Open AccessDynamic classification of personal microenvironments using a suite of wearable, low-cost sensors
Author(s) -
Casey Quinn,
G. Brooke Anderson,
Sheryl Magzamen,
Charles S. Henry,
John Volckens
Publication year - 2020
Publication title -
journal of exposure science and environmental epidemiology/journal of exposure science and environmental epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.155
H-Index - 92
eISSN - 1559-064X
pISSN - 1559-0631
DOI - 10.1038/s41370-019-0198-2
Subject(s) - wearable computer , computer science , global positioning system , real time computing , air quality index , air pollution , work (physics) , sampling (signal processing) , simulation , environmental science , embedded system , telecommunications , engineering , geography , meteorology , ecology , mechanical engineering , detector , biology
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.