{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:49:49Z","timestamp":1773802189796,"version":"3.50.1"},"reference-count":36,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2020,9,30]],"date-time":"2020-09-30T00:00:00Z","timestamp":1601424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100002920","name":"Research Grants Council, University Grants Committee","doi-asserted-by":"crossref","award":["C5026-18G"],"award-info":[{"award-number":["C5026-18G"]}],"id":[{"id":"10.13039\/501100002920","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Key Research and Development Program of China","award":["2018YFB1004801"],"award-info":[{"award-number":["2018YFB1004801"]}]},{"name":"Serbian Ministry of Science and Education","award":["TR32054"],"award-info":[{"award-number":["TR32054"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Comput. Healthcare"],"published-print":{"date-parts":[[2020,10,31]]},"abstract":"<jats:p>\n            A successive similar pattern (SSP) is a series of similar sequences that occur consecutively at non-regular intervals in time series. Mining SSPs could provide valuable information without\n            <jats:italic>a priori<\/jats:italic>\n            knowledge, which is crucial in many applications ranging from health monitoring to activity recognition. However, most existing work is computationally expensive, focuses only on periodic patterns occurring in regular time intervals, and is unable to recognize patterns containing multiple periods. Here we investigate a more general problem of finding similar patterns occurring successively, in which the similarity between patterns is measured by the\n            <jats:italic>z<\/jats:italic>\n            -normalized Euclidean distance. We propose a linear time, robust method, called\n            <jats:italic>Multiple-length Successive sIMilar PAtterns Detector<\/jats:italic>\n            (mSIMPAD), that mines SSPs of multiple lengths, making no assumptions regarding periodicity. We apply our method on the detection of repetitive movement using a wearable inertial measurement unit. The experiments were conducted on three public datasets, two of which contain simple walking and idle data, whereas the third is more complex and contains multiple activities. mSIMPAD achieved F-score improvements of 3.2% and 6.5%, respectively, over the simple and complex datasets compared to the state-of-the-art walking detector. In addition, mSIMPAD is scalable and applicable to real-time applications since it operates in linear time complexity.\n          <\/jats:p>","DOI":"10.1145\/3396250","type":"journal-article","created":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T12:32:12Z","timestamp":1594125132000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["mSIMPAD"],"prefix":"10.1145","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3882-063X","authenticated-orcid":false,"given":"Chun-Tung","family":"Li","sequence":"first","affiliation":[{"name":"The Hong Kong Polytechnic University, Kowloon, Hong Kong, China"}]},{"given":"Jiannong","family":"Cao","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Kowloon, Hong Kong, China"}]},{"given":"Xue","family":"Liu","sequence":"additional","affiliation":[{"name":"McGill University, Montreal, Quebec, Canada"}]},{"given":"Milos","family":"Stojmenovic","sequence":"additional","affiliation":[{"name":"Singidunum University, Belgrade, Serbia"}]}],"member":"320","published-online":{"date-parts":[[2020,9,30]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of ESANN","author":"Anguita Davide","year":"2013","unstructured":"Davide Anguita , Alessandro Ghio , Luca Oneto , Xavier Parra , and Jorge Luis Reyes-Ortiz . 2013 . A public domain dataset for human activity recognition using smartphones . In Proceedings of ESANN 2013. Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, and Jorge Luis Reyes-Ortiz. 2013. A public domain dataset for human activity recognition using smartphones. In Proceedings of ESANN 2013."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0735-1097(86)80478-8"},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of IEEE EMBS 2006","author":"Barralon P.","year":"2006","unstructured":"P. Barralon , N. Vuillerme , and N. Noury . 2006. Walk detection with a kinematic sensor: Frequency and wavelet comparison . In Proceedings of IEEE EMBS 2006 . DOI:https:\/\/doi.org\/10.1109\/iembs. 2006 .260770 10.1109\/iembs.2006.260770 P. Barralon, N. Vuillerme, and N. Noury. 2006. Walk detection with a kinematic sensor: Frequency and wavelet comparison. In Proceedings of IEEE EMBS 2006. DOI:https:\/\/doi.org\/10.1109\/iembs.2006.260770"},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of ACM UbiComp","author":"Brajdic Agata","year":"2013","unstructured":"Agata Brajdic and Robert Harle . 2013 . Walk detection and step counting on unconstrained smartphones . In Proceedings of ACM UbiComp 2013. ACM, New York, NY, 225--234. DOI:https:\/\/doi.org\/10.1145\/2493432.2493449 10.1145\/2493432.2493449 Agata Brajdic and Robert Harle. 2013. Walk detection and step counting on unconstrained smartphones. In Proceedings of ACM UbiComp 2013. ACM, New York, NY, 225--234. DOI:https:\/\/doi.org\/10.1145\/2493432.2493449"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2016.2628346"},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of ACM SIGKDD","author":"Dau Hoang Anh","year":"2017","unstructured":"Hoang Anh Dau and Eamonn Keogh . 2017 . Matrix Profile V: A generic technique to incorporate domain knowledge into motif discovery . In Proceedings of ACM SIGKDD 2017. ACM, New York, NY, 125--134. Hoang Anh Dau and Eamonn Keogh. 2017. Matrix Profile V: A generic technique to incorporate domain knowledge into motif discovery. In Proceedings of ACM SIGKDD 2017. ACM, New York, NY, 125--134."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-018-0589-3"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bti789"},{"key":"e_1_2_1_9_1","volume-title":"Proceedings of IEEE INFOCOM 2017","author":"Guo Xiaonan","year":"2017","unstructured":"Xiaonan Guo , Jian Liu , and Yingying Chen . 2017 . FitCoach: Virtual fitness coach empowered by wearable mobile devices . In Proceedings of IEEE INFOCOM 2017 . DOI:https:\/\/doi.org\/10.1109\/infocom.2017.8057208 10.1109\/infocom.2017.8057208 Xiaonan Guo, Jian Liu, and Yingying Chen. 2017. FitCoach: Virtual fitness coach empowered by wearable mobile devices. In Proceedings of IEEE INFOCOM 2017. DOI:https:\/\/doi.org\/10.1109\/infocom.2017.8057208"},{"key":"e_1_2_1_10_1","volume-title":"Proceedings of ACM UbiComp","author":"Hao Tian","year":"2015","unstructured":"Tian Hao , Guoliang Xing , and Gang Zhou . 2015 . RunBuddy: A smartphone system for running rhythm monitoring . In Proceedings of ACM UbiComp 2015. ACM, New York, NY, 133--144. Tian Hao, Guoliang Xing, and Gang Zhou. 2015. RunBuddy: A smartphone system for running rhythm monitoring. In Proceedings of ACM UbiComp 2015. ACM, New York, NY, 133--144."},{"key":"e_1_2_1_11_1","volume-title":"Proceedings of IEEE INFOCOM","author":"Huang Qianyi","year":"2016","unstructured":"Qianyi Huang , Yan Mei , Wei Wang , and Qian Zhang . 2016 . Battery-free sensing platform for wearable devices: The synergy between two feet . In Proceedings of IEEE INFOCOM 2016. Qianyi Huang, Yan Mei, Wei Wang, and Qian Zhang. 2016. Battery-free sensing platform for wearable devices: The synergy between two feet. In Proceedings of IEEE INFOCOM 2016."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/1964897.1964918"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-007-0064-z"},{"key":"e_1_2_1_14_1","volume-title":"Proceedings of ACM SIGMOD2018","author":"Linardi Michele","year":"2018","unstructured":"Michele Linardi , Yan Zhu , Themis Palpanas , and Eamonn Keogh . 2018 . Matrix Profile X: VALMOD\u2014Scalable discovery of variable-length motifs in data series . In Proceedings of ACM SIGMOD2018 . DOI:https:\/\/doi.org\/10.1145\/3183713.3183744 10.1145\/3183713.3183744 Michele Linardi, Yan Zhu, Themis Palpanas, and Eamonn Keogh. 2018. Matrix Profile X: VALMOD\u2014Scalable discovery of variable-length motifs in data series. In Proceedings of ACM SIGMOD2018. DOI:https:\/\/doi.org\/10.1145\/3183713.3183744"},{"key":"e_1_2_1_15_1","volume-title":"An Efficient Orientation Filter for Inertial and Inertial\/Magnetic Sensor Arrays","author":"Madgwick Sebastian","unstructured":"Sebastian Madgwick . 2010. An Efficient Orientation Filter for Inertial and Inertial\/Magnetic Sensor Arrays . Report X-IO. University of Bristol . Sebastian Madgwick. 2010. An Efficient Orientation Filter for Inertial and Inertial\/Magnetic Sensor Arrays. Report X-IO. University of Bristol."},{"key":"e_1_2_1_16_1","volume-title":"Proceedings of ACM UbiComp","author":"Maekawa Takuya","year":"2016","unstructured":"Takuya Maekawa , Daisuke Nakai , Kazuya Ohara , and Yasuo Namioka . 2016 . Toward practical factory activity recognition: Unsupervised understanding of repetitive assembly work in a factory . In Proceedings of ACM UbiComp 2016. ACM, New York, NY, 1088--1099. DOI:https:\/\/doi.org\/10.1145\/2971648.2971721 10.1145\/2971648.2971721 Takuya Maekawa, Daisuke Nakai, Kazuya Ohara, and Yasuo Namioka. 2016. Toward practical factory activity recognition: Unsupervised understanding of repetitive assembly work in a factory. In Proceedings of ACM UbiComp 2016. ACM, New York, NY, 1088--1099. DOI:https:\/\/doi.org\/10.1145\/2971648.2971721"},{"key":"e_1_2_1_17_1","volume-title":"Proceedings of PRICAI","author":"Mirmomeni Mahtab","year":"2018","unstructured":"Mahtab Mirmomeni , Yousef Kowsar , Lars Kulik , and James Bailey . 2018 . An automated matrix profile for mining consecutive repeats in time series . In Proceedings of PRICAI 2018. DOI:https:\/\/doi.org\/10.1007\/978-3-319-97310-4_22 10.1007\/978-3-319-97310-4_22 Mahtab Mirmomeni, Yousef Kowsar, Lars Kulik, and James Bailey. 2018. An automated matrix profile for mining consecutive repeats in time series. In Proceedings of PRICAI 2018. DOI:https:\/\/doi.org\/10.1007\/978-3-319-97310-4_22"},{"key":"e_1_2_1_18_1","volume-title":"Proceedings of ACM SIGMOD 2010","author":"Mueen Abdullah","year":"2010","unstructured":"Abdullah Mueen , Suman Nath , and Jie Liu . 2010 . Fast approximate correlation for massive time-series data . In Proceedings of ACM SIGMOD 2010 . DOI:https:\/\/doi.org\/10.1145\/1807167.1807188 10.1145\/1807167.1807188 Abdullah Mueen, Suman Nath, and Jie Liu. 2010. Fast approximate correlation for massive time-series data. In Proceedings of ACM SIGMOD 2010. DOI:https:\/\/doi.org\/10.1145\/1807167.1807188"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1979.4310076"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1080\/00207169108803967"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2017.10.011"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2348543.2348580"},{"key":"e_1_2_1_23_1","volume-title":"Proceedings of IEEE ISWC 2012","author":"Reiss Attila","year":"2012","unstructured":"Attila Reiss and Didier Stricker . 