IOT Wearable Data Fever Analysis - COVID Detection
Scripts
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Scope And Content | "iot_covid_analysis" is the root folder that has "data","docs" and "src" subfolders. As the name suggests "data" has database files, "docs" has document files and "src" has source files. From the root the"data" folder has 2 other subfolders those are "pickles" and "timescaledb". "timescaledb" supposed to have source-data when the pipeline is in offline mode. However due to data sharing restriction prof. Ben suggested keeping this folder empty. output artifacts for each run have been stored in "pickles" with a timestamp. From the root, the "docs" have documents like metadata,final_report, and presentation From the root, the "src" folder contains the entire source code with the below structure. "components" which has all python sources for the pipeline, and "dashboard" has dashboard application files. From the root, "dashboard" has dashboard apps From the root, "README.md" From the root "launch_pipeline_vi_env.sh" to launch pipeline in virtual environment. |
Technical Details |
pickle5==0.0.11 |
- Collection
- Cite This Work
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Krishan, Rajaraman ; Mahalingam, Sasikumar ; Varadharajan, Swetha Maithreyi ; Mamidala, Venu ; Bansal, Yogesh; Smarr, Benjamin (2021). IOT Wearable Data Fever Analysis - COVID Detection. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0KD1XSQ
- Description
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TemPredict launched in March 2020 as a collaboration between UCSF, UCSD, and Finnish wearable company Oura. The objective is to identify physiological signals from the wearable and provide early alerts for symptoms and diagnoses for COVID infection. The study's first phase ended on Nov 30th, 2020, with ~65,000 active participants. These participants shared data from their Oura ring from January 2020, answering onboarding, daily and monthly surveys about demographics, symptoms, and diagnoses, and other relevant information. Oura ring collects the person’s physiological data like heart rate, respiratory rate, skin temperature, metabolic equivalent of tasks. This data is stored, managed, and analyzed at SDSC. Our study focuses on developing an architecture that supports the different systematic exploration of approaches and performs comparison between them. We are analyzing the data, extracting new features, and building various algorithms that can be used for the early detection of COVID-19. In order to detect the onset of infection, we define a healthy window for each individual. This healthy window is derived by analyzing the daily rhythm of the physiological signals for every individual. In order to avoid false detection, the model calculates a dynamic baseline for each individual. Higher order features like ratios of temperature and activity, heart rate and its variability, deviations from the baseline, etc., are identified. Various ML models like Random Forest, XGBoost, CWT, Adaboost were trained, tested, and evaluated.
- Scope And Content
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Due to data restrictions in "Timescale DB of sdsc.edu", no input or output files have been provided, however, if the user has Nautilus environment access then this source will work from there. Please contact Prof. Benjamin Smarr regarding input and output files.
- Creation Date
- 2021-01 to 2021-06
- Date Issued
- 2021
- Advisor
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- Language
- English
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- License
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Creative Commons Attribution 4.0 International Public License
- Rights Holder
- Krishan, Rajaraman; Mahalingam, Sasikumar; Varadharajan, Swetha Maithreyi; Mamidala, Venu; Bansal, Yogesh
- Copyright
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Under copyright (US)
Use: This work is available from the UC San Diego Library. This digital copy of the work is intended to support research, teaching, and private study.
Constraint(s) on Use: This work is protected by the U.S. Copyright Law (Title 17, U.S.C.). Use of this work beyond that allowed by "fair use" or any license applied to this work requires written permission of the copyright holder(s). Responsibility for obtaining permissions and any use and distribution of this work rests exclusively with the user and not the UC San Diego Library. Inquiries can be made to the UC San Diego Library program having custody of the work.
- Digital Object Made Available By
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Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp)
- Last Modified
2024-07-18