{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T23:24:39Z","timestamp":1780529079885,"version":"3.54.1"},"reference-count":32,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T00:00:00Z","timestamp":1648512000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000050","name":"National Heart Lung and Blood Institute","doi-asserted-by":"publisher","award":["U01HL145386"],"award-info":[{"award-number":["U01HL145386"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Physical activity patterns can reveal information about one\u2019s health status. Built-in sensors in a smartphone, in comparison to a patient\u2019s self-report, can collect activity recognition data more objectively, unobtrusively, and continuously. A variety of data analysis approaches have been proposed in the literature. In this study, we applied the movelet method to classify the activities performed using smartphone accelerometer and gyroscope data, which measure a phone\u2019s acceleration and angular velocity, respectively. The movelet method constructs a personalized dictionary for each participant using training data and classifies activities in new data with the dictionary. Our results show that this method has the advantages of being interpretable and transparent. A unique aspect of our movelet application involves extracting unique information, optimally, from multiple sensors. In comparison to single-sensor applications, our approach jointly incorporates the accelerometer and gyroscope sensors with the movelet method. Our findings show that combining data from the two sensors can result in more accurate activity recognition than using each sensor alone. In particular, the joint-sensor method reduces errors of the gyroscope-only method in differentiating between standing and sitting. It also reduces errors in the accelerometer-only method when classifying vigorous activities.<\/jats:p>","DOI":"10.3390\/s22072618","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T21:45:51Z","timestamp":1648590351000},"page":"2618","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Smartphone-Based Activity Recognition Using Multistream Movelets Combining Accelerometer and Gyroscope Data"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1964-5231","authenticated-orcid":false,"given":"Emily J.","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Mathematics and Statistics, Wake Forest University, Winston-Salem, NC 27106, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kebin","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6613-8668","authenticated-orcid":false,"given":"Jukka-Pekka","family":"Onnela","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Harvard University, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1001\/jamasurg.2019.4702","article-title":"Using smartphones to capture novel recovery metrics after cancer surgery","volume":"155","author":"Panda","year":"2020","journal-title":"JAMA Surg."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"American Psychiatric Association (2013). 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