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 diers 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-seriesWCAC was introduced along with a greedy search approach that estimate segments based upon the entropy metric. ESPRESSO was shown to oer 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 dierent 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.
@article{deldari2020espresso,
title={ESPRESSO: entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data},
author={Deldari, Shohreh and Smith, Daniel V and Sadri, Amin and Salim, Flora},
journal={Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
volume={4},
number={3},
pages={1--24},
year={2020},
publisher={ACM New York, NY, USA}
}
© 2021 Flora Salim - CRUISE Research Group.