title: Refining Time-Activity Classification of Human Subjects Using the Global Positioning System.
abstract: BACKGROUND
  Detailed spatial location information is important in accurately estimating personal exposure to air pollution. Global Position System (GPS) has been widely used in tracking personal paths and activities. Previous researchers have developed time-activity classification models based on GPS data, most of them were developed for specific regions. An adaptive model for time-location classification can be widely applied to air pollution studies that use GPS to track individual level time-activity patterns.
  
  
  METHODS
  Time-activity data were collected for seven days using GPS loggers and accelerometers from thirteen adult participants from Southern California under free living conditions. We developed an automated model based on random forests to classify major time-activity patterns (i.e. indoor, outdoor-static, outdoor-walking, and in-vehicle travel). Sensitivity analysis was conducted to examine the contribution of the accelerometer data and the supplemental spatial data (i.e. roadway and tax parcel data) to the accuracy of time-activity classification. Our model was evaluated using both leave-one-fold-out and leave-one-subject-out methods.
  
  
  RESULTS
  Maximum speeds in averaging time intervals of 7 and 5 minutes, and distance to primary highways with limited access were found to be the three most important variables in the classification model. Leave-one-fold-out cross-validation showed an overall accuracy of 99.71%. Sensitivities varied from 84.62% (outdoor walking) to 99.90% (indoor). Specificities varied from 96.33% (indoor) to 99.98% (outdoor static). The exclusion of accelerometer and ambient light sensor variables caused a slight loss in sensitivity for outdoor walking, but little loss in overall accuracy. However, leave-one-subject-out cross-validation showed considerable loss in sensitivity for outdoor static and outdoor walking conditions.
  
  
  CONCLUSIONS
  The random forests classification model can achieve high accuracy for the four major time-activity categories. The model also performed well with just GPS, road and tax parcel data. However, caution is warranted when generalizing the model developed from a small number of subjects to other populations.
authors.affiliation: Program in Public Health, College of Health Sciences, University of California Irvine, Irvine, California, United States of America.|State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, China.|Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China.
authors.forename: Maogui
authors.initials: M
authors.lastname: Hu
authors.affiliation: Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, Texas, United States of America.
authors.forename: Wei
authors.initials: W
authors.lastname: Li
authors.affiliation: Program in Public Health, College of Health Sciences, University of California Irvine, Irvine, California, United States of America.|State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, China.
authors.forename: Lianfa
authors.initials: L
authors.lastname: Li
authors.affiliation: Department of Planning, Policy and Design, School of Social Ecology, University of California Irvine, Irvine, California, United States of America.
authors.forename: Douglas
authors.initials: D
authors.lastname: Houston
authors.affiliation: Program in Public Health, College of Health Sciences, University of California Irvine, Irvine, California, United States of America.
authors.forename: Jun
authors.initials: J
authors.lastname: Wu
mesh_terms: D061725:Accelerometry; D000203:Activities of Daily Living; D000328:Adult; D000397:Air Pollution / Q000032:analysis; D002947:Cities; D004781:Environmental Exposure / Q000032:analysis; D040362:Geographic Information Systems; D006801:Humans; D015233:Models, Statistical; D062206:Spatial Analysis; D013997:Time Factors; D016138:Walking