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Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2020,9,4]]},"abstract":"<jats:p>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 differs 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-series WCAC was introduced along with a greedy search approach that estimate segments based upon the entropy metric. ESPRESSO was shown to offer 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 different 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.<\/jats:p>","DOI":"10.1145\/3411832","type":"journal-article","created":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T21:39:45Z","timestamp":1599255585000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":41,"title":["ESPRESSO"],"prefix":"10.1145","volume":"4","author":[{"given":"Shohreh","family":"Deldari","sequence":"first","affiliation":[{"name":"School of Science, RMIT University\/ Data61, CSIRO, Melbourne, VIC, Australia"}]},{"given":"Daniel V.","family":"Smith","sequence":"additional","affiliation":[{"name":"Data61, CSIRO, Hobart, TAS, Australia"}]},{"given":"Amin","family":"Sadri","sequence":"additional","affiliation":[{"name":"ANZ, Melbourne, VIC, Australia"}]},{"given":"Flora","family":"Salim","sequence":"additional","affiliation":[{"name":"School of Science, RMIT University, Melbourne, VIC, Australia"}]}],"member":"320","published-online":{"date-parts":[[2020,9,4]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Cook","author":"Aminikhanghahi Samaneh","year":"2017"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOMW.2017.7917569"},{"key":"e_1_2_2_3_1","volume-title":"Enhancing Activity Recognition Using CPD-based Activity Segmentation. 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