CovidAlert - A Wristwatch-Based System to Alert Users from Face Touching
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, ISSN: 1867-822X, Vol: 431 LNICST, Page: 489-504
2022
- 3Citations
- 7Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Conference Paper Description
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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85127904318&origin=inward; http://dx.doi.org/10.1007/978-3-030-99194-4_30; https://link.springer.com/10.1007/978-3-030-99194-4_30; https://dx.doi.org/10.1007/978-3-030-99194-4_30; https://link.springer.com/chapter/10.1007/978-3-030-99194-4_30
Springer Science and Business Media LLC
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