title: Field evaluation of a random forest activity classifier for wrist-worn accelerometer data.
abstract: OBJECTIVE
  Wrist-worn accelerometers are convenient to wear and associated with greater wear-time compliance. Previous work has generally relied on choreographed activity trials to train and test classification models. However, validity in free-living contexts is starting to emerge. Study aims were: (1) train and test a random forest activity classifier for wrist accelerometer data; and (2) determine if models trained on laboratory data perform well under free-living conditions.
  
  
  METHODS
  Twenty-one participants (mean age=27.6±6.2) completed seven lab-based activity trials and a 24h free-living trial (N=16).
  
  
  METHODS
  Participants wore a GENEActiv monitor on the non-dominant wrist. Classification models recognising four activity classes (sedentary, stationary+, walking, and running) were trained using time and frequency domain features extracted from 10-s non-overlapping windows. Model performance was evaluated using leave-one-out-cross-validation. Models were implemented using the randomForest package within R. Classifier accuracy during the 24h free living trial was evaluated by calculating agreement with concurrently worn activPAL monitors.
  
  
  RESULTS
  Overall classification accuracy for the random forest algorithm was 92.7%. Recognition accuracy for sedentary, stationary+, walking, and running was 80.1%, 95.7%, 91.7%, and 93.7%, respectively for the laboratory protocol. Agreement with the activPAL data (stepping vs. non-stepping) during the 24h free-living trial was excellent and, on average, exceeded 90%. The ICC for stepping time was 0.92 (95% CI=0.75-0.97). However, sensitivity and positive predictive values were modest. Mean bias was 10.3min/d (95% LOA=-46.0 to 25.4min/d).
  
  
  CONCLUSIONS
  The random forest classifier for wrist accelerometer data yielded accurate group-level predictions under controlled conditions, but was less accurate at identifying stepping verse non-stepping behaviour in free living conditions Future studies should conduct more rigorous field-based evaluations using observation as a criterion measure.
authors.affiliation: School of Exercise and Nutrition Sciences, Queensland University of Technology, Australia; School of Human Movement and Nutrition Sciences, The University of Queensland, Australia. Electronic address: toby.pavey@qut.edu.au.
authors.forename: Toby G
authors.initials: TG
authors.lastname: Pavey
authors.affiliation: School of Human Movement and Nutrition Sciences, The University of Queensland, Australia.
authors.forename: Nicholas D
authors.initials: ND
authors.lastname: Gilson
authors.affiliation: School of Human Movement and Nutrition Sciences, The University of Queensland, Australia.
authors.forename: Sjaan R
authors.initials: SR
authors.lastname: Gomersall
authors.affiliation: School of Public Health, The University of Queensland, Australia.
authors.forename: Bronwyn
authors.initials: B
authors.lastname: Clark
authors.affiliation: School of Exercise and Nutrition Sciences, Queensland University of Technology, Australia.
authors.forename: Stewart G
authors.initials: SG
authors.lastname: Trost
mesh_terms: D061725:Accelerometry / Q000379:methods*; D000328:Adult; D000465:Algorithms; D015444:Exercise; D005260:Female; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000379:methods*; D011897:Random Allocation; D012420:Running; D057185:Sedentary Behavior; D012680:Sensitivity and Specificity; D016138:Walking; D014953:Wrist; D055815:Young Adult
keywords: Accelerometer; Physical activity; Random forest classifier; Wrist