title: Activity classification using the GENEA: optimum sampling frequency and number of axes.
abstract: BACKGROUND
  The GENEA shows high accuracy for classification of sedentary, household, walking, and running activities when sampling at 80 Hz on three axes. It is not known whether it is possible to decrease this sampling frequency and/or the number of axes without detriment to classification accuracy. The purpose of this study was to compare the classification rate of activities on the basis of data from a single axis, two axes, and three axes, with sampling rates ranging from 5 to 80 Hz.
  
  
  METHODS
  Sixty participants (age, 49.4 yr (6.5 yr); BMI, 24.6 kg·m (3.4 kg·m)) completed 10-12 semistructured activities in the laboratory and outdoor environment while wearing a GENEA accelerometer on the right wrist. We analyzed data from single axis, dual axes, and three axes at sampling rates of 5, 10, 20, 40, and 80 Hz. Mathematical models based on features extracted from mean, SD, fast Fourier transform, and wavelet decomposition were built, which combined one of the numbers of axes with one of the sampling rates to classify activities into sedentary, household, walking, and running.
  
  
  RESULTS
  Classification accuracy was high irrespective of the number of axes for data collected at 80 Hz (96.93% ± 0.97%), 40 Hz (97.4% ± 0.73%), 20 Hz (96.86% ± 1.12%), and 10 Hz (97.01% ± 1.01%) but dropped for data collected at 5 Hz (94.98% ± 1.36%).
  
  
  CONCLUSIONS
  Sampling frequencies >10 Hz and/or more than one axis of measurement were not associated with greater classification accuracy. Lower sampling rates and measurement of a single axis would result in a lower data load, longer battery life, and higher efficiency of data processing. Further research should investigate whether a lower sampling rate and a single axis affects classification accuracy when considering a wider range of activities.
authors.affiliation: Unilever Discover, Colworth, England, United Kingdom.
authors.forename: Shaoyan
authors.initials: S
authors.lastname: Zhang
authors.forename: Peter
authors.initials: P
authors.lastname: Murray
authors.forename: Ruediger
authors.initials: R
authors.lastname: Zillmer
authors.forename: Roger G
authors.initials: RG
authors.lastname: Eston
authors.forename: Michael
authors.initials: M
authors.lastname: Catt
authors.forename: Alex V
authors.initials: AV
authors.lastname: Rowlands
mesh_terms: D000054:Acceleration; D056044:Actigraphy / Q000295:instrumentation / Q000592:standards*; D000328:Adult; D000368:Aged; D000465:Algorithms; D004739:England; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009043:Motor Activity / Q000502:physiology*; D012420:Running / Q000145:classification*; D057185:Sedentary Behavior; D011795:Surveys and Questionnaires; D016138:Walking / Q000145:classification*