witowski1.pdf 413,04KB
1000 Titel
  • Using hidden Markov models to improve quantifying physical activity in accelerometer data – A simulation study
1000 Autor/in
  1. Witowski, Vitali |
  2. Foraita, Ronja |
  3. Pitsiladis, Yannis |
  4. Pigeot, Iris |
  5. Wirsik, Norman |
1000 Erscheinungsjahr 2014
1000 Art der Datei
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2014-12-02
1000 Erschienen in
1000 Quellenangabe
  • 9(12):e114089
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2014
1000 Lizenz
1000 Verlagsversion
  • |
  • |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • INTRODUCTION: 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.
1000 Sacherschließung
lokal hidden Markov models
lokal simulation and modeling
lokal bioenergetics
lokal sports
lokal probability distribution
lokal accelerometers
lokal Markov models
lokal physical activity
1000 Fachgruppe
  1. Medizin |
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
1000 Dateien
1000 Objektart article
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