title: Energy Expenditure Prediction Using Raw Accelerometer Data in Simulated Free Living.
abstract: OBJECTIVE
  The purpose of this study was to develop, validate, and compare energy expenditure (EE) prediction models for accelerometers placed on the hip, thigh, and wrists using simple accelerometer features as input variables in EE prediction models.
  
  
  METHODS
  Forty-four healthy adults participated in a 90-min, semistructured, simulated free-living activity protocol. During the protocol, participants engaged in 14 different sedentary, ambulatory, lifestyle, and exercise activities for 3-10 min each. Participants chose the order, duration, and intensity of activities. Four accelerometers were worn (right hip, right thigh, as well as right and left wrists) to predict EE compared with that measured by the criterion measure (portable metabolic analyzer). Artificial neural networks (ANNs) were created to predict EE from each accelerometer using a leave-one-out cross-validation approach. Accuracy of the ANN was evaluated using Pearson correlations, root mean square error, and bias. Several ANNs were developed using different input features to determine those most relevant for use in the models.
  
  
  RESULTS
  The ANNs for all four accelerometers achieved high measurement accuracy, with correlations of r > 0.80 for predicting EE. The thigh accelerometer provided the highest overall accuracy (r = 0.90) and lowest root mean square error (1.04 METs), and the differences between the thigh and the other monitors were more pronounced when fewer input variables were used in the predictive models. None of the predictive models had an overall bias for prediction of EE.
  
  
  CONCLUSIONS
  A single accelerometer placed on the thigh provided the highest accuracy for EE prediction, although monitors worn on the wrists or hip can also be used with high measurement accuracy.
authors.affiliation: 1Human Performance Laboratory, Ball State University, Muncie, IN; 2Department of Kinesiology, Michigan State University, East Lansing, MI; and 3Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI.
authors.forename: Alexander H K
authors.initials: AH
authors.lastname: Montoye
authors.forename: Lanay M
authors.initials: LM
authors.lastname: Mudd
authors.forename: Subir
authors.initials: S
authors.lastname: Biswas
authors.forename: Karin A
authors.initials: KA
authors.lastname: Pfeiffer
mesh_terms: D061725:Accelerometry / Q000295:instrumentation; D000293:Adolescent; D000328:Adult; D004734:Energy Metabolism; D005260:Female; D005544:Forecasting; D006801:Humans; D008297:Male; D008824:Michigan; D018670:Monitoring, Ambulatory / Q000592:standards*; D009043:Motor Activity / Q000502:physiology; D016571:Neural Networks, Computer; D055815:Young Adult