{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:14:56Z","timestamp":1778080496321,"version":"3.51.4"},"reference-count":37,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2021,12,28]],"date-time":"2021-12-28T00:00:00Z","timestamp":1640649600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ICA"],"published-print":{"date-parts":[[2021,12,28]]},"abstract":"<jats:p>Activity recognition technologies only present a good performance in controlled conditions, where a limited number of actions are allowed. On the contrary, industrial applications are scenarios with real and uncontrolled conditions where thousands of different activities (such as transporting or manufacturing craft products), with an incredible variability, may be developed. In this context, new and enhanced human activity recognition technologies are needed. Therefore, in this paper, a new activity recognition technology, focused on Industry 4.0 scenarios, is proposed. The proposed mechanism consists of different steps, including a first analysis phase where physical signals are processed using moving averages, filters and signal processing techniques, and an atomic recognition step where Dynamic Time Warping technologies and k-nearest neighbors solutions are integrated; a second phase where activities are modeled using generalized Markov models and context labels are recognized using a multi-layer perceptron; and a third step where activities are recognized using the previously created Markov models and context information, formatted as labels. The proposed solution achieves the best recognition rate of 87% which demonstrates the efficacy of the described method. Compared to the state-of-the-art solutions, an improvement up to 10% is reported.<\/jats:p>","DOI":"10.3233\/ica-210667","type":"journal-article","created":{"date-parts":[[2021,9,17]],"date-time":"2021-09-17T11:57:38Z","timestamp":1631879858000},"page":"83-103","source":"Crossref","is-referenced-by-count":26,"title":["Recognizing human activities in Industry 4.0 scenarios through an analysis-modeling- recognition algorithm and context labels"],"prefix":"10.1177","volume":"29","author":[{"given":"Borja","family":"Bordel","sequence":"first","affiliation":[{"name":"Department of Informatic Systems, Universidad Polit\u00e9cnica de Madrid, Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ram\u00f3n","family":"Alcarria","sequence":"additional","affiliation":[{"name":"Department of Geospatial Engineering, Universidad Polit\u00e9cnica de Madrid, Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tom\u00e1s","family":"Robles","sequence":"additional","affiliation":[{"name":"Department of Informatic Systems, Universidad Polit\u00e9cnica de Madrid, Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/ICA-210667_ref1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jii.2017.04.005","article-title":"Industry 4.0: A survey on technologies, applications and open research issues","volume":"6","author":"Lu","year":"2017","journal-title":"Journal of Industrial Information Integration"},{"key":"10.3233\/ICA-210667_ref2","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.pmcj.2017.06.011","article-title":"Cyber-physical systems: Extending pervasive sensing from control theory to the Internet of Things","volume":"40","author":"Bordel","year":"2017","journal-title":"Pervasive and Mobile Computing"},{"key":"10.3233\/ICA-210667_ref3","doi-asserted-by":"crossref","unstructured":"Perme, MP, Manevski D. 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