title: Predicting human movement with multiple accelerometers using movelets.
abstract: OBJECTIVE
  The study aims were 1) to develop transparent algorithms that use short segments of training data for predicting activity types and 2) to compare the prediction performance of the proposed algorithms using single accelerometers and multiple accelerometers.
  
  
  METHODS
  Sixteen participants (age, 80.6 yr (4.8 yr); body mass index, 26.1 kg·m (2.5 kg·m)) performed 15 lifestyle activities in the laboratory, each wearing three accelerometers at the right hip and left and right wrists. Triaxial accelerometry data were collected at 80 Hz using ActiGraph GT3X+. Prediction algorithms were developed, which, instead of extracting features, build activity-specific dictionaries composed of short signal segments called movelets. Three alternative approaches were proposed to integrate the information from the multiple accelerometers.
  
  
  RESULTS
  With at most several seconds of training data per activity, the prediction accuracy at the second-level temporal resolution was very high for lying, standing, normal/fast walking, and standing up from a chair (the median prediction accuracy ranged from 88.2% to 99.9% on the basis of the single-accelerometer movelet approach). For these activities, wrist-worn accelerometers performed almost as well as hip-worn accelerometers (the median difference in accuracy between wrist and hip ranged from -2.7% to 5.8%). Modest improvements in prediction accuracy were achieved by integrating information from multiple accelerometers.
  
  
  CONCLUSIONS
  It is possible to achieve high prediction accuracy at the second-level temporal resolution with very limited training data. To increase prediction accuracy from the simultaneous use of multiple accelerometers, a careful selection of integrative approaches is required.
authors.affiliation: 1Department of Biostatistics, The Johns Hopkins University, Baltimore, MD; 2Department of Social Medicine, University of Maastricht, Maastricht, THE NETHERLANDS; 3Institute of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, DENMARK; 4Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA; and 5Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, MD.
authors.forename: Bing
authors.initials: B
authors.lastname: He
authors.forename: Jiawei
authors.initials: J
authors.lastname: Bai
authors.forename: Vadim V
authors.initials: VV
authors.lastname: Zipunnikov
authors.forename: Annemarie
authors.initials: A
authors.lastname: Koster
authors.forename: Paolo
authors.initials: P
authors.lastname: Caserotti
authors.forename: Brittney
authors.initials: B
authors.lastname: Lange-Maia
authors.forename: Nancy W
authors.initials: NW
authors.lastname: Glynn
authors.forename: Tamara B
authors.initials: TB
authors.lastname: Harris
authors.forename: Ciprian M
authors.initials: CM
authors.lastname: Crainiceanu
mesh_terms: D061725:Accelerometry / Q000295:instrumentation / Q000379:methods*; D000203:Activities of Daily Living; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D005260:Female; D006801:Humans; D008297:Male; D009043:Motor Activity / Q000502:physiology*; D011187:Posture / Q000502:physiology; D011237:Predictive Value of Tests; D016138:Walking / Q000502:physiology