtual Event, December 6-8, 2021, Proceedings}, proceedings_a={PERVASIVEHEALTH}, year={2022}, month={3}, keywords={Covid-19 CovidAlert model Sensors Machine learning STA/LTA algorithm Hand to face transition dataset Smartwatch}, doi={10.1007/978-3-030-99194-4_30} }- Mrinmoy Roy
Venkata Devesh Reddy Seethi
Pratool Bharti
Year: 2022
CovidAlert - A Wristwatch-Based System to Alert Users from Face Touching
PERVASIVEHEALTH
Springer
DOI: 10.1007/978-3-030-99194-4_30
Abstract
Worldwide 219 million people have been infected and 4.5 million have lost their lives in ongoing Covid-19 pandemic. Until vaccines became widely available, precautions and safety measures like wearing masks, physical distancing, avoiding face touching were some of the primary means to curb the spread of virus. Face touching is a compulsive human behavior that can not be prevented without constantly making a conscious effort, even then it is inevitable. To address this problem, we have designed a smartwatch-based solution, CovidAlert, that leverages Random Forest algorithm trained on accelerometer and gyroscope data from the smartwatch to detect hand transition to face and sends a quick haptic alert to the users. CovidAlert is highly energy efficient as it employs STA/LTA algorithm as a gatekeeper to curtail the usage of Random Forest model on the watch when user is inactive. The overall accuracy of system is(88.4\%)with low false negatives and false positives. We also demonstrated the system viability by implementing it on a commercial Fossil Gen 5 smartwatch.
