A machine learning approach for the automatic estimation of fixation-time data signals' quality
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 prepr...
Saved in:
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2021
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/145869 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Nanyang Technological University |
| Language: | English |
| id |
sg-ntu-dr.10356-145869 |
|---|---|
| record_format |
dspace |
| spelling |
sg-ntu-dr.10356-1458692023-03-05T15:34:21Z A machine learning approach for the automatic estimation of fixation-time data signals' quality Gabrieli, Giulio Balagtas, Jan Paolo Macapinlac Esposito, Gianluca Setoh, Peipei School of Social Sciences Lee Kong Chian School of Medicine (LKCMedicine) Social sciences::Psychology Eye-tracking Machine Learning 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. Ministry of Education (MOE) Published version This research was funded by Singapore’s Ministry of Education (MOE) (P.S., 020158-00001, ‘The Development of Moral Altruism in Young Children’) and by the NAP-Start Up Grant (G.E., M4081597). 2021-01-13T02:18:14Z 2021-01-13T02:18:14Z 2020 Journal Article 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 1424-8220 https://hdl.handle.net/10356/145869 10.3390/s20236775 33260851 2-s2.0-85096679344 23 20 en 020158-00001 Sensors /10.21979/N9/0FU9ZG © 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/). application/pdf |
| institution |
Nanyang Technological University |
| building |
NTU Library |
| continent |
Asia |
| country |
Singapore Singapore |
| content_provider |
NTU Library |
| collection |
DR-NTU |
| language |
English |
| topic |
Social sciences::Psychology Eye-tracking Machine Learning |
| spellingShingle |
Social sciences::Psychology Eye-tracking Machine Learning Gabrieli, Giulio Balagtas, Jan Paolo Macapinlac Esposito, Gianluca Setoh, Peipei A machine learning approach for the automatic estimation of fixation-time data signals' quality |
| description |
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. |
| author2 |
School of Social Sciences |
| author_facet |
School of Social Sciences Gabrieli, Giulio Balagtas, Jan Paolo Macapinlac Esposito, Gianluca Setoh, Peipei |
| format |
Article |
| author |
Gabrieli, Giulio Balagtas, Jan Paolo Macapinlac Esposito, Gianluca Setoh, Peipei |
| author_sort |
Gabrieli, Giulio |
| title |
A machine learning approach for the automatic estimation of fixation-time data signals' quality |
| title_short |
A machine learning approach for the automatic estimation of fixation-time data signals' quality |
| title_full |
A machine learning approach for the automatic estimation of fixation-time data signals' quality |
| title_fullStr |
A machine learning approach for the automatic estimation of fixation-time data signals' quality |
| title_full_unstemmed |
A machine learning approach for the automatic estimation of fixation-time data signals' quality |
| title_sort |
machine learning approach for the automatic estimation of fixation-time data signals' quality |
| publishDate |
2021 |
| url |
https://hdl.handle.net/10356/145869 |
| _version_ |
1759857249765818368 |