ESPRESSO

ESPRESSO (Entropy and ShaPe awaRe timE-Series SegmentatiOn) is 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. ESPRESSO has been used to extract meaningful temporal segments from high-dimensional wearable sensor data, smart devices, or IoT data as a vital preprocessing step for Human Activity Recognition (HAR), trajectory prediction, gesture recognition, and lifelogging, and smart cities. Available in matlab and python( comming soon).

Shohreh Deldari, Daniel V. Smith, Amin Sadri, Flora D. Salim

Link to the paper : https://arxiv.org/abs/2008.03230

Presented at UbiComp/ISWC 2020 : Teaser , Presentation

Bibtex

If you find this code or the paper useful, please consider citing:

@inproceedings{deldari2020espresso,
    title={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 D. },
    journal={Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)},
    volume={4},
    number={3},
    articleno={77},
    year={2020},
    url = {https://doi.org/10.1145/3411832},
    doi = {10.1145/3411832}
}