Towards Classifying Cognitive Performance by Sensing Electrodermal Activity in Children With Specific Learning Disorders
When children suffer from cognitive disorders, school performance and social environment are affected. Measuring changes in cognitive progress is essential for assessing the clinical follow-up of the patient's cognitive abilities. This process is considered as a challenge in ambulatory settings...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9239296/ |
| _version_ | 1828909976445255680 |
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| author | Carolina Rico-Olarte Diego M. Lopez Linda Becker Bjoern Eskofier |
| author_facet | Carolina Rico-Olarte Diego M. Lopez Linda Becker Bjoern Eskofier |
| author_sort | Carolina Rico-Olarte |
| collection | DOAJ |
| description | When children suffer from cognitive disorders, school performance and social environment are affected. Measuring changes in cognitive progress is essential for assessing the clinical follow-up of the patient's cognitive abilities. This process is considered as a challenge in ambulatory settings, where follow-ups should be non-invasive and continuous. Psychophysiological measures are an objective and unobtrusive evaluation alternative for recognizing cognitive changes. This paper aims to validate the relationship between cognition and the changes in physiological signals of children suffering from Specific Learning Disorders (SLD). This validation was carried out in an eHealth rehabilitation context (with the HapHop-Physio game). Electrodermal activity (EDA) signals were collected, processed, and analyzed through a machine learning approach. Obtained results were: a dataset built from wearable physiological data and a supervised classification model. The classification model can identify the children's cognitive performance (class) from the features of the tonic component of the EDA signal (attributes) with an accuracy of 79.95%. The presented results evidence that psychophysiological measures could allow for a highly objective follow-up for patients. They can also lead to creating a basis for further improvement of rehabilitation environments and developing neurofeedback applications. |
| first_indexed | 2024-12-13T18:35:38Z |
| format | Article |
| id | doaj.art-fc5b927fd4024bf39d1b1c7d7edfabd1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T18:35:38Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-fc5b927fd4024bf39d1b1c7d7edfabd12022-12-21T23:35:22ZengIEEEIEEE Access2169-35362020-01-01819618719619610.1109/ACCESS.2020.30337699239296Towards Classifying Cognitive Performance by Sensing Electrodermal Activity in Children With Specific Learning DisordersCarolina Rico-Olarte0https://orcid.org/0000-0003-1588-4112Diego M. Lopez1https://orcid.org/0000-0001-9425-4375Linda Becker2https://orcid.org/0000-0002-9950-6882Bjoern Eskofier3https://orcid.org/0000-0002-0417-0336Telematics Engineering Research Group, Universidad del Cauca (Unicauca), Popayán, ColombiaTelematics Engineering Research Group, Universidad del Cauca (Unicauca), Popayán, ColombiaDepartment of Psychology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyMachine Learning and Data Analytics Laboratory, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyWhen children suffer from cognitive disorders, school performance and social environment are affected. Measuring changes in cognitive progress is essential for assessing the clinical follow-up of the patient's cognitive abilities. This process is considered as a challenge in ambulatory settings, where follow-ups should be non-invasive and continuous. Psychophysiological measures are an objective and unobtrusive evaluation alternative for recognizing cognitive changes. This paper aims to validate the relationship between cognition and the changes in physiological signals of children suffering from Specific Learning Disorders (SLD). This validation was carried out in an eHealth rehabilitation context (with the HapHop-Physio game). Electrodermal activity (EDA) signals were collected, processed, and analyzed through a machine learning approach. Obtained results were: a dataset built from wearable physiological data and a supervised classification model. The classification model can identify the children's cognitive performance (class) from the features of the tonic component of the EDA signal (attributes) with an accuracy of 79.95%. The presented results evidence that psychophysiological measures could allow for a highly objective follow-up for patients. They can also lead to creating a basis for further improvement of rehabilitation environments and developing neurofeedback applications.https://ieeexplore.ieee.org/document/9239296/Electrodermal activitycognitive performancesupervised classificationspecific learning disorders |
| spellingShingle | Carolina Rico-Olarte Diego M. Lopez Linda Becker Bjoern Eskofier Towards Classifying Cognitive Performance by Sensing Electrodermal Activity in Children With Specific Learning Disorders IEEE Access Electrodermal activity cognitive performance supervised classification specific learning disorders |
| title | Towards Classifying Cognitive Performance by Sensing Electrodermal Activity in Children With Specific Learning Disorders |
| title_full | Towards Classifying Cognitive Performance by Sensing Electrodermal Activity in Children With Specific Learning Disorders |
| title_fullStr | Towards Classifying Cognitive Performance by Sensing Electrodermal Activity in Children With Specific Learning Disorders |
| title_full_unstemmed | Towards Classifying Cognitive Performance by Sensing Electrodermal Activity in Children With Specific Learning Disorders |
| title_short | Towards Classifying Cognitive Performance by Sensing Electrodermal Activity in Children With Specific Learning Disorders |
| title_sort | towards classifying cognitive performance by sensing electrodermal activity in children with specific learning disorders |
| topic | Electrodermal activity cognitive performance supervised classification specific learning disorders |
| url | https://ieeexplore.ieee.org/document/9239296/ |
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