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

Publication Year: 2020 Publication Type : JournalArticle


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 di‚ers 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 o‚er 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 di‚erent 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},
    publisher={ACM New York, NY, USA}


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