{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:58:15Z","timestamp":1760241495989,"version":"build-2065373602"},"reference-count":77,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,3,29]],"date-time":"2018-03-29T00:00:00Z","timestamp":1522281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Authoring protocols for manual tasks such as following recipes, manufacturing processes or laboratory experiments requires significant effort. This paper presents a system that estimates individual procedure transitions from the user\u2019s physical movement and gestures recorded with inertial motion sensors. Combined with egocentric or external video recordings, this facilitates efficient review and annotation of video databases. We investigate different clustering algorithms on wearable inertial sensor data recorded on par with video data, to automatically create transition marks between task steps. The goal is to match these marks to the transitions given in a description of the workflow, thus creating navigation cues to browse video repositories of manual work. To evaluate the performance of unsupervised algorithms, the automatically-generated marks are compared to human expert-created labels on two publicly-available datasets. Additionally, we tested the approach on a novel dataset in a manufacturing lab environment, describing an existing sequential manufacturing process. The results from selected clustering methods are also compared to some supervised methods.<\/jats:p>","DOI":"10.3390\/informatics5020016","type":"journal-article","created":{"date-parts":[[2018,3,29]],"date-time":"2018-03-29T12:51:56Z","timestamp":1522327916000},"page":"16","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Detecting Transitions in Manual Tasks from Wearables: An Unsupervised Labeling Approach"],"prefix":"10.3390","volume":"5","author":[{"given":"Sebastian","family":"B\u00f6ttcher","sequence":"first","affiliation":[{"name":"Epilepsy Center, Department of Neurosurgery, Medical Center\u2014University of Freiburg, 79106 Freiburg, Germany"}]},{"given":"Philipp","family":"Scholl","sequence":"additional","affiliation":[{"name":"Embedded Systems Group, Computer Science Institute, University of Freiburg, 79110 Freiburg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5296-5347","authenticated-orcid":false,"given":"Kristof","family":"Van Laerhoven","sequence":"additional","affiliation":[{"name":"Ubiquitous Computing Group, Faculty of Science and Technology, University of Siegen, 57076 Siegen, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2018,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"B\u00f6ttcher, S., Scholl, P.M., and Laerhoven, K.V. 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