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Investigating Methods for Cognitive Workload Estimation for Assistive Robots
Robots interacting with humans in assistive contexts have to be sensitive to human cognitive states to be able to provide help when it is needed and not overburden the human when the human is busy. Yet, it is currently still unclear which sensing modality might allow robots to derive the best eviden...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9505485/ https://www.ncbi.nlm.nih.gov/pubmed/36146189 http://dx.doi.org/10.3390/s22186834 |
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author | Aygun, Ayca Nguyen, Thuan Haga, Zachary Aeron, Shuchin Scheutz, Matthias |
author_facet | Aygun, Ayca Nguyen, Thuan Haga, Zachary Aeron, Shuchin Scheutz, Matthias |
author_sort | Aygun, Ayca |
collection | PubMed |
description | Robots interacting with humans in assistive contexts have to be sensitive to human cognitive states to be able to provide help when it is needed and not overburden the human when the human is busy. Yet, it is currently still unclear which sensing modality might allow robots to derive the best evidence of human workload. In this work, we analyzed and modeled data from a multi-modal simulated driving study specifically designed to evaluate different levels of cognitive workload induced by various secondary tasks such as dialogue interactions and braking events in addition to the primary driving task. Specifically, we performed statistical analyses of various physiological signals including eye gaze, electroencephalography, and arterial blood pressure from the healthy volunteers and utilized several machine learning methodologies including k-nearest neighbor, naive Bayes, random forest, support-vector machines, and neural network-based models to infer human cognitive workload levels. Our analyses provide evidence for eye gaze being the best physiological indicator of human cognitive workload, even when multiple signals are combined. Specifically, the highest accuracy (in %) of binary workload classification based on eye gaze signals is 80.45 ∓ 3.15 achieved by using support-vector machines, while the highest accuracy combining eye gaze and electroencephalography is only 77.08 ∓ 3.22 achieved by a neural network-based model. Our findings are important for future efforts of real-time workload estimation in the multimodal human-robot interactive systems given that eye gaze is easy to collect and process and less susceptible to noise artifacts compared to other physiological signal modalities. |
format | Online Article Text |
id | pubmed-9505485 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95054852022-09-24 Investigating Methods for Cognitive Workload Estimation for Assistive Robots Aygun, Ayca Nguyen, Thuan Haga, Zachary Aeron, Shuchin Scheutz, Matthias Sensors (Basel) Article Robots interacting with humans in assistive contexts have to be sensitive to human cognitive states to be able to provide help when it is needed and not overburden the human when the human is busy. Yet, it is currently still unclear which sensing modality might allow robots to derive the best evidence of human workload. In this work, we analyzed and modeled data from a multi-modal simulated driving study specifically designed to evaluate different levels of cognitive workload induced by various secondary tasks such as dialogue interactions and braking events in addition to the primary driving task. Specifically, we performed statistical analyses of various physiological signals including eye gaze, electroencephalography, and arterial blood pressure from the healthy volunteers and utilized several machine learning methodologies including k-nearest neighbor, naive Bayes, random forest, support-vector machines, and neural network-based models to infer human cognitive workload levels. Our analyses provide evidence for eye gaze being the best physiological indicator of human cognitive workload, even when multiple signals are combined. Specifically, the highest accuracy (in %) of binary workload classification based on eye gaze signals is 80.45 ∓ 3.15 achieved by using support-vector machines, while the highest accuracy combining eye gaze and electroencephalography is only 77.08 ∓ 3.22 achieved by a neural network-based model. Our findings are important for future efforts of real-time workload estimation in the multimodal human-robot interactive systems given that eye gaze is easy to collect and process and less susceptible to noise artifacts compared to other physiological signal modalities. MDPI 2022-09-09 /pmc/articles/PMC9505485/ /pubmed/36146189 http://dx.doi.org/10.3390/s22186834 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Aygun, Ayca Nguyen, Thuan Haga, Zachary Aeron, Shuchin Scheutz, Matthias Investigating Methods for Cognitive Workload Estimation for Assistive Robots |
title | Investigating Methods for Cognitive Workload Estimation for Assistive Robots |
title_full | Investigating Methods for Cognitive Workload Estimation for Assistive Robots |
title_fullStr | Investigating Methods for Cognitive Workload Estimation for Assistive Robots |
title_full_unstemmed | Investigating Methods for Cognitive Workload Estimation for Assistive Robots |
title_short | Investigating Methods for Cognitive Workload Estimation for Assistive Robots |
title_sort | investigating methods for cognitive workload estimation for assistive robots |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9505485/ https://www.ncbi.nlm.nih.gov/pubmed/36146189 http://dx.doi.org/10.3390/s22186834 |
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