Dynamic classification of personal microenvironments using a suite of wearable, low-cost sensors.

ABSTRACT: 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 push

SUBMITTER: Quinn C 

PROVIDER: S-EPMC7358126 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature