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Contact-based temperature, breathing and cough patterns dataset for early COVID-19 symptoms identification

Ali, Omer; Ishak, Mohamad Khairi; Bhatti, Muhammad Kamran Liaquat


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{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<p>This release features the dataset recorded using our prototype hardware device (contact-based) that collects accelerometer and temperature readings to investigate breathing patterns, personal activity and cough patterns. As fever and cough are considered as two of the most common symptoms for COVID-19, our aim was to focus on these physiological features. In addition, research also states that all COVID-19 contractions showed elevated breathing patterns in patients, that could be easily identifiable from Eupnea state. Therefore, in this research project, we aimed at:<br>\n1. Designing a prototype chest-worn device to measure dynamic chest movements (to record breathing and cough patterns)<br>\n2. Detect different activity patterns<br>\n3. Record temperature variations during idle and active stage<br>\n4. Using unsupervised machine learning algorithm to detect anomalies by creating a composite score for (breathing and cough patterns as well as temperature).&nbsp;</p>\n\n<p>This release also features some code examples that were used for data pre-processing, feature identification and anomaly detection using (K-means and DBSCAN algorithms).&nbsp;</p>\n\n<p>It is important to note that this dataset should be considered and further investigated for preliminary exploratory analysis. The data was collected from healthy adults (that did not undergo COVID-19 clinical screening tests). Therefore, it must be clearly identified that this dataset DOES NOT represent positive COVID-19 contractions.&nbsp;</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Universiti Sains Malaysia", 
      "@type": "Person", 
      "name": "Ali, Omer"
    }, 
    {
      "affiliation": "Universiti Sains Malaysia", 
      "@type": "Person", 
      "name": "Ishak, Mohamad Khairi"
    }, 
    {
      "affiliation": "Department of Electrical Engineering, NFC Institute of Engineering and Technology (NFC IET), Multan, Pakistan", 
      "@type": "Person", 
      "name": "Bhatti, Muhammad Kamran Liaquat"
    }
  ], 
  "url": "https://zenodo.org/record/4537822", 
  "datePublished": "2021-02-12", 
  "version": "1.1", 
  "keywords": [
    "covid-19", 
    "dataset", 
    "anomaly detection", 
    "feature extraction"
  ], 
  "@context": "https://schema.org/", 
  "distribution": [
    {
      "contentUrl": "https://zenodo.org/api/files/94fbe8b7-12b5-4772-9e58-74ce7d109e89/Patient-COVID-19.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }
  ], 
  "identifier": "https://doi.org/10.5281/zenodo.4537822", 
  "@id": "https://doi.org/10.5281/zenodo.4537822", 
  "@type": "Dataset", 
  "name": "Contact-based temperature, breathing and cough patterns dataset for early COVID-19 symptoms identification"
}
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