{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:25:27Z","timestamp":1760239527493,"version":"build-2065373602"},"reference-count":59,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T00:00:00Z","timestamp":1606435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Singapore Ministry of Education","award":["020158-00001"],"award-info":[{"award-number":["020158-00001"]}]},{"name":"Nanyang Assistant Professor - Start Up Grant","award":["M4081597"],"award-info":[{"award-number":["M4081597"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fixation time measures have been widely adopted in studies with infants and young children because they can successfully tap on their meaningful nonverbal behaviors. While recording preverbal children\u2019s behavior is relatively simple, analysis of collected signals requires extensive manual preprocessing. In this paper, we investigate the possibility of using different Machine Learning (ML)\u2014a Linear SVC, a Non-Linear SVC, and K-Neighbors\u2014classifiers to automatically discriminate between Usable and Unusable eye fixation recordings. Results of our models show an accuracy of up to the 80%, suggesting that ML tools can help human researchers during the preprocessing and labelling phase of collected data.<\/jats:p>","DOI":"10.3390\/s20236775","type":"journal-article","created":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T09:16:49Z","timestamp":1606468609000},"page":"6775","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Machine Learning Approach for the Automatic Estimation of Fixation-Time Data Signals\u2019 Quality"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9846-5767","authenticated-orcid":false,"given":"Giulio","family":"Gabrieli","sequence":"first","affiliation":[{"name":"Psychology Program, School of Social Sciences, Nanyang Technological University, Singapore 639818, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5176-2855","authenticated-orcid":false,"given":"Jan Paolo Macapinlac","family":"Balagtas","sequence":"additional","affiliation":[{"name":"Psychology Program, School of Social Sciences, Nanyang Technological University, Singapore 639818, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9442-0254","authenticated-orcid":false,"given":"Gianluca","family":"Esposito","sequence":"additional","affiliation":[{"name":"Psychology Program, School of Social Sciences, Nanyang Technological University, Singapore 639818, Singapore"},{"name":"Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore"},{"name":"Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7830-5977","authenticated-orcid":false,"given":"Peipei","family":"Setoh","sequence":"additional","affiliation":[{"name":"Psychology Program, School of Social Sciences, Nanyang Technological University, Singapore 639818, Singapore"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1080\/15248372.2015.1135801","article-title":"Pupillometry in infancy research","volume":"17","author":"Hepach","year":"2016","journal-title":"J. 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