DOI: 10.5061/dryad.tq0gt
title: Data from: Using hidden Markov models to improve quantifying physical activity in accelerometer data – a simulation study

description: 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.
description: Generated Accelerometer
 data for Simulation Study (Format RData)Each R-File
 (Day1-Day1000) contains the list object "data" which contains
 the complete generated data for a day. The simulations were designed to
 reflect free living-environment accelerometer observations obtained for
 children. 1,000 different time series with the length of T=1,440 and an
 epoch length of 15 seconds were simulated, each representing 6 hours of
 counts per day. Counts per day were randomly generated using the negative
 binomial distribution (with parameters r=1 and p=0.0009, resulting in the
 lowest PA-level μ_1=111.11) and the Gaussian distribution (with the
 parameters μ_2=400, μ_3=600 and μ_4=900, with σ_2^2=σ_3^2=σ_4^2=10,000)
 around three or four pre-defined physical activity levels (depending on
 the random time series generated by a Markov chain).Format
 RData.zipGenerated Accelerometer
 data for Simulation Study (Format rds)Each R-File
 (Day1-Day1000) contains the list object "data" which contains
 the complete generated data for a day. The simulations were designed to
 reflect free living-environment accelerometer observations obtained for
 children. 1,000 different time series with the length of T=1,440 and an
 epoch length of 15 seconds were simulated, each representing 6 hours of
 counts per day. Counts per day were randomly generated using the negative
 binomial distribution (with parameters r=1 and p=0.0009, resulting in the
 lowest PA-level μ_1=111.11) and the Gaussian distribution (with the
 parameters μ_2=400, μ_3=600 and μ_4=900, with σ_2^2=σ_3^2=σ_4^2=10,000)
 around three or four pre-defined physical activity levels (depending on
 the random time series generated by a Markov chain).Format
 rds.zip