title: Using hidden markov models to improve quantifying physical activity in accelerometer data - a simulation study.
abstract: BACKGROUND
  The use of accelerometers to objectively measure physical activity (PA) has become the most preferred method of choice in recent years. Traditionally, cutpoints are used to assign impulse counts recorded by the devices to sedentary and activity ranges. Here, hidden Markov models (HMM) are used to improve the cutpoint method to achieve a more accurate identification of the sequence of modes of PA.
  
  
  METHODS
  1,000 days of labeled accelerometer data have been simulated. For the simulated data the actual sedentary behavior and activity range of each count is known. The cutpoint method is compared with HMMs based on the Poisson distribution (HMM[Pois]), the generalized Poisson distribution (HMM[GenPois]) and the Gaussian distribution (HMM[Gauss]) with regard to misclassification rate (MCR), bout detection, detection of the number of activities performed during the day and runtime.
  
  
  RESULTS
  The cutpoint method had a misclassification rate (MCR) of 11% followed by HMM[Pois] with 8%, HMM[GenPois] with 3% and HMM[Gauss] having the best MCR with less than 2%. HMM[Gauss] detected the correct number of bouts in 12.8% of the days, HMM[GenPois] in 16.1%, HMM[Pois] and the cutpoint method in none. HMM[GenPois] identified the correct number of activities in 61.3% of the days, whereas HMM[Gauss] only in 26.8%. HMM[Pois] did not identify the correct number at all and seemed to overestimate the number of activities. Runtime varied between 0.01 seconds (cutpoint), 2.0 minutes (HMM[Gauss]) and 14.2 minutes (HMM[GenPois]).
  
  
  CONCLUSIONS
  Using simulated data, HMM-based methods were superior in activity classification when compared to the traditional cutpoint method and seem to be appropriate to model accelerometer data. Of the HMM-based methods, HMM[Gauss] seemed to be the most appropriate choice to assess real-life accelerometer data.
authors.affiliation: Department Biometry and Data Management, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany; Department of Mathematics and Computer Science, University of Bremen, Bremen, Germany.
authors.forename: Vitali
authors.initials: V
authors.lastname: Witowski
authors.affiliation: Department Biometry and Data Management, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany.
authors.forename: Ronja
authors.initials: R
authors.lastname: Foraita
authors.affiliation: School of Sport and Service Management, University of Brighton, Eastbourne, United Kingdom.
authors.forename: Yannis
authors.initials: Y
authors.lastname: Pitsiladis
authors.affiliation: Department Biometry and Data Management, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany; Department of Mathematics and Computer Science, University of Bremen, Bremen, Germany.
authors.forename: Iris
authors.initials: I
authors.lastname: Pigeot
authors.affiliation: Department Biometry and Data Management, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany.
authors.forename: Norman
authors.initials: N
authors.lastname: Wirsik
mesh_terms: D061725:Accelerometry / Q000379:methods*; D003198:Computer Simulation; D008390:Markov Chains; D008962:Models, Theoretical; D018670:Monitoring, Ambulatory; D009043:Motor Activity; D016012:Poisson Distribution