{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:07:40Z","timestamp":1760710060321,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,11,27]],"date-time":"2019-11-27T00:00:00Z","timestamp":1574812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009882","name":"Regione Lombardia","doi-asserted-by":"publisher","award":["CUP: E47H16001380009"],"award-info":[{"award-number":["CUP: E47H16001380009"]}],"id":[{"id":"10.13039\/501100009882","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents an efficient approach for subsequence search in data streams. The problem consists of identifying coherent repetitions of a given reference time-series, also in the multivariate case, within a longer data stream. The most widely adopted metric to address this problem is Dynamic Time Warping (DTW), but its computational complexity is a well-known issue. In this paper, we present an approach aimed at learning a kernel approximating DTW for efficiently analyzing streaming data collected from wearable sensors, while reducing the burden of DTW computation. Contrary to kernel, DTW allows for comparing two time-series with different length. To enable the use of kernel for comparing two time-series with different length, a feature embedding is required in order to obtain a fixed length vector representation. Each vector component is the DTW between the given time-series and a set of \u201cbasis\u201d series, randomly chosen. The approach has been validated on two benchmark datasets and on a real-life application for supporting self-rehabilitation in elderly subjects has been addressed. A comparison with traditional DTW implementations and other state-of-the-art algorithms is provided: results show a slight decrease in accuracy, which is counterbalanced by a significant reduction in computational costs.<\/jats:p>","DOI":"10.3390\/s19235192","type":"journal-article","created":{"date-parts":[[2019,11,27]],"date-time":"2019-11-27T03:55:51Z","timestamp":1574826951000},"page":"5192","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Efficient Kernel-Based Subsequence Search for Enabling Health Monitoring Services in IoT-Based Home Setting"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1431-576X","authenticated-orcid":false,"given":"Antonio","family":"Candelieri","sequence":"first","affiliation":[{"name":"Department of Computer Science, Systems and Communication, University of Milano-Bicocca, 20126 Milano MI, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8896-5047","authenticated-orcid":false,"given":"Stanislav","family":"Fedorov","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Systems and Communication, University of Milano-Bicocca, 20126 Milano MI, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4062-0824","authenticated-orcid":false,"given":"Enza","family":"Messina","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Systems and Communication, University of Milano-Bicocca, 20126 Milano MI, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"M\u00fcller, M. (2007). Dynamic time warping. Information Retrieval for Music and Motion, Springer.","DOI":"10.1007\/978-3-540-74048-3"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Han, L., Gao, C., Zhang, S., Li, D., Sun, Z., Yang, G., Li, J., Zhang, C., and Shao, G. (2018). Speech Recognition Algorithm of Substation Inspection Robot Based on Improved DTW. International Conference on Intelligent and Interactive Systems and Applications, Springer.","DOI":"10.1007\/978-3-030-02804-6_6"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.patcog.2018.02.011","article-title":"Structured dynamic time warping for continuous hand trajectory gesture recognition","volume":"80","author":"Tang","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_4","unstructured":"Calli, B., Kimmel, A., Hang, K., Bekris, K., and Dollar, A. (2018, January 5\u20138). Path Planning for Within-Hand Manipulation over Learned Representations of Safe States. Proceedings of the International Symposium on Experimental Robotics (ISER), Buenos Aires, Argentina."},{"key":"ref_5","unstructured":"Bauters, K., Cottyn, J., and Van Landeghem, H. (2018, January 6\u20138). Real time trajectory matching and outlier detection for assembly operator trajectories. Proceedings of the Internation Simulation Conference, Eurosis, Ponta Delgada, Portugal."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Paiyarom, S., Tangamchit, P., Keinprasit, R., and Kayasith, P. (2009, January 22\u201326). Fall detection and activity monitoring system using dynamic time warping for elderly and disabled people. Proceedings of the 3rd International Convention on Rehabilitation Engineering & Assistive Technology, Singapore.","DOI":"10.1145\/1592700.1592711"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1007\/s10586-017-0977-2","article-title":"Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm","volume":"21","author":"Varatharajan","year":"2018","journal-title":"Clust. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2969","DOI":"10.1007\/s00500-018-3597-8","article-title":"Global optimization in machine learning: The design of a predictive analytics application","volume":"23","author":"Candelieri","year":"2019","journal-title":"Soft Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1016\/j.proeng.2014.11.218","article-title":"Identifying typical urban water demand patterns for a reliable short-term forecasting\u2013the icewater project approach","volume":"89","author":"Candelieri","year":"2014","journal-title":"Procedia Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.engappai.2014.12.015","article-title":"Fuzzy clustering of time series data using dynamic time warping distance","volume":"39","author":"Izakian","year":"2015","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Petitjean, F., Forestier, G., Webb, G.I., Nicholson, A.E., Chen, Y., and Keogh, E. (2014, January 14\u201317). Dynamic time warping averaging of time series allows faster and more accurate classification. Proceedings of the 2014 IEEE International Conference on Data Mining, Shenzhen, China.","DOI":"10.1109\/ICDM.2014.27"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Radoi, A., and Burileanu, C. (2018). Retrieval of Similar Evolution Patterns from Satellite Image Time Series. Appl. Sci., 8.","DOI":"10.3390\/app8122435"},{"key":"ref_13","first-page":"224","article-title":"Dynamic programming algorithm optimization for spoken word recognition","volume":"159","author":"Sakoe","year":"1990","journal-title":"Readings Speech Recognit."