{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T07:04:52Z","timestamp":1774767892563,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T00:00:00Z","timestamp":1639699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100016714","name":"University of Sharjah","doi-asserted-by":"publisher","award":["CoV19-0207"],"award-info":[{"award-number":["CoV19-0207"]}],"id":[{"id":"10.13039\/100016714","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Physiological measures, such as heart rate variability (HRV) and beats per minute (BPM), can be powerful health indicators of respiratory infections. HRV and BPM can be acquired through widely available wrist-worn biometric wearables and smartphones. Successive abnormal changes in these indicators could potentially be an early sign of respiratory infections such as COVID-19. Thus, wearables and smartphones should play a significant role in combating COVID-19 through the early detection supported by other contextual data and artificial intelligence (AI) techniques. In this paper, we investigate the role of the heart measurements (i.e., HRV and BPM) collected from wearables and smartphones in demonstrating early onsets of the inflammatory response to the COVID-19. The AI framework consists of two blocks: an interpretable prediction model to classify the HRV measurements status (as normal or affected by inflammation) and a recurrent neural network (RNN) to analyze users\u2019 daily status (i.e., textual logs in a mobile application). Both classification decisions are integrated to generate the final decision as either \u201cpotentially COVID-19 infected\u201d or \u201cno evident signs of infection\u201d. We used a publicly available dataset, which comprises 186 patients with more than 3200 HRV readings and numerous user textual logs. The first evaluation of the approach showed an accuracy of 83.34 \u00b1 1.68% with 0.91, 0.88, 0.89 precision, recall, and F1-Score, respectively, in predicting the infection two days before the onset of the symptoms supported by a model interpretation using the local interpretable model-agnostic explanations (LIME).<\/jats:p>","DOI":"10.3390\/s21248424","type":"journal-article","created":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T02:40:32Z","timestamp":1639968032000},"page":"8424","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Wearable Devices, Smartphones, and Interpretable Artificial Intelligence in Combating COVID-19"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4981-3649","authenticated-orcid":false,"given":"Haytham","family":"Hijazi","sequence":"first","affiliation":[{"name":"Department of Informatics Engineering, CISUC-Centre for Informatics and Systems of the University of Coimbra, University of Coimbra, P-3030-790 Coimbra, Portugal"},{"name":"Intelligent Systems Department, Palestine Ahliya University, Bethlehem P-150-199, Palestine"}]},{"given":"Manar","family":"Abu Talib","sequence":"additional","affiliation":[{"name":"College of Computing and Informatics, University of Sharjah, Sharjah P-27272, United Arab Emirates"}]},{"given":"Ahmad","family":"Hasasneh","sequence":"additional","affiliation":[{"name":"Department of Natural, Engineering, and Technology Sciences, Arab American University, Ramallah P-600-699, Palestine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1570-0897","authenticated-orcid":false,"given":"Ali","family":"Bou Nassif","sequence":"additional","affiliation":[{"name":"College of Computing and Informatics, University of Sharjah, Sharjah P-27272, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9404-6410","authenticated-orcid":false,"given":"Nafisa","family":"Ahmed","sequence":"additional","affiliation":[{"name":"College of Computing and Informatics, University of Sharjah, Sharjah P-27272, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2837-3402","authenticated-orcid":false,"given":"Qassim","family":"Nasir","sequence":"additional","affiliation":[{"name":"College of Computing and Informatics, University of Sharjah, Sharjah P-27272, United Arab Emirates"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.ijid.2020.01.009","article-title":"The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health\u2014The latest 2019 novel coronavirus outbreak in Wuhan, China","volume":"91","author":"Hui","year":"2020","journal-title":"Int. J. Infect. Dis."},{"key":"ref_2","unstructured":"(2020, June 19). World Health Organization: WHO Director-General\u2019s Opening Remarks at the Media Briefing on COVID-19. Available online: https:\/\/www.who.int\/dg\/speeches\/detail\/who-director-general-sopening-remarks-at-the-media-briefing-on-covid-19-15-june-2020."},{"key":"ref_3","unstructured":"World Health Organization (2021, December 01). COVID-19 Weekly Epidemiological Update. Available online: https:\/\/www.who.int\/publications\/m\/item\/weekly-operational-update-on-covid-19-30-november-2021."},{"key":"ref_4","unstructured":"World Health Organization (2021, November 23). Laboratory Testing for Coronavirus Disease 2019 (COVID-19) in Suspected Human Cases: Interim Guidance. Available online: https:\/\/apps.who.int\/iris\/handle\/10665\/331329."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6096","DOI":"10.1007\/s00330-021-07715-1","article-title":"A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)","volume":"31","author":"Wang","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5088","DOI":"10.1038\/s41467-020-18685-1","article-title":"development and evaluation of an artificial intelligence system for COVID-19 diagnosis","volume":"11","author":"Jin","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Fusco, R., Grassi, R., Granata, V., Setola, S.V., Grassi, F., Cozzi, D., Pecori, B., Izzo, F., and Petrillo, A. (2021). Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment. J. Pers. Med., 11.","DOI":"10.3390\/jpm11100993"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"17472","DOI":"10.3390\/s131217472","article-title":"Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges","volume":"13","author":"Banaee","year":"2013","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"21640","DOI":"10.1038\/s41598-020-78355-6","article-title":"Feasibility of continuous fever monitoring using wearable devices","volume":"10","author":"Smarr","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e26107","DOI":"10.2196\/26107","article-title":"Use of physiological data from a wearable device to identify SARS-CoV-2 infection and symptoms and predict COVID-19 diagnosis: Observational study","volume":"23","author":"Hirten","year":"2021","journal-title":"J. Med. Internet Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e34","DOI":"10.1093\/milmed\/usaa405","article-title":"Heart Rate Variability as a Possible Predictive Marker for Acute Inflammatory Response in COVID-19 Patients","volume":"186","author":"Hasty","year":"2020","journal-title":"Mil. Med."},{"key":"ref_12","first-page":"5557582","article-title":"C-Reactive Protein as a Prognostic Indicator in COVID-19 Patients","volume":"2021","author":"Bayani","year":"2021","journal-title":"Interdiscip. Perspect. Infect. Dis."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.medmal.2020.03.007","article-title":"C-reactive protein levels in the early stage of COVID-19","volume":"50","author":"Wang","year":"2020","journal-title":"Med. Mal. Infect."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/1755-7682-7-28","article-title":"Ventricular trigeminy in a patient with serologically confirmed dengue haemorrhagic fever","volume":"7","author":"Matthias","year":"2014","journal-title":"Int. Arch. Med."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Howard, J., and Gugger, S. (2020). Fastai: A Layered API for Deep Learning. Information, 11.","DOI":"10.3390\/info11020108"},{"key":"ref_16","unstructured":"Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., and Shpanskaya, K. (2017). Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Channa, A., Popescu, N., Skibinska, J., and Burget, R. (2021). The Rise of Wearable Devices during the COVID-19 Pandemic: A Systematic Review. Sensors, 21.","DOI":"10.3390\/s21175787"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e2128534","DOI":"10.1001\/jamanetworkopen.2021.28534","article-title":"Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset","volume":"4","author":"Grzesiak","year":"2021","journal-title":"JAMA Netw. Open"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Marinsek, N., Shapiro, A., Clay, I., Bradshaw, B., Ramirez, E., Min, J., Trister, A., Wang, Y., Althoff, T., and Foschini, L. (2020). Measuring COVID-19 and Influenza in the Real World via Person-Generated Health Data. medRxiv, 1\u201323.","DOI":"10.1101\/2020.05.28.20115964"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6152041","DOI":"10.1155\/2020\/6152041","article-title":"Learning from Large-Scale Wearable Device Data for Predicting the Epidemic Trend of COVID-19","volume":"2020","author":"Zhu","year":"2020","journal-title":"Discret. Dyn. Nat. Soc."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mishra, T., Wang, M., Metwally, A.A., Bogu, G.K., Brooks, A.W., Bahmani, A., Alavi, A., Celli, A., Higgs, E., and Dagan-Rosenfeld, O. (2020). Early Detection Of COVID-19 Using A Smartwatch. medRxiv.","DOI":"10.1101\/2020.07.06.20147512"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1021\/acssensors.1c00312","article-title":"Detecting COVID-19 from Breath: A Game Changer for a Big Challenge","volume":"6","author":"Giovannini","year":"2021","journal-title":"ACS Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Fedorin, I., Slyusarenko, K., and Nastenko, M. (2020, January 12\u201317). Respiratory events screening using consumer smartwatches. Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers, Virtual, Mexico.","DOI":"10.1145\/3410530.3414399"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Miller, D.J., Capodilupo, J.V., Lastella, M., Sargent, C., Roach, G.D., Lee, V.H., and Capodilupo, E.R. (2020). Analyzing changes in respiratory rate to predict the risk of COVID-19 infection. PLoS ONE, 15.","DOI":"10.1101\/2020.06.18.20131417"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1038\/s41591-020-1123-x","article-title":"Wearable sensor data and self-reported symptoms for COVID-19 detection","volume":"27","author":"Quer","year":"2020","journal-title":"Nat. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1038\/s41928-020-00533-1","article-title":"Wearable devices for the detection of COVID-19","volume":"4","author":"Ates","year":"2021","journal-title":"Nat. Electron."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.jmii.2020.04.005","article-title":"Continuous temperature monitoring by a wearable device for early detection of febrile events in the SARS-CoV-2 outbreak in Taiwan, 2020","volume":"53","author":"Chung","year":"2020","journal-title":"J. Microbiol. Immunol. Infect."