{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:44:23Z","timestamp":1772909063412,"version":"3.50.1"},"reference-count":26,"publisher":"Wiley","license":[{"start":{"date-parts":[[2018,9,27]],"date-time":"2018-09-27T00:00:00Z","timestamp":1538006400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61327807"],"award-info":[{"award-number":["61327807"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Sensors"],"published-print":{"date-parts":[[2018,9,27]]},"abstract":"<jats:p>Human activity recognition (HAR) problems have traditionally been solved by using engineered features obtained by heuristic methods. These methods ignore the time information of the streaming sensor data and cannot achieve sequential human activity recognition. With the use of traditional statistical learning methods, results could easily plunge into the local minimum other than the global optimal and also face the problem of low efficiency. Therefore, we propose a hybrid deep framework based on convolution operations, LSTM recurrent units, and ELM classifier; the advantages are as follows: (1) does not require expert knowledge in extracting features; (2) models temporal dynamics of features; and (3) is more suitable to classify the extracted features and shortens the runtime. All of these unique advantages make it superior to other HAR algorithms. We evaluate our framework on OPPORTUNITY dataset which has been used in OPPORTUNITY challenge. Results show that our proposed method outperforms deep nonrecurrent networks by 6%, outperforming the previous reported best result by 8%. When compared with neural network using BP algorithm, testing time reduced by 38%.<\/jats:p>","DOI":"10.1155\/2018\/8580959","type":"journal-article","created":{"date-parts":[[2018,9,27]],"date-time":"2018-09-27T19:48:44Z","timestamp":1538077724000},"page":"1-10","source":"Crossref","is-referenced-by-count":61,"title":["Sequential Human Activity Recognition Based on Deep Convolutional Network and Extreme Learning Machine Using Wearable Sensors"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1328-3479","authenticated-orcid":true,"given":"Jian","family":"Sun","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering & Automation, Beihang University, Beijing 100191, China"},{"name":"The F.R.I. of Ministry of Public Security, Beijing 100048, China"}]},{"given":"Yongling","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering & Automation, Beihang University, Beijing 100191, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4623-8607","authenticated-orcid":true,"given":"Shengguang","family":"Li","sequence":"additional","affiliation":[{"name":"The F.R.I. of Ministry of Public Security, Beijing 100048, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2134-0998","authenticated-orcid":true,"given":"Jie","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China"}]},{"given":"Cheng","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China"}]},{"given":"Lin","family":"Tan","sequence":"additional","affiliation":[{"name":"The F.R.I. of Ministry of Public Security, Beijing 100048, China"}]}],"member":"311","reference":[{"key":"2","doi-asserted-by":"publisher","DOI":"10.1109\/tbme.2015.2471094"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2017.06.002"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.2991\/978-94-91216-05-3_2"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/1260734"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46562-3_23"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.3390\/computers2020088"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2017.09.014"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2012.59"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2012.12.014"},{"issue":"3-4","key":"12","first-page":"323","volume":"25","year":"2015","journal-title":"Adhoc & Sensor Wireless Networks"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1109\/tbme.2014.2307069"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1109\/TAP.2017.2742551"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.5296\/npa.v8i3.10146"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2017.2744718"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.3390\/s16010115"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1109\/tsmcb.2011.2168604"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/4160652"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-26561-2_6"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.1145\/2499621"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.2.461"},{"key":"32","year":"2010"},{"key":"33","doi-asserted-by":"publisher","DOI":"10.1037\/h0042519"},{"key":"34","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"36","year":"2015"},{"key":"37","year":"2011"}],"container-title":["Journal of Sensors"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/js\/2018\/8580959.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/js\/2018\/8580959.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/js\/2018\/8580959.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,9,27]],"date-time":"2018-09-27T19:48:46Z","timestamp":1538077726000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/js\/2018\/8580959\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,27]]},"references-count":26,"alternative-id":["8580959","8580959"],"URL":"https:\/\/doi.org\/10.1155\/2018\/8580959","relation":{},"ISSN":["1687-725X","1687-7268"],"issn-type":[{"value":"1687-725X","type":"print"},{"value":"1687-7268","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9,27]]}}}