{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T11:07:02Z","timestamp":1769339222282,"version":"3.49.0"},"reference-count":55,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,28]],"date-time":"2021-07-28T00:00:00Z","timestamp":1627430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Electronics"],"abstract":"<jats:p>Searching for characteristic patterns in time series is a topic addressed for decades by the research community. Conventional subsequence matching techniques usually rely on the definition of a target template pattern and a searching method for detecting similar patterns. However, the intrinsic variability of time series introduces changes in patterns, either morphologically and temporally, making such techniques not as accurate as desired. Intending to improve segmentation performances, in this paper, we proposed a Mask-based Neural Network (NN) which is capable of extracting desired patterns of interest from long time series, without using any predefined template. The proposed NN has been validated, alongside a subsequence matching algorithm, in two datasets: clinical (electrocardiogram) and human activity (inertial sensors). Moreover, the reduced dimension of the data in the latter dataset led to the application of transfer learning and data augmentation techniques to reach model convergence. The results have shown the proposed model achieved better segmentation performances than the baseline one, in both domains, reaching average Precision and Recall scores of 99.0% and 97.5% (clinical domain), along with 77.0% and 71.4% (human activity domain), introducing Neural Networks and Transfer Learning as promising alternatives for pattern searching in time series.<\/jats:p>","DOI":"10.3390\/electronics10151805","type":"journal-article","created":{"date-parts":[[2021,7,28]],"date-time":"2021-07-28T05:23:48Z","timestamp":1627449828000},"page":"1805","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Time Series Segmentation Using Neural Networks with Cross-Domain Transfer Learning"],"prefix":"10.3390","volume":"10","author":[{"given":"Pedro","family":"Matias","sequence":"first","affiliation":[{"name":"Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455\/461, 4200-135 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8481-6079","authenticated-orcid":false,"given":"Duarte","family":"Folgado","sequence":"additional","affiliation":[{"name":"Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455\/461, 4200-135 Porto, Portugal"},{"name":"Laborat\u00f3rio de Instrumenta\u00e7\u00e3o, Engenharia Biom\u00e9dica e F\u00edsica da Radia\u00e7\u00e3o (LIBPhys-UNL), Departamento de F\u00edsica, Faculdade de Ci\u00eancias e Tecnologia (FCT), Universidade Nova de Lisboa, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4022-7424","authenticated-orcid":false,"given":"Hugo","family":"Gamboa","sequence":"additional","affiliation":[{"name":"Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455\/461, 4200-135 Porto, Portugal"},{"name":"Laborat\u00f3rio de Instrumenta\u00e7\u00e3o, Engenharia Biom\u00e9dica e F\u00edsica da Radia\u00e7\u00e3o (LIBPhys-UNL), Departamento de F\u00edsica, Faculdade de Ci\u00eancias e Tecnologia (FCT), Universidade Nova de Lisboa, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4234-5336","authenticated-orcid":false,"given":"Andr\u00e9","family":"Carreiro","sequence":"additional","affiliation":[{"name":"Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455\/461, 4200-135 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"210816","DOI":"10.1109\/ACCESS.2020.3037715","article-title":"Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey","volume":"8","author":"Demrozi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","unstructured":"Lin, J., Williamson, S., Borne, K., and DeBarr, D. 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