{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:23:37Z","timestamp":1778631817604,"version":"3.51.4"},"reference-count":48,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"vor","delay-in-days":273,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Biometrics"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:p>\n                    Human behavior recognition is the process of automatically identifying and analyzing multiple human behaviors using modern technology. From previous studies, we find that redundant features not only slow down the model training process and increase the structural complexity but also degrade the overall performance of the model. To overcome this problem, this paper investigates a temporal convolutional neural network (TCN) model based on improved sparrow search algorithm random forest (SSARF) feature selection to accurately identify human behavioral traits based on wearable devices. The model is based on the TCN classification model and incorporates a random forest with the sparrow optimization algorithm to perform dimensionality reduction on the original features, which is used to remove poorly correlated and unimportant features and retain effective features with a certain contribution rate to generate the optimal feature subset. In order to verify the reliability of the method, the performance of the model was evaluated on two public datasets, UCI Human Activity Recognition and WISDM, respectively, and 98.54% and 97.83% recognition accuracies were obtained, which were improved by 0.47% and 1.04% compared to the prefeature selection, but the number of features was reduced by 84.31% and 32.50% compared to the original feature set. In addition, we compared the TCN classification model with other deep learning models in terms of evaluation metrics such as\n                    <jats:italic>F<\/jats:italic>\n                    <jats:sub>1<\/jats:sub>\n                    score, recall, precision, and accuracy, and the results showed that the TCN model outperformed the other control models in all four metrics. Meanwhile, it also outperforms the existing recognition methods in terms of accuracy and other aspects, which have some practical application value.\n                  <\/jats:p>","DOI":"10.1049\/2024\/4982277","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T03:51:13Z","timestamp":1727668273000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Research on TCN Model Based on SSARF Feature Selection in the Field of Human Behavior Recognition"],"prefix":"10.1049","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6845-3847","authenticated-orcid":false,"given":"Wei","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guibo","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4574-263X","authenticated-orcid":false,"given":"Shijie","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2024,9,30]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1145\/2523819"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.9734\/ajrcos\/2018\/v2i430080"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2010.05.023"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11276-023-03429-y"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.3390\/s140814302"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.5755\/j01.itc.44.3.11965"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1002\/adma.200701709"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.04.032"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.3390\/s23156727"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-023-09204-6"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-018-1395-8"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.5755\/j01.itc.52.4.33239"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00607-021-00928-8"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuri.2022.100078"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-03832-6"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-019-09682-y"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20010317"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-27192-w"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107681"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3100580"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aej.2023.11.075"},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-51971-1_5"},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-99010-1_25"},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.1080\/21642583.2019.1708830"},{"key":"e_1_2_9_25_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_9_26_2","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbx124"},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.2307\/2408678"},{"key":"e_1_2_9_28_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1013964023376"},{"key":"e_1_2_9_29_2","unstructured":"ZarembaW. SutskeverI. andVinyalsO. Recurrent neural network regularization 2014 arxiv preprint arxiv: 1409.2329."},{"key":"e_1_2_9_30_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_9_31_2","doi-asserted-by":"crossref","unstructured":"ChoK. van MerrienboerB. GulcehreC. BahdanauD. BougaresF. SchwenkH. andBengioY. Learning phrase representations using RNN encoder-decoder for statistical machine translation Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 2014 Doha Qatar Association for Computational Linguistics 1724\u20131734.","DOI":"10.3115\/v1\/D14-1179"},{"key":"e_1_2_9_32_2","unstructured":"BaiS. KolterJ. Z. andKoltunV. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling 2018 arxiv preprint arxiv: 1803.01271."},{"key":"e_1_2_9_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2008.06.054"},{"key":"e_1_2_9_34_2","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.7258"},{"key":"e_1_2_9_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aej.2023.09.013"},{"key":"e_1_2_9_36_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0285981"},{"key":"e_1_2_9_37_2","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-14-106"},{"key":"e_1_2_9_38_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-023-08587-x"},{"key":"e_1_2_9_39_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-41545-z"},{"key":"e_1_2_9_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2022.106131"},{"key":"e_1_2_9_41_2","doi-asserted-by":"publisher","DOI":"10.3390\/informatics9030056"},{"key":"e_1_2_9_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/JTEHM.2022.3177710"},{"key":"e_1_2_9_43_2","doi-asserted-by":"publisher","DOI":"10.3390\/s23125715"},{"key":"e_1_2_9_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2020.3045135"},{"key":"e_1_2_9_45_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2020.03.289"},{"key":"e_1_2_9_46_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2024.06.016"},{"key":"e_1_2_9_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2982225"},{"key":"e_1_2_9_48_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-021-02283-3"}],"container-title":["IET Biometrics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/2024\/4982277","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T17:24:43Z","timestamp":1762363483000},"score":1,"resource":{"primary":{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/10.1049\/2024\/4982277"}},"subtitle":[],"editor":[{"given":"Vincenzo","family":"Conti","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["10.1049\/2024\/4982277"],"URL":"https:\/\/doi.org\/10.1049\/2024\/4982277","archive":["Portico"],"relation":{},"ISSN":["2047-4938","2047-4946"],"issn-type":[{"value":"2047-4938","type":"print"},{"value":"2047-4946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1]]},"assertion":[{"value":"2024-03-19","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-08-23","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-09-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"4982277"}}