Using hidden markov models to improve quantifying physical activity in accelerometer data - a simulation study.

ABSTRACT:

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 misclas

SUBMITTER: Witowski V 

PROVIDER: S-EPMC4251969 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature