English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Extracting informative and meaningful temporal segments from high-dimensionalwearable sensor data, smart devices, or IoT data is a vital preprocessing stepin applications such as Human Activity Recognition (HAR), trajectoryprediction, gesture recognition, and lifelogging. In this paper, we proposeESPRESSO (Entropy and ShaPe awaRe timE-Series SegmentatiOn), a hybridsegmentation model for multi-dimensional time-series that is formulated toexploit the entropy and temporal shape properties of time-series. ESPRESSOdiffers from existing methods that focus upon particular statistical ortemporal properties of time-series exclusively. As part of model development, anovel temporal representation of time-series $WCAC$ was introduced along with agreedy search approach that estimate segments based upon the entropy metric.ESPRESSO was shown to offer superior performance to four state-of-the-artmethods across seven public datasets of wearable and wear-free sensing. Inaddition, we undertake a deeper investigation of these datasets to understandhow ESPRESSO and its constituent methods perform with respect to differentdataset characteristics. Finally, we provide two interesting case-studies toshow how applying ESPRESSO can assist in inferring daily activity routines andthe emotional state of humans.

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