title: Performance of Activity Classification Algorithms in Free-Living Older Adults.
abstract: OBJECTIVE
  The objective of this study is to compare activity type classification rates of machine learning algorithms trained on laboratory versus free-living accelerometer data in older adults.
  
  
  METHODS
  Thirty-five older adults (21 females and 14 males, 70.8 ± 4.9 yr) performed selected activities in the laboratory while wearing three ActiGraph GT3X+ activity monitors (in the dominant hip, wrist, and ankle; ActiGraph, LLC, Pensacola, FL). Monitors were initialized to collect raw acceleration data at a sampling rate of 80 Hz. Fifteen of the participants also wore GT3X+ in free-living settings and were directly observed for 2-3 h. Time- and frequency-domain features from acceleration signals of each monitor were used to train random forest (RF) and support vector machine (SVM) models to classify five activity types: sedentary, standing, household, locomotion, and recreational activities. All algorithms were trained on laboratory data (RFLab and SVMLab) and free-living data (RFFL and SVMFL) using 20-s signal sampling windows. Classification accuracy rates of both types of algorithms were tested on free-living data using a leave-one-out technique.
  
  
  RESULTS
  Overall classification accuracy rates for the algorithms developed from laboratory data were between 49% (wrist) and 55% (ankle) for the SVMLab algorithms and 49% (wrist) to 54% (ankle) for the RFLab algorithms. The classification accuracy rates for SVMFL and RFFL algorithms ranged from 58% (wrist) to 69% (ankle) and from 61% (wrist) to 67% (ankle), respectively.
  
  
  CONCLUSIONS
  Our algorithms developed on free-living accelerometer data were more accurate in classifying the activity type in free-living older adults than those on our algorithms developed on laboratory accelerometer data. Future studies should consider using free-living accelerometer data to train machine learning algorithms in older adults.
authors.affiliation: 1Department of Kinesiology, University of Massachusetts, Amherst, MA; 2Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA; and 3Department of Health Sciences, Northeastern University, Boston, MA.
authors.forename: Jeffer Eidi
authors.initials: JE
authors.lastname: Sasaki
authors.forename: Amanda M
authors.initials: AM
authors.lastname: Hickey
authors.forename: John W
authors.initials: JW
authors.lastname: Staudenmayer
authors.forename: Dinesh
authors.initials: D
authors.lastname: John
authors.forename: Jane A
authors.initials: JA
authors.lastname: Kent
authors.forename: Patty S
authors.initials: PS
authors.lastname: Freedson
mesh_terms: D061725:Accelerometry / Q000295:instrumentation*; D000203:Activities of Daily Living / Q000145:classification; D000368:Aged; D000465:Algorithms; D000842:Ankle; D005260:Female; D006615:Hip; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000379:methods; D060388:Support Vector Machine; D014953:Wrist