{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T07:29:23Z","timestamp":1774510163605,"version":"3.50.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2018,9,25]],"date-time":"2018-09-25T00:00:00Z","timestamp":1537833600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2018,9,25]],"date-time":"2018-09-25T00:00:00Z","timestamp":1537833600000},"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":["R01HD083431"],"award-info":[{"award-number":["R01HD083431"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Science Foundation","award":["1510741"],"award-info":[{"award-number":["1510741"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2019,1]]},"DOI":"10.1007\/s10618-018-0589-3","type":"journal-article","created":{"date-parts":[[2018,9,25]],"date-time":"2018-09-25T04:41:15Z","timestamp":1537850475000},"page":"96-130","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Domain agnostic online semantic segmentation for multi-dimensional time series"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0258-7557","authenticated-orcid":false,"given":"Shaghayegh","family":"Gharghabi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chin-Chia Michael","family":"Yeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifei","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul","family":"Hibbing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samuel","family":"LaMunion","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew","family":"Kaplan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Scott E.","family":"Crouter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eamonn","family":"Keogh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,9,25]]},"reference":[{"key":"589_CR1","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1007\/s10115-016-0987-z","volume":"51","author":"S Aminikhanghahi","year":"2017","unstructured":"Aminikhanghahi S, Cook DJ (2017) A survey of methods for time series change point detection. Knowl Inf Syst 51:339\u2013367","journal-title":"Knowl Inf Syst"},{"key":"589_CR2","unstructured":"Anonymous (2018) Progress in artificial intelligence. Wikipedia"},{"key":"589_CR3","doi-asserted-by":"crossref","unstructured":"Aoki T, Lin JF-S, Kuli\u0107 D, Venture G (2016) Segmentation of human upper body movement using multiple IMU sensors. In: Engineering in medicine and biology society (EMBC), 2016 IEEE 38th annual international conference of the. IEEE, pp 3163\u20133166","DOI":"10.1109\/EMBC.2016.7591400"},{"key":"589_CR4","doi-asserted-by":"crossref","unstructured":"Bouchard D, Badler N (2007) Semantic segmentation of motion capture using laban movement analysis. In: International workshop on intelligent virtual agents. Springer, pp 37\u201344","DOI":"10.1007\/978-3-540-74997-4_4"},{"key":"589_CR5","doi-asserted-by":"crossref","unstructured":"Bregler C (1997) Learning and recognizing human dynamics in video sequences. In: 1997 IEEE Computer society conference on computer vision and pattern recognition, 1997. Proceedings, IEEE, pp 568\u2013574","DOI":"10.1109\/CVPR.1997.609382"},{"key":"589_CR6","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1123\/jpah.10.3.437","volume":"10","author":"KL Cain","year":"2013","unstructured":"Cain KL, Sallis JF, Conway TL, Van Dyck D, Calhoon L (2013) Using accelerometers in youth physical activity studies: a review of methods. J Phys Act Health 10:437\u2013450","journal-title":"J Phys Act Health"},{"key":"589_CR7","doi-asserted-by":"publisher","first-page":"2365","DOI":"10.1007\/s00024-016-1284-1","volume":"173","author":"C Cassisi","year":"2016","unstructured":"Cassisi C, Prestifilippo M, Cannata A, Montalto P, Patan\u00e8 D, Privitera E (2016) Probabilistic reasoning over seismic time series: volcano monitoring by hidden markov models at mt. etna. Pure appl Geophys 173:2365\u20132386","journal-title":"Pure appl Geophys"},{"key":"589_CR8","unstructured":"Chen Y, Keogh E, Hu B, Begum N, Bagnall A, Mueen A, Batista G Welcome to the UCR Time Series Classification\/Clustering Page. http:\/\/www.cs.ucr.edu\/~eamonn\/time_series_data\/ . Accessed 7 Sep 2018"},{"key":"589_CR9","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1016\/0002-8703(94)90561-4","volume":"127","author":"K Chuttani","year":"1994","unstructured":"Chuttani K, Tischler MD, Pandian NG, Lee RT, Mohanty PK (1994) Diagnosis of cardiac tamponade after cardiac surgery: relative value of clinical, echocardiographic, and hemodynamic signs. Am Heart J 127:913\u2013918","journal-title":"Am Heart J"},{"key":"589_CR10","doi-asserted-by":"publisher","first-page":"944","DOI":"10.1249\/MSS.0000000000000502","volume":"47","author":"SE Crouter","year":"2015","unstructured":"Crouter SE, Flynn JI, Bassett DR Jr (2015) Estimating physical activity in youth using a wrist accelerometer. Med Sci Sports Exerc 47:944","journal-title":"Med Sci Sports Exerc"},{"key":"589_CR11","doi-asserted-by":"crossref","unstructured":"Dau HA, Begum N, Keogh E (2016) Semi-supervision dramatically improves time series clustering under dynamic time warping. In: Proceedings of the 25th ACM international on conference on information and knowledge management. ACM, pp 999\u20131008","DOI":"10.1145\/2983323.2983855"},{"key":"589_CR12","unstructured":"Esteban C, Hyland SL, R\u00e4tsch G (2017) Real-valued (medical) time series generation with recurrent conditional GANs. arXiv preprint arXiv:170602633"},{"key":"589_CR13","unstructured":"Ha TM, Bunke H (1997) Off-line, handwritten numeral recognition by perturbation method. In: IEEE transactions on pattern analysis & machine intelligence, pp 535\u2013539"},{"key":"589_CR14","doi-asserted-by":"crossref","unstructured":"Hao Y, Chen Y, Zakaria J, Hu B, Rakthanmanon T, Keogh E (2013) Towards never-ending learning from time series streams. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 874\u2013 882","DOI":"10.1145\/2487575.2487634"},{"key":"589_CR15","doi-asserted-by":"crossref","unstructured":"Harguess J, Aggarwal JK (2009) Semantic labeling of track events using time series segmentation and shape analysis. In: 2009 16th IEEE international conference on image processing (ICIP), IEEE, pp 4317\u20134320","DOI":"10.1109\/ICIP.2009.5413671"},{"key":"589_CR16","doi-asserted-by":"crossref","unstructured":"Heldt T, Oefinger MB, Hoshiyama M, Mark RG (2003) Circulatory response to passive and active changes in posture. In: Computers in cardiology, 2003. IEEE, pp 263\u2013266","DOI":"10.1109\/CIC.2003.1291141"},{"key":"589_CR17","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1007\/s10618-015-0415-0","volume":"30","author":"B Hu","year":"2016","unstructured":"Hu B, Chen Y, Keogh E (2016) Classification of streaming time series under more realistic assumptions. Data Min Knowl Disc 30:403\u2013437","journal-title":"Data Min Knowl Disc"},{"key":"589_CR18","unstructured":"Keogh E (2017) Supporting website for this paper. http:\/\/www.cs.ucr.edu\/~eamonn\/FLOSS\/ . Accessed 7 Sep 2018"},{"key":"589_CR19","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1023\/A:1024988512476","volume":"7","author":"E Keogh","year":"2003","unstructured":"Keogh E, Kasetty S (2003) On the need for time series data mining benchmarks: a survey and empirical demonstration. Data Min Knowl Disc 7:349\u2013371","journal-title":"Data Min Knowl Disc"},{"key":"589_CR20","doi-asserted-by":"crossref","unstructured":"Keogh E, Chu S, Hart D, Pazzani M (2004) Segmenting time series: A survey and novel approach. In: Data mining in time series databases. World Scientific, pp 1\u201321","DOI":"10.1142\/9789812565402_0001"},{"key":"589_CR21","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.1249\/MSS.0b013e31820ce174","volume":"43","author":"S Kozey-Keadle","year":"2011","unstructured":"Kozey-Keadle S, Libertine A, Lyden K, Staudenmayer J, Freedson PS (2011) Validation of wearable monitors for assessing sedentary behavior. Med Sci Sports Exerc 43:1561\u20131567","journal-title":"Med Sci Sports Exerc"},{"key":"589_CR22","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2013.00200","author":"C Lainscsek","year":"2013","unstructured":"Lainscsek C, Hernandez ME, Weyhenmeyer J, Sejnowski TJ, Poizner H (2013) Non-linear dynamical analysis of EEG time series distinguishes patients with Parkinson\u2019s disease from healthy individuals. Front Neurol. https:\/\/doi.org\/10.3389\/fneur.2013.00200","journal-title":"Front Neurol"},{"key":"589_CR23","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/s00371-013-0902-5","volume":"31","author":"R Lan","year":"2015","unstructured":"Lan R, Sun H (2015) Automated human motion segmentation