2012 . Introducing a new benchmarked dataset for activity monitoring . In Proceedings of IEEE ISWC 2012 . DOI:https:\/\/doi.org\/10.1109\/iswc.2012.13 10.1109\/iswc.2012.13 Attila Reiss and Didier Stricker. 2012. Introducing a new benchmarked dataset for activity monitoring. In Proceedings of IEEE ISWC 2012. DOI:https:\/\/doi.org\/10.1109\/iswc.2012.13"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.3390\/s150102059"},{"key":"e_1_2_1_25_1","volume-title":"Energy harvesting sensor nodes: Survey and implications","author":"Sudevalayam Sujesha","year":"2010","unstructured":"Sujesha Sudevalayam and Purushottam Kulkarni . 2010. Energy harvesting sensor nodes: Survey and implications . IEEE Communications Surveys 8 Tutorials 13, 3 ( 2010 ), 443--461. Sujesha Sudevalayam and Purushottam Kulkarni. 2010. Energy harvesting sensor nodes: Survey and implications. IEEE Communications Surveys 8 Tutorials 13, 3 (2010), 443--461."},{"key":"e_1_2_1_26_1","volume-title":"Proceedings of SDM 2005","author":"Vlachos Michail","year":"2005","unstructured":"Michail Vlachos , Philip Yu , and Vittorio Castelli . 2005 . On periodicity detection and structural periodic similarity . In Proceedings of SDM 2005 . DOI:https:\/\/doi.org\/10.1137\/1.9781611972757.40 10.1137\/1.9781611972757.40 Michail Vlachos, Philip Yu, and Vittorio Castelli. 2005. On periodicity detection and structural periodic similarity. In Proceedings of SDM 2005. DOI:https:\/\/doi.org\/10.1137\/1.9781611972757.40"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-005-0016-4"},{"key":"e_1_2_1_28_1","volume-title":"Proceedings of IEEE INFOCOM 2017","author":"Xie Lei","year":"2017","unstructured":"Lei Xie , Xu Dong , Wei Wang , and Dawei Huang . 2017 . Meta-activity recognition: A wearable approach for logic cognition-based activity sensing . In Proceedings of IEEE INFOCOM 2017 . DOI:https:\/\/doi.org\/10.1109\/infocom.2017.8057209 10.1109\/infocom.2017.8057209 Lei Xie, Xu Dong, Wei Wang, and Dawei Huang. 2017. Meta-activity recognition: A wearable approach for logic cognition-based activity sensing. In Proceedings of IEEE INFOCOM 2017. DOI:https:\/\/doi.org\/10.1109\/infocom.2017.8057209"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2003.1198394"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2013.01.021"},{"key":"e_1_2_1_31_1","volume-title":"Proceedings of IEEE ICDM 2016","author":"Michael Yeh Chin-Chia","year":"2016","unstructured":"Chin-Chia Michael Yeh , Helga Van Herle , and Eamonn Keogh . 2016 . Matrix Profile III: The matrix profile allows visualization of salient subsequences in massive time series . In Proceedings of IEEE ICDM 2016 . DOI:https:\/\/doi.org\/10.1109\/icdm.2016.0069 10.1109\/icdm.2016.0069 Chin-Chia Michael Yeh, Helga Van Herle, and Eamonn Keogh. 2016. Matrix Profile III: The matrix profile allows visualization of salient subsequences in massive time series. In Proceedings of IEEE ICDM 2016. DOI:https:\/\/doi.org\/10.1109\/icdm.2016.0069"},{"key":"e_1_2_1_32_1","volume-title":"Proceedings of IEEE ICDM 2017","author":"Michael Yeh Chin-Chia","year":"2017","unstructured":"Chin-Chia Michael Yeh , Nickolas Kavantzas , and Eamonn Keogh . 2017 . Matrix Profile VI: Meaningful multidimensional motif discovery . In Proceedings of IEEE ICDM 2017 . DOI:https:\/\/doi.org\/10.1109\/icdm.2017.66 10.1109\/icdm.2017.66 Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix Profile VI: Meaningful multidimensional motif discovery. In Proceedings of IEEE ICDM 2017. DOI:https:\/\/doi.org\/10.1109\/icdm.2017.66"},{"key":"e_1_2_1_33_1","volume-title":"Proceedings of IEEE ICDM 2016","author":"Michael Yeh Chin-Chia","year":"2016","unstructured":"Chin-Chia Michael Yeh , Yan Zhu , Liudmila Ulanova , Nurjahan Begum , Yifei Ding , Hoang Anh Dau , Diego Furtado Silva , Abdullah Mueen , and Eamonn Keogh . 