{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T01:06:19Z","timestamp":1779325579585,"version":"3.51.4"},"reference-count":48,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,15]],"date-time":"2021-10-15T00:00:00Z","timestamp":1634256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Florida Center for Advanced Analytics and Data Science funded by Ernesto.Net (under the Algorithms for Good Grant)","award":["N\/A"],"award-info":[{"award-number":["N\/A"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The COVID-19 pandemic has affected almost every country causing devastating economic and social disruption and stretching healthcare systems to the limit. Furthermore, while being the current gold standard, existing test methods including NAAT (Nucleic Acid Amplification Tests), clinical analysis of chest CT (Computer Tomography) scan images, and blood test results, require in-person visits to a hospital which is not an adequate way to control such a highly contagious pandemic. Therefore, top priority must be given, among other things, to enlisting recent and adequate technologies to reduce the adverse impact of this pandemic. Modern smartphones possess a rich variety of embedded MEMS (Micro-Electro-Mechanical-Systems) sensors capable of recording movements, temperature, audio, and video of their carriers. This study leverages the smartphone sensors for the preliminary diagnosis of COVID-19. Deep learning, an important breakthrough in the domain of artificial intelligence in the past decade, has huge potential for extracting apt and appropriate features in healthcare. Motivated from these facts, this paper presents a new framework that leverages advanced machine learning and data analytics techniques for the early detection of coronavirus disease using smartphone embedded sensors. The proposal provides a simple to use and quickly deployable screening tool that can be easily configured with a smartphone. Experimental results indicate that the model can detect positive cases with an overall accuracy of 79% using only the data from the smartphone sensors. This means that the patient can either be isolated or treated immediately to prevent further spread, thereby saving more lives. The proposed approach does not involve any medical tests and is a cost-effective solution that provides robust results.<\/jats:p>","DOI":"10.3390\/s21206853","type":"journal-article","created":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T23:25:15Z","timestamp":1634513115000},"page":"6853","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Deep Learning Based Early Detection Framework for Preliminary Diagnosis of COVID-19 via Onboard Smartphone Sensors"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1802-584X","authenticated-orcid":false,"given":"Hayat","family":"Khaloufi","sequence":"first","affiliation":[{"name":"LAROSERI Laboratory, Department of Computer Science, Faculty of Sciences, Chouaib Doukali University, El Jadida 24000, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karim","family":"Abouelmehdi","sequence":"additional","affiliation":[{"name":"LAROSERI Laboratory, Department of Computer Science, Faculty of Sciences, Chouaib Doukali University, El Jadida 24000, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abderrahim","family":"Beni-Hssane","sequence":"additional","affiliation":[{"name":"LAROSERI Laboratory, Department of Computer Science, Faculty of Sciences, Chouaib Doukali University, El Jadida 24000, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8403-1047","authenticated-orcid":false,"given":"Furqan","family":"Rustam","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2705-1823","authenticated-orcid":false,"given":"Anca Delia","family":"Jurcut","sequence":"additional","affiliation":[{"name":"School of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1209-8565","authenticated-orcid":false,"given":"Ernesto","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Broward College, Broward County, FL 33332, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8271-6496","authenticated-orcid":false,"given":"Imran","family":"Ashraf","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,15]]},"reference":[{"key":"ref_1","first-page":"3009","article-title":"Prediction Models for COVID-19 Integrating Age Groups, Gender, and Underlying Conditions","volume":"67","author":"Ashraf","year":"2021","journal-title":"Comput. 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