ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data

TL;DR

ESPRESSO is a hybrid segmentation model for multi-dimensional time-series data that leverages entropy and shape properties, outperforming existing methods in various sensor data applications.

Contribution

The paper introduces a novel entropy and shape-aware segmentation approach with a new temporal representation and greedy search, advancing time-series segmentation techniques.

Findings

ESPRESSO outperforms four state-of-the-art methods on seven datasets.

The new WCAC representation effectively captures temporal properties.

Case studies demonstrate ESPRESSO's utility in activity and emotion inference.

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.