2016 . Matrix Profile I: All pairs similarity joins for time series: A unifying view that includes motifs, discords and shapelets . In Proceedings of IEEE ICDM 2016 . DOI:https:\/\/doi.org\/10.1109\/icdm.2016.0179 10.1109\/icdm.2016.0179 Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix Profile I: All pairs similarity joins for time series: A unifying view that includes motifs, discords and shapelets. In Proceedings of IEEE ICDM 2016. DOI:https:\/\/doi.org\/10.1109\/icdm.2016.0179"},{"key":"e_1_2_1_34_1","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s11280-018-0566-1","article-title":"Scalable and parallel sequential pattern mining using spark","volume":"22","author":"Yu Xiao","year":"2019","unstructured":"Xiao Yu , Qing Li , and Jin Liu . 2019 . Scalable and parallel sequential pattern mining using spark . World Wide Web: Internet and Web Information Systems 22 (2019), 295 -- 324 . DOI:https:\/\/doi.org\/10.1007\/s11280-018-0566-1 10.1007\/s11280-018-0566-1 Xiao Yu, Qing Li, and Jin Liu. 2019. Scalable and parallel sequential pattern mining using spark. World Wide Web: Internet and Web Information Systems 22 (2019), 295--324. DOI:https:\/\/doi.org\/10.1007\/s11280-018-0566-1","journal-title":"World Wide Web: Internet and Web Information Systems"},{"key":"e_1_2_1_35_1","volume-title":"Proceedings of ACM CIKM","author":"Yuan Quan","year":"2017","unstructured":"Quan Yuan , Jingbo Shang , Xin Cao , Chao Zhang , Xinhe Geng , and Jiawei Han . 2017 . Detecting multiple periods and periodic patterns in event time sequences . In Proceedings of ACM CIKM 2017. DOI:https:\/\/doi.org\/10.1145\/3132847.3133027 10.1145\/3132847.3133027 Quan Yuan, Jingbo Shang, Xin Cao, Chao Zhang, Xinhe Geng, and Jiawei Han. 2017. Detecting multiple periods and periodic patterns in event time sequences. In Proceedings of ACM CIKM 2017. DOI:https:\/\/doi.org\/10.1145\/3132847.3133027"},{"key":"e_1_2_1_36_1","volume-title":"Proceedings of IEEE ICDM","author":"Zhu Yan","year":"2018","unstructured":"Yan Zhu , Chin-Chia Michael Yeh , Zachary Zimmerman , Kaveh Kamgar , and Eamonn Keogh . 2018 . Matrix Profile XI: SCRIMP++: Time series motif discovery at interactive speeds . In Proceedings of IEEE ICDM 2018. Yan Zhu, Chin-Chia Michael Yeh, Zachary Zimmerman, Kaveh Kamgar, and Eamonn Keogh. 2018. Matrix Profile XI: SCRIMP++: Time series motif discovery at interactive speeds. In Proceedings of IEEE ICDM 2018."}],"container-title":["ACM Transactions on Computing for Healthcare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3396250","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3396250","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:38:45Z","timestamp":1750199925000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3396250"}},"subtitle":["Efficient and Robust Mining of Successive Similar Patterns of Multiple Lengths in Time Series"],"short-title":[],"issued":{"date-parts":[[2020,9,30]]},"references-count":36,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,10,31]]}},"alternative-id":["10.1145\/3396250"],"URL":"https:\/\/doi.org\/10.1145\/3396250","relation":{},"ISSN":["2691-1957","2637-8051"],"issn-type":[{"value":"2691-1957","type":"print"},{"value":"2637-8051","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,30]]},"assertion":[{"value":"2019-08-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-04-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-09-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}