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Modeling Users' Cognitive Performance Using Digital Pen Features

Digital pen features model characteristics of sketches and user behavior, and can be used for various supervised machine learning (ML) applications, such as multi-stroke sketch recognition and user modeling. In this work, we use a state-of-the-art set of more than 170 digital pen features, which we...

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Autores principales: Prange, Alexander, Sonntag, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9113515/
https://www.ncbi.nlm.nih.gov/pubmed/35592648
http://dx.doi.org/10.3389/frai.2022.787179
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author Prange, Alexander
Sonntag, Daniel
author_facet Prange, Alexander
Sonntag, Daniel
author_sort Prange, Alexander
collection PubMed
description Digital pen features model characteristics of sketches and user behavior, and can be used for various supervised machine learning (ML) applications, such as multi-stroke sketch recognition and user modeling. In this work, we use a state-of-the-art set of more than 170 digital pen features, which we implement and make publicly available. The feature set is evaluated in the use case of analyzing paper-pencil-based neurocognitive assessments in the medical domain. Most cognitive assessments, for dementia screening for example, are conducted with a pen on normal paper. We record these tests with a digital pen as part of a new interactive cognitive assessment tool with automatic analysis of pen input. The physician can, first, observe the sketching process in real-time on a mobile tablet, e.g., in telemedicine settings or to follow Covid-19 distancing regulations. Second, the results of an automatic test analysis are presented to the physician in real-time, thereby reducing manual scoring effort and producing objective reports. As part of our evaluation we examine how accurately different feature-based, supervised ML models can automatically score cognitive tests, with and without semantic content analysis. A series of ML-based sketch recognition experiments is conducted, evaluating 10 modern off-the-shelf ML classifiers (i.e., SVMs, Deep Learning, etc.) on a sketch data set which we recorded with 40 subjects from a geriatrics daycare clinic. In addition, an automated ML approach (AutoML) is explored for fine-tuning and optimizing classification performance on the data set, achieving superior recognition accuracies. Using standard ML techniques our feature set outperforms all previous approaches on the cognitive tests considered, i.e., the Clock Drawing Test, the Rey-Osterrieth Complex Figure Test, and the Trail Making Test, by automatically scoring cognitive tests with up to 87.5% accuracy in a binary classification task.
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spelling pubmed-91135152022-05-18 Modeling Users' Cognitive Performance Using Digital Pen Features Prange, Alexander Sonntag, Daniel Front Artif Intell Artificial Intelligence Digital pen features model characteristics of sketches and user behavior, and can be used for various supervised machine learning (ML) applications, such as multi-stroke sketch recognition and user modeling. In this work, we use a state-of-the-art set of more than 170 digital pen features, which we implement and make publicly available. The feature set is evaluated in the use case of analyzing paper-pencil-based neurocognitive assessments in the medical domain. Most cognitive assessments, for dementia screening for example, are conducted with a pen on normal paper. We record these tests with a digital pen as part of a new interactive cognitive assessment tool with automatic analysis of pen input. The physician can, first, observe the sketching process in real-time on a mobile tablet, e.g., in telemedicine settings or to follow Covid-19 distancing regulations. Second, the results of an automatic test analysis are presented to the physician in real-time, thereby reducing manual scoring effort and producing objective reports. As part of our evaluation we examine how accurately different feature-based, supervised ML models can automatically score cognitive tests, with and without semantic content analysis. A series of ML-based sketch recognition experiments is conducted, evaluating 10 modern off-the-shelf ML classifiers (i.e., SVMs, Deep Learning, etc.) on a sketch data set which we recorded with 40 subjects from a geriatrics daycare clinic. In addition, an automated ML approach (AutoML) is explored for fine-tuning and optimizing classification performance on the data set, achieving superior recognition accuracies. Using standard ML techniques our feature set outperforms all previous approaches on the cognitive tests considered, i.e., the Clock Drawing Test, the Rey-Osterrieth Complex Figure Test, and the Trail Making Test, by automatically scoring cognitive tests with up to 87.5% accuracy in a binary classification task. Frontiers Media S.A. 2022-05-03 /pmc/articles/PMC9113515/ /pubmed/35592648 http://dx.doi.org/10.3389/frai.2022.787179 Text en Copyright © 2022 Prange and Sonntag. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Prange, Alexander
Sonntag, Daniel
Modeling Users' Cognitive Performance Using Digital Pen Features
title Modeling Users' Cognitive Performance Using Digital Pen Features
title_full Modeling Users' Cognitive Performance Using Digital Pen Features
title_fullStr Modeling Users' Cognitive Performance Using Digital Pen Features
title_full_unstemmed Modeling Users' Cognitive Performance Using Digital Pen Features
title_short Modeling Users' Cognitive Performance Using Digital Pen Features
title_sort modeling users' cognitive performance using digital pen features
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9113515/
https://www.ncbi.nlm.nih.gov/pubmed/35592648
http://dx.doi.org/10.3389/frai.2022.787179
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