title: Hip and Wrist Accelerometer Algorithms for Free-Living Behavior Classification.
abstract: OBJECTIVE
  Accelerometers are a valuable tool for objective measurement of physical activity (PA). Wrist-worn devices may improve compliance over standard hip placement, but more research is needed to evaluate their validity for measuring PA in free-living settings. Traditional cut-point methods for accelerometers can be inaccurate and need testing in free living with wrist-worn devices. In this study, we developed and tested the performance of machine learning (ML) algorithms for classifying PA types from both hip and wrist accelerometer data.
  
  
  METHODS
  Forty overweight or obese women (mean age = 55.2 ± 15.3 yr; BMI = 32.0 ± 3.7) wore two ActiGraph GT3X+ accelerometers (right hip, nondominant wrist; ActiGraph, Pensacola, FL) for seven free-living days. Wearable cameras captured ground truth activity labels. A classifier consisting of a random forest and hidden Markov model classified the accelerometer data into four activities (sitting, standing, walking/running, and riding in a vehicle). Free-living wrist and hip ML classifiers were compared with each other, with traditional accelerometer cut points, and with an algorithm developed in a laboratory setting.
  
  
  RESULTS
  The ML classifier obtained average values of 89.4% and 84.6% balanced accuracy over the four activities using the hip and wrist accelerometer, respectively. In our data set with average values of 28.4 min of walking or running per day, the ML classifier predicted average values of 28.5 and 24.5 min of walking or running using the hip and wrist accelerometer, respectively. Intensity-based cut points and the laboratory algorithm significantly underestimated walking minutes.
  
  
  CONCLUSIONS
  Our results demonstrate the superior performance of our PA-type classification algorithm, particularly in comparison with traditional cut points. Although the hip algorithm performed better, additional compliance achieved with wrist devices might justify using a slightly lower performing algorithm.
authors.affiliation: 1Department of Electrical and Computer Engineering, University of California, San Diego, CA; 2Department of Family Medicine and Public Health, University of California, San Diego, CA; and 3Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA.
authors.forename: Katherine
authors.initials: K
authors.lastname: Ellis
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Kerr
authors.forename: Suneeta
authors.initials: S
authors.lastname: Godbole
authors.forename: John
authors.initials: J
authors.lastname: Staudenmayer
authors.forename: Gert
authors.initials: G
authors.lastname: Lanckriet
mesh_terms: D061725:Accelerometry / Q000295:instrumentation*; D000203:Activities of Daily Living / Q000145:classification; D000328:Adult; D000368:Aged; D000465:Algorithms; D015444:Exercise; D005260:Female; D006615:Hip; D006801:Humans; D000069550:Machine Learning; D008390:Markov Chains; D008875:Middle Aged; D018670:Monitoring, Ambulatory / Q000379:methods; D009765:Obesity; D050177:Overweight; D012680:Sensitivity and Specificity; D014953:Wrist