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A method of processing a continuous stream of RGB-D data and a feature vector are proposed. The classification is carried out using the k-nearest neighbors algorithm with dynamic time warping, hidden Markov models, and bidirectional long short-term memory. The leave-one-subject-out protocol is used for the dataset containing 121 Polish Sign Language sentences performed five times by four deaf people. A data augmentation method is also proposed and tested. Preliminary observations and conclusions from the use of the system in a laboratory, as well as in real conditions with an experimental installation in the Office of Civil Affairs are given.<\/jats:p>","DOI":"10.3390\/s20082190","type":"journal-article","created":{"date-parts":[[2020,4,13]],"date-time":"2020-04-13T10:41:52Z","timestamp":1586774512000},"page":"2190","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Recognition of Signed Expressions in an Experimental System Supporting Deaf Clients in the City Office"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4084-8113","authenticated-orcid":false,"given":"Tomasz","family":"Kapuscinski","sequence":"first","affiliation":[{"name":"Department of Computer and Control Engineering, Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, W. 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