  Cohort studies are increasingly using accelerometers for physical activity
and sedentary behavior estimation. These devices tend to be less error-prone
than self-report, can capture activity throughout the day, and are economical.
However, previous methods for estimating sedentary behavior based on hip-worn
data are often invalid or suboptimal under free-living situations and
subject-to-subject variation. In this paper, we propose a local Markov
switching model that takes this situation into account, and introduce a general
procedure for posture classification and sedentary behavior analysis that fits
the model naturally. Our method features changepoint detection methods in time
series and also a two stage classification step that labels data into 3
classes(sitting, standing, stepping). Through a rigorous training-testing
paradigm, we showed that our approach achieves > 80% accuracy. In addition, our
method is robust and easy to interpret.