via motion regularities. Vis Comput 31:35\u201353","journal-title":"Vis Comput"},{"key":"589_CR24","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1109\/THMS.2015.2493536","volume":"46","author":"JF-S Lin","year":"2016","unstructured":"Lin JF-S, Karg M, Kuli\u0107 D (2016) Movement primitive segmentation for human motion modeling: a framework for analysis. IEEE Trans Hum Mach Syst 46:325\u2013339","journal-title":"IEEE Trans Hum Mach Syst"},{"key":"589_CR25","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1249\/MSS.0b013e3182a42a2d","volume":"46","author":"K Lyden","year":"2014","unstructured":"Lyden K, Keadle SK, Staudenmayer J, Freedson PS (2014) A method to estimate free-living active and sedentary behavior from an accelerometer. Med Sci Sports Exerc 46:386","journal-title":"Med Sci Sports Exerc"},{"key":"589_CR26","doi-asserted-by":"publisher","first-page":"12355","DOI":"10.1038\/s41598-017-12401-8","volume":"7","author":"R Machn\u00e9","year":"2017","unstructured":"Machn\u00e9 R, Murray DB, Stadler PF (2017) Similarity-based segmentation of multi-dimensional signals. Sci Rep 7:12355","journal-title":"Sci Rep"},{"key":"589_CR27","unstructured":"Maschke GW, Scalabrini GJ (2005) The lie behind the lie detector. Antipolygraph org"},{"key":"589_CR28","doi-asserted-by":"crossref","unstructured":"Matsubara Y, Sakurai Y, Faloutsos C (2014a) Autoplait: Automatic mining of co-evolving time sequences. In: Proceedings of the 2014 ACM SIGMOD international conference on Management of data. ACM, pp 193\u2013204","DOI":"10.1145\/2588555.2588556"},{"key":"589_CR29","doi-asserted-by":"crossref","unstructured":"Matsubara Y, Sakurai Y, Ueda N, Yoshikawa M (2014b) Fast and exact monitoring of co-evolving data streams. In: 2014 IEEE international conference on data mining (ICDM), IEEE, pp 390\u2013399","DOI":"10.1109\/ICDM.2014.62"},{"key":"589_CR30","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1080\/01621459.2013.849605","volume":"109","author":"DS Matteson","year":"2014","unstructured":"Matteson DS, James NA (2014) A nonparametric approach for multiple change point analysis of multivariate data. J Am Stat Assoc 109:334\u2013345","journal-title":"J Am Stat Assoc"},{"key":"589_CR31","unstructured":"Mocap.cs.cmu.edu (2017) Carnegie Mellon University\u2014CMU Graphics Lab\u2014motion capture library. http:\/\/mocap.cs.cmu.edu .\/. Accessed 7 Sep 2018"},{"key":"589_CR32","unstructured":"Mohammadian E, Noferesti M, Jalili R (2014) FAST: Fast Anonymization of Big Data Streams. In: Proceedings of the 2014 international conference on big data science and computing (BigDataScience \u201814). ACM, pp 231\u2013238"},{"key":"589_CR33","doi-asserted-by":"crossref","unstructured":"Molina JM, Garc\u00eda J, Garcia AB, Melo R, Correia L (2009) Segmentation and classification of time-series: real case studies. In: International conference on intelligent data engineering and automated learning. Springer, pp 743\u2013750","DOI":"10.1007\/978-3-642-04394-9_91"},{"key":"589_CR34","doi-asserted-by":"crossref","unstructured":"Morris D, Saponas TS, Guillory A, Kelner I (2014) RecoFit: using a wearable sensor to find, recognize, and count repetitive exercises. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 3225\u20133234","DOI":"10.1145\/2556288.2557116"},{"key":"589_CR35","unstructured":"Mu Y, Lo H, Amaral K, Ding W, Crouter SE (2013) Discriminative accelerometer patterns in children physical activities"},{"key":"589_CR36","unstructured":"Mueen A, Viswanathan K, Gupta CK, Keogh E (2015) The fastest similarity search algorithm for time series subsequences under Euclidean distance. url: www cs unm edu\/\u223c\u2009mueen\/FastestSimilaritySearch html (Accessed 24 May 2016)"},{"key":"589_CR37","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/s10163-003-0086-6","volume":"5","author":"J Nishino","year":"2003","unstructured":"Nishino J, Itoh M, Ishinomori T, Kubota N, Uemichi Y (2003) Development of a catalytic cracking process for converting waste plastics to petrochemicals. J Mater Cycles Waste Manag 5:89\u201393. https:\/\/doi.org\/10.1007\/s10163-003-0086-6","journal-title":"J Mater Cycles Waste Manag"},{"key":"589_CR38","unstructured":"Pavlovic V, Rehg JM, MacCormick J (2001) Learning switching linear models of human motion. In: Advances in neural information processing systems. pp 981\u2013987"},{"key":"589_CR39","doi-asserted-by":"crossref","unstructured":"Reinhardt A, Christin D, Darmstadt TU, Kanhere SS (2013) Predicting the power consumption of electric appliances through time series pattern matching. In: In: Proceedings of the 5th ACM workshop on embedded systems for energy-efficient buildings (BuildSys","DOI":"10.1145\/2528282.2528315"},{"key":"589_CR40","doi-asserted-by":"crossref","unstructured":"Reiss A, Stricker D (2012) Introducing a new benchmarked dataset for activity monitoring. In: 2012 16th International symposium on wearable computers. IEEE, Newcastle, United Kingdom, pp 108\u2013109","DOI":"10.1109\/ISWC.2012.13"},{"key":"589_CR41","doi-asserted-by":"publisher","first-page":"1229","DOI":"10.1109\/TMM.2014.2310701","volume":"16","author":"J Serra","year":"2014","unstructured":"Serra J, Muller M, Grosche P, Arcos JL (2014) Unsupervised music structure annotation by time series structure features and segment similarity. IEEE Trans Multimed 16:1229\u20131240. https:\/\/doi.org\/10.1109\/TMM.2014.2310701","journal-title":"IEEE Trans Multimed"},{"key":"589_CR42","doi-asserted-by":"crossref","unstructured":"Wang P, Wang H, Wang W (2011) Finding semantics in time series. In: SIGMOD\u201911 proceedings of the 2011 ACM SIGMOD. pp 385\u2013396","DOI":"10.1145\/1989323.1989364"},{"key":"589_CR43","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1681\/ASN.V871179","volume":"8","author":"ID Weiner","year":"1997","unstructured":"Weiner ID, Charles SW (1997) Hypokalemia\u2013consequences, causes, and correction. J Am Soc Nephrol 8:1179\u20131188","journal-title":"J Am Soc Nephrol"},{"key":"589_CR44","unstructured":"Crouter S, Ding W, Keogh E Novel Approaches for Predicting Unstructured Short Periods of Physical Activities in Youth. Grantome"},{"key":"589_CR45","unstructured":"Yao L, Sheng QZ, Ruan W, Li X, Wang S, Yang Z (2015) Unobtrusive posture recognition via online learning of multi\u2014dimensional RFID received signal strength. In: 2015 IEEE 21st international conference on parallel and distributed systems (ICPADS), IEEE, pp 116\u2013123"},{"key":"589_CR46","unstructured":"Yeh C-CM, Zhu Y, Ulanova L, Begum N, Ding Y, Hoang AD, Furtado Silva D, Mueen A (2016) Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. IEEE, pp 1317\u20131322"},{"key":"589_CR47","doi-asserted-by":"crossref","unstructured":"Zhao J, Itti L (2016) Decomposing time series with application to temporal segmentation. In: 2016 IEEE winter conference on applications of computer vision (WACV). pp 1\u20139","DOI":"10.1109\/WACV.2016.7477722"}],"updated-by":[{"DOI":"10.1007\/s10618-019-00618-2","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2019,2,14]],"date-time":"2019-02-14T00:00:00Z","timestamp":1550102400000}}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10618-018-0589-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-018-0589-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-018-0589-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T16:58:35Z","timestamp":1662137915000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10618-018-0589-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,25]]},"references-count":47,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,1]]}},"alternative-id":["589"],"URL":"https:\/\/doi.org\/10.1007\/s10618-018-0589-3","relation":{"correction":[{"id-type":"doi","id":"10.1007\/s10618-019-00618-2","asserted-by":"object"}]},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9,25]]},"assertion":[{"value":"26 October 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 September 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 September 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2019","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The article Domain agnostic online semantic segmentation for multi-dimensional time series, written by Shaghayegh Gharghabi, Chin-Chia Michael Yeh, Yifei Ding, Wei Ding, Paul Hibbing, Samuel LaMunion, Andrew Kaplan, Scott E. Crouter, Eamonn Keogh was originally published electronically on the publisher\ufffd\ufffd\ufffds internet portal (currently SpringerLink) on 25 September 2018 without open access.","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}}]}}