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

45 citations · 45 across the 1 of their papers we have counts for

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1 paper

cs.LG2020★ 45 cited

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

Shohreh Deldari, Daniel V. Smith, Amin Sadri +1

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…