| Autor: |
Dhakal, Parashar, Damacharla, Praveen, Javaid, Ahmad Y., Vege, Hari K., Devabhaktuni, Vijay K. |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
1st International Conference on Artificial Intelligence & Modern Assistive Technology (ICAIMAT), Riyadh Saudi Arabia, November, 24-26, 2020 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/ICAIMAT51101.2020.9308013 |
| Popis: |
At the time of writing this paper, the world has around eleven million cases of COVID-19, scientifically known as severe acute respiratory syndrome corona-virus 2 (SARS-COV-2). One of the popular critical steps various health organizations are advocating to prevent the spread of this contagious disease is self-assessment of symptoms. Multiple organizations have already pioneered mobile and web-based applications for self-assessment of COVID-19 to reduce this global pandemic's spread. We propose an intelligent voice-based assistant for COVID-19 self-assessment (IVACS). This interactive assistant has been built to diagnose the symptoms related to COVID-19 using the guidelines provided by the Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO). The empirical testing of the application has been performed with 22 human subjects, all volunteers, using the NASA Task Load Index (TLX), and subjects performance accuracy has been measured. The results indicate that the IVACS is beneficial to users. However, it still needs additional research and development to promote its widespread application. |
| Databáze: |
arXiv |
| Externí odkaz: |
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