Robust cognitive load detection from wrist-band sensors

In recent years, the detection of cognitive load has received a lot of attention. Understanding the circumstances in which cognitive load occurs and reliably predicting such occurrences, offers the potential for considerable advances in the field of Human-Computer Interaction (HCI). Numerous HCI app...

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Autores principales:	Vadim Borisov, Enkelejda Kasneci, Gjergji Kasneci
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Cognitive load detection Human-computer interaction Machine learning Ensemble methods Electronic computers. Computer science QA75.5-76.95 Psychology BF1-990
Acceso en línea:	https://doaj.org/article/def24efabc244de1895efba7ce759274
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spelling	oai:doaj.org-article:def24efabc244de1895efba7ce7592742021-12-01T05:04:23ZRobust cognitive load detection from wrist-band sensors2451-958810.1016/j.chbr.2021.100116https://doaj.org/article/def24efabc244de1895efba7ce7592742021-08-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2451958821000646https://doaj.org/toc/2451-9588In recent years, the detection of cognitive load has received a lot of attention. Understanding the circumstances in which cognitive load occurs and reliably predicting such occurrences, offers the potential for considerable advances in the field of Human-Computer Interaction (HCI). Numerous HCI applications, ranging from medical and health-related solutions to (smart) automotive environments, would directly benefit from the reliable detection of cognitive load. However, this task still remains highly challenging. We present a machine learning (ML) approach based on ensemble learning for robust cognitive load classification. The features used by the proposed solution are generated from the interpretation of physiological measurements (e.g., heart rate, r-r interval, skin temperature, and skin response) from a wearable device. Hence, our approach consists of two steps: (1) transforming the original data into discriminative features and (2) training an ensemble model to accurately and robustly predict cognitive load. The empirical results confirm that our method has a superior performance compared to various state-of-the-art baselines on the original and transformed data. Moreover, in the open-data CogLoad@UbiComp 2020 Competition, the proposed approach achieved the best results among 17 competing approaches and outperformed all participating competitors by a considerable margin.Vadim BorisovEnkelejda KasneciGjergji KasneciElsevierarticleCognitive load detectionHuman-computer interactionMachine learningEnsemble methodsElectronic computers. Computer scienceQA75.5-76.95PsychologyBF1-990ENComputers in Human Behavior Reports, Vol 4, Iss , Pp 100116- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Cognitive load detection Human-computer interaction Machine learning Ensemble methods Electronic computers. Computer science QA75.5-76.95 Psychology BF1-990
spellingShingle	Cognitive load detection Human-computer interaction Machine learning Ensemble methods Electronic computers. Computer science QA75.5-76.95 Psychology BF1-990 Vadim Borisov Enkelejda Kasneci Gjergji Kasneci Robust cognitive load detection from wrist-band sensors
description	In recent years, the detection of cognitive load has received a lot of attention. Understanding the circumstances in which cognitive load occurs and reliably predicting such occurrences, offers the potential for considerable advances in the field of Human-Computer Interaction (HCI). Numerous HCI applications, ranging from medical and health-related solutions to (smart) automotive environments, would directly benefit from the reliable detection of cognitive load. However, this task still remains highly challenging. We present a machine learning (ML) approach based on ensemble learning for robust cognitive load classification. The features used by the proposed solution are generated from the interpretation of physiological measurements (e.g., heart rate, r-r interval, skin temperature, and skin response) from a wearable device. Hence, our approach consists of two steps: (1) transforming the original data into discriminative features and (2) training an ensemble model to accurately and robustly predict cognitive load. The empirical results confirm that our method has a superior performance compared to various state-of-the-art baselines on the original and transformed data. Moreover, in the open-data CogLoad@UbiComp 2020 Competition, the proposed approach achieved the best results among 17 competing approaches and outperformed all participating competitors by a considerable margin.
format	article
author	Vadim Borisov Enkelejda Kasneci Gjergji Kasneci
author_facet	Vadim Borisov Enkelejda Kasneci Gjergji Kasneci
author_sort	Vadim Borisov
title	Robust cognitive load detection from wrist-band sensors
title_short	Robust cognitive load detection from wrist-band sensors
title_full	Robust cognitive load detection from wrist-band sensors
title_fullStr	Robust cognitive load detection from wrist-band sensors
title_full_unstemmed	Robust cognitive load detection from wrist-band sensors
title_sort	robust cognitive load detection from wrist-band sensors
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/def24efabc244de1895efba7ce759274
work_keys_str_mv	AT vadimborisov robustcognitiveloaddetectionfromwristbandsensors AT enkelejdakasneci robustcognitiveloaddetectionfromwristbandsensors AT gjergjikasneci robustcognitiveloaddetectionfromwristbandsensors
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Robust cognitive load detection from wrist-band sensors

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