{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T07:34:16Z","timestamp":1778571256975,"version":"3.51.4"},"reference-count":32,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2020,7,2]],"date-time":"2020-07-02T00:00:00Z","timestamp":1593648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1DP2MH103909"],"award-info":[{"award-number":["1DP2MH103909"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Sensors"],"abstract":"<jats:p>Physical activity, such as walking and ascending stairs, is commonly used in biomedical settings as an outcome or covariate. Researchers have traditionally relied on surveys to quantify activity levels of subjects in both research and clinical settings, but surveys are subjective in nature and have known limitations, such as recall bias. Smartphones provide an opportunity for unobtrusive objective measurement of physical activity in naturalistic settings, but their data tends to be noisy and needs to be analyzed with care. We explored the potential of smartphone accelerometer and gyroscope data to distinguish between walking, sitting, standing, ascending stairs, and descending stairs. We conducted a study in which four participants followed a study protocol and performed a sequence of activities with one phone in their front pocket and another phone in their back pocket. The subjects were filmed throughout, and the obtained footage was annotated to establish moment-by-moment ground truth activity. We introduce a modified version of the so-called movelet method to classify activity type and to quantify the uncertainty present in that classification. Our results demonstrate the promise of smartphones for activity recognition in naturalistic settings, but they also highlight challenges in this field of research.<\/jats:p>","DOI":"10.3390\/s20133706","type":"journal-article","created":{"date-parts":[[2020,7,3]],"date-time":"2020-07-03T06:51:20Z","timestamp":1593759080000},"page":"3706","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Augmented Movelet Method for Activity Classification Using Smartphone Gyroscope and Accelerometer Data"],"prefix":"10.3390","volume":"20","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":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jukka-Pekka","family":"Onnela","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e16","DOI":"10.2196\/mental.5165","article-title":"New tools for new research in psychiatry: A scalable and customizable platform to empower data driven smartphone research","volume":"3","author":"Torous","year":"2016","journal-title":"JMIR Mental Health"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-019-0121-1","article-title":"Best practices for analyzing large-scale health data from wearables and smartphone apps","volume":"2","author":"Hicks","year":"2019","journal-title":"NPJ Digital Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1691","DOI":"10.1038\/npp.2016.7","article-title":"Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health","volume":"41","author":"Onnela","year":"2016","journal-title":"Neuropsychopharmacology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s10916-019-1506-z","article-title":"Mobile apps to quantify aspects of physical activity: A systematic review on its reliability and validity","volume":"44","author":"Silva","year":"2020","journal-title":"J. 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