{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T11:49:32Z","timestamp":1775303372885,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,12,10]],"date-time":"2018-12-10T00:00:00Z","timestamp":1544400000000},"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>Human activity recognition (HAR) through sensors embedded in smartphones has allowed for the development of systems that are capable of detecting and monitoring human behavior. However, such systems have been affected by the high consumption of computational resources (e.g., memory and processing) needed to effectively recognize activities. In addition, existing HAR systems are mostly based on supervised classification techniques, in which the feature extraction process is done manually, and depends on the knowledge of a specialist. To overcome these limitations, this paper proposes a new method for recognizing human activities based on symbolic representation algorithms. The method, called \u201cMultivariate Bag-Of-SFA-Symbols\u201d (MBOSS), aims to increase the efficiency of HAR systems and maintain accuracy levels similar to those of conventional systems based on time and frequency domain features. The experiments conducted on three public datasets showed that MBOSS performed the best in terms of accuracy, processing time, and memory consumption.<\/jats:p>","DOI":"10.3390\/s18124354","type":"journal-article","created":{"date-parts":[[2018,12,10]],"date-time":"2018-12-10T03:36:41Z","timestamp":1544413001000},"page":"4354","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["MBOSS: A Symbolic Representation of Human Activity Recognition Using Mobile Sensors"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0550-4748","authenticated-orcid":false,"given":"Kevin G.","family":"Montero Quispe","sequence":"first","affiliation":[{"name":"Computer Institute, Federal University of Amazonas, Manaus 69080-900, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9669-1659","authenticated-orcid":false,"given":"Wesllen","family":"Sousa Lima","sequence":"additional","affiliation":[{"name":"Computer Institute, Federal University of Amazonas, Manaus 69080-900, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Mac\u00eado Batista","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of S\u00e3o Paulo, S\u00e3o Paulo 05508-090, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eduardo","family":"Souto","sequence":"additional","affiliation":[{"name":"Computer Institute, Federal University of Amazonas, Manaus 69080-900, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cook, D.J., and Krishnan, N.C. 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