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ESPRESSO: Entropy and Shape Aware Time Series Segmentation for Processing Heterogeneous Sensor Data
Shohreh Deldari, Daniel Smith, Amin Sadri, Flora D Salim

UbiComp '20: The ACM International Joint Conference on Pervasive and Ubiquitous Computing 2020
Session: Human Activity Recognition II

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.ESPRESSOwasshown 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.

DOI:: https://doi.org/10.1145/3411832
WEB:: https://ubicomp.org/ubicomp2020/

Remote Presentations for ACM International Joint Conference on Pervasive and Ubiquitous Computing 2020 (UbiComp '20)