{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T10:40:23Z","timestamp":1761129623049,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,4,18]],"date-time":"2019-04-18T00:00:00Z","timestamp":1555545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002301","name":"Eesti Teadusagentuur","doi-asserted-by":"publisher","award":["PUT638, PUT1075, PUT1081"],"award-info":[{"award-number":["PUT638, PUT1075, PUT1081"]}],"id":[{"id":"10.13039\/501100002301","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004410","name":"T\u00fcrkiye Bilimsel ve Teknolojik Ara\u015ftirma Kurumu","doi-asserted-by":"publisher","award":["116E097"],"award-info":[{"award-number":["116E097"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002924","name":"Federaci\u00f3n Espa\u00f1ola de Enfermedades Raras","doi-asserted-by":"publisher","award":["TIN2016-74946-P"],"award-info":[{"award-number":["TIN2016-74946-P"]}],"id":[{"id":"10.13039\/501100002924","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Action recognition is a challenging task that plays an important role in many robotic systems, which highly depend on visual input feeds. However, due to privacy concerns, it is important to find a method which can recognise actions without using visual feed. In this paper, we propose a concept for detecting actions while preserving the test subject\u2019s privacy. Our proposed method relies only on recording the temporal evolution of light pulses scattered back from the scene. Such data trace to record one action contains a sequence of one-dimensional arrays of voltage values acquired by a single-pixel detector at 1 GHz repetition rate. Information about both the distance to the object and its shape are embedded in the traces. We apply machine learning in the form of recurrent neural networks for data analysis and demonstrate successful action recognition. The experimental results show that our proposed method could achieve on average     96.47 %     accuracy on the actions walking forward, walking backwards, sitting down, standing up and waving hand, using recurrent neural network.<\/jats:p>","DOI":"10.3390\/e21040414","type":"journal-article","created":{"date-parts":[[2019,4,18]],"date-time":"2019-04-18T11:58:21Z","timestamp":1555588701000},"page":"414","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Action Recognition Using Single-Pixel Time-of-Flight Detection"],"prefix":"10.3390","volume":"21","author":[{"given":"Ikechukwu","family":"Ofodile","sequence":"first","affiliation":[{"name":"iCv Lab, Institute of Technology, University of Tartu, 50411 Tartu, Estonia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed","family":"Helmi","sequence":"additional","affiliation":[{"name":"iCv Lab, Institute of Technology, University of Tartu, 50411 Tartu, Estonia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Albert","family":"Clap\u00e9s","sequence":"additional","affiliation":[{"name":"University of Barcelona, 08007 Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Egils","family":"Avots","sequence":"additional","affiliation":[{"name":"iCv Lab, Institute of Technology, University of Tartu, 50411 Tartu, Estonia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kerttu Maria","family":"Peensoo","sequence":"additional","affiliation":[{"name":"Institute of Physics, University of Tartu, 50411 Tartu, Estonia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sandhra-Mirella","family":"Valdma","sequence":"additional","affiliation":[{"name":"Institute of Physics, University of Tartu, 50411 Tartu, Estonia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Valdmann","sequence":"additional","affiliation":[{"name":"Institute of Physics, University of Tartu, 50411 Tartu, Estonia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8882-233X","authenticated-orcid":false,"given":"Heli","family":"Valtna-Lukner","sequence":"additional","affiliation":[{"name":"Institute of Physics, University of Tartu, 50411 Tartu, Estonia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9440-1718","authenticated-orcid":false,"given":"Sergey","family":"Omelkov","sequence":"additional","affiliation":[{"name":"Institute of Physics, University of Tartu, 50411 Tartu, Estonia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sergio","family":"Escalera","sequence":"additional","affiliation":[{"name":"University of Barcelona, 08007 Barcelona, Spain"},{"name":"The Computer Vision Centre, 08193 Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cagri","family":"Ozcinar","sequence":"additional","affiliation":[{"name":"Trinity College Dublin, Dublin 2, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8460-5717","authenticated-orcid":false,"given":"Gholamreza","family":"Anbarjafari","sequence":"additional","affiliation":[{"name":"iCv Lab, Institute of Technology, University of Tartu, 50411 Tartu, Estonia"},{"name":"Department of Electrical and Electronic Engineering, Hasan Kalyoncu University, Gaziantep 27000, Turkey"},{"name":"Institute of Digital Technologies, Loughborough University London, London E15 2GZ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fernando, B., Gavves, E., Oramas, J.M., Ghodrati, A., and Tuytelaars, T. 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