ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for
processing heterogeneous sensor data
- URL: http://arxiv.org/abs/2008.03230v1
- Date: Fri, 24 Jul 2020 10:41:20 GMT
- Title: ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for
processing heterogeneous sensor data
- Authors: Shohreh Deldari, Daniel V. Smith, Amin Sadri, Flora D. Salim
- Abstract summary: We propose ESPRESSO, a hybrid segmentation model for multi-dimensional time-series.
ESPRESSO exploits the entropy and temporal shape properties of time-series.
It offers superior performance to four state-of-the-art methods across seven public datasets of wearable and wear-free sensing.
- Score: 5.142415132534397
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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