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|Title:||A machine learning approach for the automatic estimation of fixation-time data signals' quality||Authors:||Gabrieli, Giulio
Balagtas, Jan Paolo Macapinlac
|Keywords:||Social sciences::Psychology||Issue Date:||2020||Source:||Gabrieli, G., Balagtas, J. P. M., Esposito, G., & Setoh, P. (2020). A machine learning approach for the automatic estimation of fixation-time data signals' quality. Sensors, 20(23), 6775-. doi:10.3390/s20236775||Project:||020158-00001||Journal:||Sensors||Abstract:||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's 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)-a Linear SVC, a Non-Linear SVC, and K-Neighbors-classifiers 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.||URI:||https://hdl.handle.net/10356/145869||ISSN:||1424-8220||DOI:||10.3390/s20236775||Rights:||© 2020 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SSS Journal Articles|
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