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/TASSP.1975.1162641","article-title":"Minimum prediction residual principle applied to speech recognition","volume":"23","author":"Itakura","year":"1975","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mueen, A., and Keogh, E. (2016, January 13\u201317). Extracting optimal performance from dynamic time warping. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2945383"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Silva, D.F., and Batista, G.E. (2016, January 5\u20137). Speeding up all-pairwise dynamic time warping matrix calculation. Proceedings of the 2016 SIAM International Conference on Data Mining, Miami, FL, USA.","DOI":"10.1137\/1.9781611974348.94"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1007\/s10618-018-0557-y","article-title":"Speeding up similarity search under dynamic time warping by pruning unpromising alignments","volume":"32","author":"Silva","year":"2018","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Keogh, E.J., and Pazzani, M.J. (2001, January 5\u20137). Derivative dynamic time warping. Proceedings of the 2001 SIAM International Conference on Data Mining, Chicago, IL, USA.","DOI":"10.1137\/1.9781611972719.1"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Nagendar, G., and Jawahar, C.V. (2015, January 18\u201321). Fast approximate dynamic warping kernels. Proceedings of the Second ACM IKDD Conference on Data Sciences, Bangalore, India.","DOI":"10.1145\/2732587.2732592"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cuturi, M., Vert, J.P., Birkenes, O., and Matsui, T. (2007, January 15\u201320). A kernel for time series based on global alignments. Proceedings of the 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP\u201907, Honolulu, HI, USA.","DOI":"10.1109\/ICASSP.2007.366260"},{"key":"ref_21","unstructured":"Cuturi, M. (July, January 28). Fast global alignment kernels. Proceedings of the 28th International Conference on Machine Learning (ICML-11), Bellevue, WA, USA."},{"key":"ref_22","unstructured":"Bahlmann, C., Haasdonk, B., and Burkhardt, H. (2002, January 6\u20138). Online handwriting recognition with support vector machines-a kernel approach. Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition, Niagara on the Lake, ON, Canada."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sakurai, Y., Yoshikawa, M., and Faloutsos, C. (2005, January 13\u201315). FTW: Fast similarity search under the time warping distance. Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, Baltimore, MD, USA.","DOI":"10.1145\/1065167.1065210"},{"key":"ref_24","unstructured":"Yen, I.E.H., Lin, T.W., Lin, S.D., Ravikumar, P.K., and Dhillon, I.S. (2014). Sparse random feature algorithm as coordinate descent in hilbert space. Advances in Neural Information Processing Systems, The MIT Press."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1109\/TNNLS.2014.2333876","article-title":"On recursive edit distance kernels with application to time series classification","volume":"26","author":"Marteau","year":"2014","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_26","unstructured":"Wu, L., Yen, I.E.H., Yi, J., Xu, F., Lei, Q., and Witbrock, M. (2018). Random warping series: A random features method for time-series embedding. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Frazier, P.I. (2018). Bayesian Optimization. Recent Advances in Optimization and Modeling of Contemporary Problems, Informs PubsOnLine. Chapter 11.","DOI":"10.1287\/educ.2018.0188"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hutter, F., Kotthoff, L., and Vanschoren, J. (2019). Automatic machine learning: Methods, systems, challenges. Challenges in Machine Learning, Springer.","DOI":"10.1007\/978-3-030-05318-5"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Feurer, M., Klein, A., Eggensperger, K., Springenberg, J.T., Blum, M., and Hutter, F. (2019). Auto-sklearn: Efficient and Robust Automated Machine Learning. Automated Machine Learning, Springer.","DOI":"10.1007\/978-3-030-05318-5_6"},{"key":"ref_30","unstructured":"Mueen, A., Zhu, Y., Yeh, M., Kamgar, K., Viswanathan, K., Gupta, C., and Keogh, E. (2019, November 26). The Fastest Similarity Search Algorithm for Time Series Subsequences under Euclidean Distance. Available online: http:\/\/www.cs.unm.edu\/~mueen\/FastestSimilaritySearch.html."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1007\/s00779-011-0415-z","article-title":"Personalization and user verification in wearable systems using biometric walking patterns","volume":"16","author":"Casale","year":"2012","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_32","unstructured":"Ye, L., and Keogh, E. (July, January 28). Time series shapelets: A new primitive for data mining. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/23\/5192\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:37:52Z","timestamp":1760189872000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/23\/5192"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,27]]},"references-count":32,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["s19235192"],"URL":"https:\/\/doi.org\/10.3390\/s19235192","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,11,27]]}}}