[ { "id": "https://authors.library.caltech.edu/records/f7bc9-z2g18", "eprint_id": 114583, "eprint_status": "archive", "datestamp": "2023-08-20 05:51:54", "lastmod": "2023-10-24 15:02:54", "type": "book_section", "metadata_visibility": "show", "creators": { "items": [ { "id": "Lubecke-Lana-C", "name": { "family": "Lubecke", "given": "Lana C." } }, { "id": "Ishmael-Khaldoon", "name": { "family": "Ishmael", "given": "Khaldoon" }, "orcid": "0000-0001-6003-7284" }, { "id": "Zheng-Yao", "name": { "family": "Zheng", "given": "Yao" }, "orcid": "0000-0003-2820-1034" }, { "id": "Bori\u0107-Lubecke-Olga", "name": { "family": "Bori\u0107-Lubecke", "given": "Olga" }, "orcid": "0000-0003-2877-7359" }, { "id": "Lubecke-Victor-M", "name": { "family": "Lubecke", "given": "Victor M." }, "orcid": "0000-0001-8407-3554" } ] }, "title": "Identification of COVID-19 Type Respiratory Disorders Using Channel State Analysis of Wireless Communications Links", "ispublished": "unpub", "full_text_status": "public", "note": "\u00a9 IEEE 2021. This article is free to access and download, along with rights for full text and data mining, re-use and analysis.\n\nThis work was supported in part by the U.S. National Science\nFoundation under Grant IIS1915738. K. Ishmael is supported by a DoD SMART scholarship. \n\nThe authors would like to thank all the members of the\nWireless and Biosensing labs at the University of Hawaii\nManoa who helped with these experiments.\n\n
", "abstract": "One deadly aspect of COVID-19 is that those infected can often be contagious before exhibiting overt symptoms. While methods such as temperature checks and sinus swabs have aided with early detection, the former does not always provide a reliable indicator of COVID-19, and the latter is invasive and requires significant human and material resources to administer. This paper presents a non-invasive COVID-19 early screening system implementable with commercial off-the-shelf wireless communications devices. The system leverages the Doppler radar principle to monitor respiratory-related chest motion and identifies breathing rates that indicate COVID-19 infection. A prototype was developed from software-defined radios (SDRs) designed for 5G NR wireless communications and system performance was evaluated using a robotic mover simulating human breathing, and using actual breathing, resulting in a consistent respiratory rate accuracy better than one breath per minute, exceeding that used in common medical practice. \n\nClinical Relevance\u2014This establishes the potential efficacy of wireless communications based radar for recognizing respiratory disorders such as COVID-19.", "date": "2021-11", "date_type": "published", "publisher": "IEEE", "place_of_pub": "Piscataway, NJ", "pagerange": "7582-7585", "id_number": "CaltechAUTHORS:20220504-115246900", "isbn": "978-1-7281-1179-7", "book_title": "2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)", "official_url": "https://resolver.caltech.edu/CaltechAUTHORS:20220504-115246900", "rights": "No commercial reproduction, distribution, display or performance rights in this work are provided.", "funders": { "items": [ { "agency": "NSF", "grant_number": "IIS-1915738" }, { "agency": "Department of Defense SMART Scholarship" } ] }, "local_group": { "items": [ { "id": "COVID-19" } ] }, "doi": "10.1109/embc46164.2021.9630016", "primary_object": { "basename": "Identification_of_COVID-19_Type_Respiratory_Disorders_Using_Channel_State_Analysis_of_Wireless_Communications_Links.pdf", "url": "https://authors.library.caltech.edu/records/f7bc9-z2g18/files/Identification_of_COVID-19_Type_Respiratory_Disorders_Using_Channel_State_Analysis_of_Wireless_Communications_Links.pdf" }, "pub_year": "2021", "author_list": "Lubecke, Lana C.; Ishmael, Khaldoon; et el." }, { "id": "https://authors.library.caltech.edu/records/s9xxr-33c73", "eprint_id": 113043, "eprint_status": "archive", "datestamp": "2023-08-19 23:50:35", "lastmod": "2023-10-23 22:54:02", "type": "book_section", "metadata_visibility": "show", "creators": { "items": [ { "id": "Jin-Qixuan", "name": { "family": "Jin", "given": "Qixuan" } } ] }, "title": "Time Warping Clustering for the Forecast and Analysis of COVID-19", "ispublished": "unpub", "full_text_status": "restricted", "keywords": "COVID-19, dynamic time warping, clustering", "note": "\u00a9 2020 IEEE. \n\nThe major sponsor of this research is the Clinard Innovation Fund. \n\nThis work was part of the CS156 Model project at Caltech\n(http://cs156.caltech.edu). We thank Yaser Abu-Mostafa for\nthe supervision and revision of this work. We thank Amanda\nLi and Tynesha Pham for their support and discussion during\nthe CS156b competition. We thank Dominic Yurk for the\nimproved Hampel filter.", "abstract": "This paper presents an effective algorithm for the clustering of confirmed COVID-19 cases at the county-level in the United States. Dynamic time warping and Euclidean distance are examined as the k-means clustering distance metrics. Dynamic time warping can compare time series varying in speed, as counties often experience similar outbreak trends without the timelines matching up exactly. The effect of data preprocessing on clustering was systematically studied. Further analyses demonstrate the immediate value of our clusters for both retrospective interpretation of the pandemic and as informative inputs for case prediction models. We visualize the time progression of COVID-19 from April 5, 2020 to August 23, 2020. We proposed a Monte-Carlo dropout feedforward neural network with the ability to forecast four weeks into the future. Predictions evaluated from July 24, 2020 to August 20, 2020 demonstrate the better empirical performance of the model when trained on the clusters, in comparison with the model trained on individual counties and the model trained on counties clustered by state.", "date": "2020-10-09", "date_type": "published", "publisher": "IEEE", "place_of_pub": "Piscataway, NJ", "pagerange": "1-5", "id_number": "CaltechAUTHORS:20220121-88735100", "isbn": "978-1-7281-7571-3", "book_title": "2020 IEEE MIT Undergraduate Research Technology Conference (URTC)", "official_url": "https://resolver.caltech.edu/CaltechAUTHORS:20220121-88735100", "rights": "No commercial reproduction, distribution, display or performance rights in this work are provided.", "funders": { "items": [ { "agency": "Clinard Innovation Fund" } ] }, "local_group": { "items": [ { "id": "COVID-19" } ] }, "doi": "10.1109/urtc51696.2020.9668904", "pub_year": "2020", "author_list": "Jin, Qixuan" } ]