{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T07:49:25Z","timestamp":1780991365896,"version":"3.54.1"},"reference-count":50,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,2,23]],"date-time":"2019-02-23T00:00:00Z","timestamp":1550880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2018YFB0505200"],"award-info":[{"award-number":["2018YFB0505200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872046"],"award-info":[{"award-number":["61872046"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"BUPT Excellent Ph.D. Students Foundation","award":["CX2018102"],"award-info":[{"award-number":["CX2018102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recently, the demand for human activity recognition has become more and more urgent. It is widely used in indoor positioning, medical monitoring, safe driving, etc. Existing activity recognition approaches require either the location information of the sensors or the specific domain knowledge, which are expensive, intrusive, and inconvenient for pervasive implementation. In this paper, a human activity recognition algorithm based on SDAE (Stacking Denoising Autoencoder) and LightGBM (LGB) is proposed. The SDAE is adopted to sanitize the noise in raw sensor data and extract the most effective characteristic expression with unsupervised learning. The LGB reveals the inherent feature dependencies among categories for accurate human activity recognition. Extensive experiments are conducted on four datasets of distinct sensor combinations collected by different devices in three typical application scenarios, which are human moving modes, current static, and dynamic behaviors of users. The experimental results demonstrate that our proposed algorithm achieves an average accuracy of 95.99%, outperforming other comparative algorithms using XGBoost, CNN (Convolutional Neural Network), CNN + Statistical features, or single SDAE.<\/jats:p>","DOI":"10.3390\/s19040947","type":"journal-article","created":{"date-parts":[[2019,2,25]],"date-time":"2019-02-25T03:06:52Z","timestamp":1551064012000},"page":"947","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":84,"title":["A Human Activity Recognition Algorithm Based on Stacking Denoising Autoencoder and LightGBM"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1957-8369","authenticated-orcid":false,"given":"Xile","family":"Gao","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6827-4225","authenticated-orcid":false,"given":"Haiyong","family":"Luo","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6551-6807","authenticated-orcid":false,"given":"Qu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunication, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Beijing University of Posts and Telecommunication, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Langlang","family":"Ye","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuexia","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4132","DOI":"10.3390\/s18124132","article-title":"Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors","volume":"18","author":"Elamvazuthi","year":"2018","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2085","DOI":"10.1109\/JIOT.2018.2823084","article-title":"Locomotion Activity Recognition Using Stacked Denoising Autoencoders","volume":"5","author":"Gu","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"6155","DOI":"10.3390\/s120506155","article-title":"Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning","volume":"12","author":"Pei","year":"2012","journal-title":"Sensors"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yan, S., Teng, Y., Smith, J.S., and Zhang, B. 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