{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T05:38:38Z","timestamp":1772257118204,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T00:00:00Z","timestamp":1593993600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Smartwatch battery limitations are one of the biggest hurdles to their acceptability in the consumer market. To our knowledge, despite promising studies analyzing smartwatch battery data, there has been little research that has analyzed the battery usage of a diverse set of smartwatches in a real-world setting. To address this challenge, this paper utilizes a smartwatch dataset collected from 832 real-world users, including different smartwatch brands and geographic locations. First, we employ clustering to identify common patterns of smartwatch battery utilization; second, we introduce a transparent low-parameter convolutional neural network model, which allows us to identify the latent patterns of smartwatch battery utilization. Our model converts the battery consumption rate into a binary classification problem; i.e., low and high consumption. Our model has 85.3% accuracy in predicting high battery discharge events, outperforming other machine learning algorithms that have been used in state-of-the-art research. Besides this, it can be used to extract information from filters of our deep learning model, based on learned filters of the feature extractor, which is impossible for other models. Third, we introduce an indexing method that includes a longitudinal study to quantify smartwatch battery quality changes over time. Our novel findings can assist device manufacturers, vendors and application developers, as well as end-users, to improve smartwatch battery utilization.<\/jats:p>","DOI":"10.3390\/s20133784","type":"journal-article","created":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T11:07:42Z","timestamp":1594033662000},"page":"3784","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Understanding Smartwatch Battery Utilization in the Wild"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6695-4101","authenticated-orcid":false,"given":"Morteza","family":"Homayounfar","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Amirkabir University of Technology, Tehran 159163, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5851-6531","authenticated-orcid":false,"given":"Amirhossein","family":"Malekijoo","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, Semnan University, Semnan 35131, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7127-4031","authenticated-orcid":false,"given":"Aku","family":"Visuri","sequence":"additional","affiliation":[{"name":"Center for Ubiquitous Computing, University of Oulu, 4500 Oulu, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9420-2452","authenticated-orcid":false,"given":"Chelsea","family":"Dobbins","sequence":"additional","affiliation":[{"name":"School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane QLD 4072, Australia"}]},{"given":"Ella","family":"Peltonen","sequence":"additional","affiliation":[{"name":"Center for Ubiquitous Computing, University of Oulu, 4500 Oulu, Finland"}]},{"given":"Eugene","family":"Pinsky","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Metropolitan College, Boston University, Boston, MA 02215, USA"}]},{"given":"Kia","family":"Teymourian","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Metropolitan College, Boston University, Boston, MA 02215, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2607-1777","authenticated-orcid":false,"given":"Reza","family":"Rawassizadeh","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Metropolitan College, Boston University, Boston, MA 02215, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,6]]},"reference":[{"key":"ref_1","unstructured":"(2020, June 10). 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