Dynamic Classification of Personal Microenvironments Using a Suite of Wearable, Low-Cost Sensors

Details

• Personal Author:
  Anderson, G. Brooke ; Henry, Charles S. ; Magzamen, Sheryl ; Quinn, Casey ; Volckens, John
• Description:
  Human exposure to air pollution is associated with increased risk of morbidity and mortality. However, personal air pollution exposures can vary substantially depending on an individual's daily activity patterns and air quality within their residence and workplace. This work developed and validated an adaptive buffer size (ABS) algorithm capable of dynamically classifying an individual's time spent in predefined microenvironments using data from global positioning systems (GPS), motion sensors, temperature sensors, and light sensors. Twenty-two participants in Fort Collins, CO were recruited to carry a personal air sampler for a 48-h period. The personal sampler was retrofitted with a GPS and a pushbutton to complement the existing sensor measurements (temperature, motion, light). The pushbutton was used in conjunction with a traditional time-activity diary to note when the participant was located at "home", "work", or within an "other" microenvironment. The ABS algorithm predicted the amount of time spent in each microenvironment with a median accuracy of 99.1%, 98.9%, and 97.5% for the "home", "work", and "other" microenvironments. The ability to classify microenvironments dynamically in real time can enable the development of new sampling and measurement technologies that classify personal exposure by microenvironment. ErratumJ Expo Sci Environ Epidemiol 2020 Nov; 30(6):1032. The original version of this article featured an incorrect supplementary figure file. This error has been rectified in the PDF and HTML versions of this article. [Description provided by NIOSH]
• Subjects:
  Air Pollution Environmental Exposure Environmental Monitoring Geographic Information Systems Occupational Health Safety Workplace
• Keywords:
  Humans; Air pollution; Exposure assessment; Wearable technologies; Sensors; Detectors; Monitors; Air samplers; Air sampling equipment; Measurement equipment; Algorithms; Air quality monitoring; Environmental factors; Work environment; Activity levels; Temperature measurement; Light emission; Equipment testing; Performance tests; Environmental exposure; Information processing; Geographic information systems; Environmental monitoring
• ISSN:
  1559-0631
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Colorado ; OSHA Region 8
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  9 pdf pages
• Volume:
  30
• NIOSHTIC Number:
  nn:20061782
• Citation:
  J Expo Sci Environ Epidemiol 2020 Nov; 30(6):962-970
• Contact Point Address:
  John Volckens, Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO 80523, USA
• Email:
  john.volckens@colostate.edu
• Federal Fiscal Year:
  2021
• Performing Organization:
  Colorado State University, Fort Collins
• Peer Reviewed:
  True
• Start Date:
  20150901
• Source Full Name:
  Journal of Exposure Science and Environmental Epidemiology
• End Date:
  20190831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:109e609dc3ef750329e4577b70fe8aa6a98ab167f5b3a605ebee447059ff2bcdae483061d813932ae6f3284bcf901f708fae887b56311be43b25a62094389ffa
• Download URL:
  https://stacks.cdc.gov/view/cdc/231549/cdc_231549_DS1.pdf
• File Type:
  [PDF - 1.66 MB ]