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hassantabar, S., Stefano, N., Ghanakota, V., Ferrari, A., Nicola, G.N., Bruno, R., Marino, I.R., Hamidouche, K., and Jha, N.K. (2020). CovidDeep: SARS-CoV-2\/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks. arXiv.","DOI":"10.1109\/TCE.2021.3130228"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ashraf, M.U., Hannan, A., Cheema, S.M., Ali, Z., and Alofi, A. (2020, January 12\u201313). Detection and tracking contagion using IoT-edge technologies: Confronting COVID-19 pandemic. Proceedings of the 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Istanbul, Turkey.","DOI":"10.1109\/ICECCE49384.2020.9179284"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JTEHM.2021.3058841","article-title":"Rapid Screening of Physiological Changes Associated With COVID-19 Using Soft-Wearables and Structured Activities: A Pilot Study","volume":"9","author":"Lonini","year":"2021","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"100588","DOI":"10.1016\/j.imu.2021.100588","article-title":"IoT based wearable device to monitor the signs of quarantined remote patients of COVID-19","volume":"24","author":"Hussain","year":"2021","journal-title":"Informatics Med. Unlocked"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e20","DOI":"10.1016\/j.jamcollsurg.2021.08.057","article-title":"Autonomic Dysfunction in COVID-19: Early Detection and Prediction Using Heart Rate Variability","volume":"233","author":"Khalpey","year":"2021","journal-title":"J. Am. Coll. Surg."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ponomarev, A., Tyapochkin, K., Surkova, E., Smorodnikova, E., and Pravdin, P. (2021). Heart Rate Variability as a Prospective Predictor of Early COVID-19 Symptoms. medRxiv.","DOI":"10.1101\/2021.07.02.21259891"},{"key":"ref_34","unstructured":"(2021, November 10). COVID-19 and Wearables Open Data Research. [Data Set]. Available online: https:\/\/github.com\/Welltory\/hrv-covid19."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Pinheiro, N., Couceiro, R., Henriques, J., Muehlsteff, J., Quintal, I., Goncalves, L., and Carvalho, P. (2016, January 16\u201320). Can PPG be used for HRV analysis?. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7591347"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Bhowmik, T., Dey, J., and Tiwari, V.N. (2017, January 11\u201315). A novel method for accurate estimation of HRV from smartwatch PPG signals. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju, Korea.","DOI":"10.1109\/EMBC.2017.8036774"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"101794","DOI":"10.1016\/j.media.2020.101794","article-title":"Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning","volume":"65","author":"Minaee","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_38","first-page":"e9448","article-title":"Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning","volume":"12","author":"Cohen","year":"2020","journal-title":"Cureus"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1038\/s41746-020-0226-6","article-title":"Investigating sources of inaccuracy in wearable optical heart rate sensors","volume":"3","author":"Bent","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"ref_40","first-page":"145","article-title":"Nonlinear Methods in Heart Rate Variability: Can they Distinguish between Nonpathological and Pathological Subjects?","volume":"25","author":"Hagmair","year":"2015","journal-title":"SNE Simul. Notes Eur."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.bbi.2019.03.009","article-title":"Heart rate variability and inflammation: A meta-analysis of human studies","volume":"80","author":"Williams","year":"2019","journal-title":"Brain, Behav. Immun."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1111\/j.1540-8159.2009.02175.x","article-title":"Comparison of time-domain short-term heart interval variability analysis using a wrist-worn heart rate monitor and the conventional electrocardiogram","volume":"32","author":"Porto","year":"2009","journal-title":"Pacing Clin. Electrophysiol."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Freedman, D.A. (2005). Statistical Models: Theory and Practice, Cambridge University Press.","DOI":"10.1017\/CBO9781139165495"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s00521-013-1368-0","article-title":"A review of feature selection methods based on mutual information","volume":"24","author":"Vergara","year":"2013","journal-title":"Neural Comput. Appl."},{"key":"ref_45","unstructured":"Merity, S., Keskar, N.S., and Socher, R. (2017). Regularizing and optimizing LSTM language models. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Tanwar, G., Chauhan, R., Singh, M., and Singh, D. (2020). Pre-Emption of Affliction Severity Using HRV Measurements from a Smart Wearable; Case-Study on SARS-Cov-2 Symptoms. Sensors, 20.","DOI":"10.3390\/s20247068"},{"key":"ref_47","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016). Model-agnostic interpretability of machine learning. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"630038","DOI":"10.3389\/fphys.2021.630038","article-title":"Alteration of autonomic nervous system is associated with severity and outcomes in patients with COVID-19","volume":"12","author":"Pan","year":"2021","journal-title":"Front. Physiol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/24\/8424\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:50:32Z","timestamp":1760169032000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/24\/8424"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,17]]},"references-count":48,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["s21248424"],"URL":"https:\/\/doi.org\/10.3390\/s21248424","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,17]]}}}