  In a nutshell, stay-points are locations that a person has stopped for some
amount of time. Previous work depends mainly on stay-point identification
methods using experimentally fine tuned threshold values. These behave well on
their experimental datasets but may exhibit reduced performance on other
datasets.
  In this work, we demonstrate the potential of a geometry-based method for
stay-point extraction. This is accomplished by transforming the user's
trajectory path to a two-dimensional discrete time series curve that in turn
transforms the stay-points to the local minima of the first derivative of this
curve.
  To demonstrate the soundness of the proposed method, we evaluated it on raw,
noisy trajectory data acquired over the period of 28 different days using four
different techniques. The results demonstrate, among others, that given a good
trajectory tracking technique, we can identify correctly 86% to 98% of the
stay-points.
