{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T15:09:20Z","timestamp":1777129760900,"version":"3.51.4"},"reference-count":23,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,30]],"date-time":"2022-05-30T00:00:00Z","timestamp":1653868800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003170","name":"Stiftelsen f\u00f6r Kunskaps-och Kompetensutveckling (the Knowledge Foundation)","doi-asserted-by":"publisher","award":["20200001"],"award-info":[{"award-number":["20200001"]}],"id":[{"id":"10.13039\/501100003170","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Machine Activity Recognition (MAR) can be used to monitor manufacturing processes and find bottlenecks and potential for improvement in production. Several interesting results on MAR techniques have been produced in the last decade, but mostly on construction equipment. Forklift trucks, which are ubiquitous and highly important industrial machines, have been missing from the MAR research. This paper presents a data-driven method for forklift activity recognition that uses Controller Area Network (CAN) signals and semi-supervised learning (SSL). The SSL enables the utilization of large quantities of unlabeled operation data to build better classifiers; after a two-step post-processing, the recognition results achieve balanced accuracy of 88% for driving activities and 95% for load-handling activities on a hold-out data set. In terms of the Matthews correlation coefficient for five activity classes, the final score is 0.82, which is equal to the recognition results of two non-domain experts who use videos of the activities. A particular success is that context can be used to capture the transport of small weight loads that are not detected by the forklift\u2019s built-in weight sensor.<\/jats:p>","DOI":"10.3390\/s22114170","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T02:30:06Z","timestamp":1653964206000},"page":"4170","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Semi-Supervised Learning for Forklift Activity Recognition from Controller Area Network (CAN) Signals"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6420-8316","authenticated-orcid":false,"given":"Kunru","family":"Chen","sequence":"first","affiliation":[{"name":"Center for Applied Intelligent Systems, Halmstad University, Kristian IV:s v\u00e4g 3, 301 18 Halmstad, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5163-2997","authenticated-orcid":false,"given":"Thorsteinn","family":"R\u00f6gnvaldsson","sequence":"additional","affiliation":[{"name":"Center for Applied Intelligent Systems, Halmstad University, Kristian IV:s v\u00e4g 3, 301 18 Halmstad, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7796-5201","authenticated-orcid":false,"given":"S\u0142awomir","family":"Nowaczyk","sequence":"additional","affiliation":[{"name":"Center for Applied Intelligent Systems, Halmstad University, Kristian IV:s v\u00e4g 3, 301 18 Halmstad, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3272-4145","authenticated-orcid":false,"given":"Sepideh","family":"Pashami","sequence":"additional","affiliation":[{"name":"Center for Applied Intelligent Systems, Halmstad University, Kristian IV:s v\u00e4g 3, 301 18 Halmstad, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emilia","family":"Johansson","sequence":"additional","affiliation":[{"name":"Toyota Material Handling Manufacturing Sweden AB, Svarvargatan 8, 595 35 Mjolby, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gustav","family":"Sternel\u00f6v","sequence":"additional","affiliation":[{"name":"Toyota Material Handling Manufacturing Sweden AB, Svarvargatan 8, 595 35 Mjolby, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,30]]},"reference":[{"key":"ref_1","unstructured":"Fortune Business Insights (2022, March 30). 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