  Extracting informative and meaningful temporal segments from high-dimensional
wearable sensor data, smart devices, or IoT data is a vital preprocessing step
in applications such as Human Activity Recognition (HAR), trajectory
prediction, gesture recognition, and lifelogging. In this paper, we propose
ESPRESSO (Entropy and ShaPe awaRe timE-Series SegmentatiOn), a hybrid
segmentation model for multi-dimensional time-series that is formulated to
exploit the entropy and temporal shape properties of time-series. ESPRESSO
differs from existing methods that focus upon particular statistical or
temporal properties of time-series exclusively. As part of model development, a
novel temporal representation of time-series $WCAC$ was introduced along with a
greedy search approach that estimate segments based upon the entropy metric.
ESPRESSO was shown to offer superior performance to four state-of-the-art
methods across seven public datasets of wearable and wear-free sensing. In
addition, we undertake a deeper investigation of these datasets to understand
how ESPRESSO and its constituent methods perform with respect to different
dataset characteristics. Finally, we provide two interesting case-studies to
show how applying ESPRESSO can assist in inferring daily activity routines and
the emotional state of humans.
