title: Objective Assessment of Physical Activity: Classifiers for Public Health.
abstract: OBJECTIVE
  Walking for health is recommended by health agencies, partly based on epidemiological studies of self-reported behaviors. Accelerometers are now replacing survey data, but it is not clear that intensity-based cut points reflect the behaviors previously reported. New computational techniques can help classify raw accelerometer data into behaviors meaningful for public health.
  
  
  METHODS
  Five hundred twenty days of triaxial 30-Hz accelerometer data from three studies (n = 78) were employed as training data. Study 1 included prescribed activities completed in natural settings. The other two studies included multiple days of free-living data with SenseCam-annotated ground truth. The two populations in the free-living data sets were demographically and physical different. Random forest classifiers were trained on each data set, and the classification accuracy on the training data set and that applied to the other available data sets were assessed. Accelerometer cut points were also compared with the ground truth from the three data sets.
  
  
  RESULTS
  The random forest classified all behaviors with over 80% accuracy. Classifiers developed on the prescribed data performed with higher accuracy than the free-living data classifier, but these did not perform as well on the free-living data sets. Many of the observed behaviors occurred at different intensities compared with those identified by existing cut points.
  
  
  CONCLUSIONS
  New machine learning classifiers developed from prescribed activities (study 1) were considerably less accurate when applied to free-living populations or to a functionally different population (studies 2 and 3). These classifiers, developed on free-living data, may have value when applied to large cohort studies with existing hip accelerometer data.
authors.affiliation: 1Department of Family Medicine and Public Health, University of California, San Diego, CA; 2Department of Computer Science and Engineering, University of California, San Diego, CA; and 3University of Massachusetts, Amherst, MA.
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Kerr
authors.forename: Ruth E
authors.initials: RE
authors.lastname: Patterson
authors.forename: Katherine
authors.initials: K
authors.lastname: Ellis
authors.forename: Suneeta
authors.initials: S
authors.lastname: Godbole
authors.forename: Eileen
authors.initials: E
authors.lastname: Johnson
authors.forename: Gert
authors.initials: G
authors.lastname: Lanckriet
authors.forename: John
authors.initials: J
authors.lastname: Staudenmayer
mesh_terms: D061725:Accelerometry / Q000379:methods*; D000203:Activities of Daily Living / Q000145:classification; D000328:Adult; D000368:Aged; D000465:Algorithms; D001642:Bicycling; D015444:Exercise; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D018670:Monitoring, Ambulatory / Q000379:methods*; D009765:Obesity; D050177:Overweight; D011634:Public Health