CLAS: A Database for Cognitive Load, Affect and Stress Recognition

Markova, Valentina; Ganchev, Todor; Kalinkov, Kalin

doi:10.1109/bia48344.2019.8967457

Cited by 73 publications

(49 citation statements)

References 10 publications

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“…For this reason, open access datasets are very valuable for EMSR research. Unfortunately, existing open access datasets (e.g., Dan-Glauser and Scherer, 2011 ; Koelstra et al, 2011 ; Abadi et al, 2015 ; Sharma et al, 2018 ; Markova et al, 2019 ) contain only the data of induced emotions.…”

Section: The In-the-wild Methodologymentioning

confidence: 99%

Toward Emotion Recognition From Physiological Signals in the Wild: Approaching the Methodological Issues in Real-Life Data Collection

et al. 2020

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Emotion, mood, and stress recognition (EMSR) has been studied in laboratory settings for decades. In particular, physiological signals are widely used to detect and classify affective states in lab conditions. However, physiological reactions to emotional stimuli have been found to differ in laboratory and natural settings. Thanks to recent technological progress (e.g., in wearables) the creation of EMSR systems for a large number of consumers during their everyday activities is increasingly possible. Therefore, datasets created in the wild are needed to insure the validity and the exploitability of EMSR models for real-life applications. In this paper, we initially present common techniques used in laboratory settings to induce emotions for the purpose of physiological dataset creation. Next, advantages and challenges of data collection in the wild are discussed. To assess the applicability of existing datasets to real-life applications, we propose a set of categories to guide and compare at a glance different methodologies used by researchers to collect such data. For this purpose, we also introduce a visual tool called Graphical Assessment of Real-life Application-Focused Emotional Dataset (GARAFED). In the last part of the paper, we apply the proposed tool to compare existing physiological datasets for EMSR in the wild and to show possible improvements and future directions of research. We wish for this paper and GARAFED to be used as guidelines for researchers and developers who aim at collecting affect-related data for real-life EMSR-based applications.

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Section: The In-the-wild Methodologymentioning

confidence: 99%

Toward Emotion Recognition From Physiological Signals in the Wild: Approaching the Methodological Issues in Real-Life Data Collection

et al. 2020

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“…The ECG device used was from Thought Technology, and the configuration setup had three leads, 1 kHz. The annotation was by self-assessment using the ADM. CLAS [ 68 ]: This stands for C ognitive L oad, A ffect and S tress Recognition. The data were collected from 62 subjects, with 32 samples each.…”

Section: Ecg-inclusive Affective Datasetsmentioning

confidence: 99%

“…CLAS [ 68 ]: This stands for C ognitive L oad, A ffect and S tress Recognition. The data were collected from 62 subjects, with 32 samples each.…”

Section: Ecg-inclusive Affective Datasetsmentioning

confidence: 99%

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Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare—A Review

Hasnul

Aziz

Alelyani

et al. 2021

Sensors

118

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Affective computing is a field of study that integrates human affects and emotions with artificial intelligence into systems or devices. A system or device with affective computing is beneficial for the mental health and wellbeing of individuals that are stressed, anguished, or depressed. Emotion recognition systems are an important technology that enables affective computing. Currently, there are a lot of ways to build an emotion recognition system using various techniques and algorithms. This review paper focuses on emotion recognition research that adopted electrocardiograms (ECGs) as a unimodal approach as well as part of a multimodal approach for emotion recognition systems. Critical observations of data collection, pre-processing, feature extraction, feature selection and dimensionality reduction, classification, and validation are conducted. This paper also highlights the architectures with accuracy of above 90%. The available ECG-inclusive affective databases are also reviewed, and a popularity analysis is presented. Additionally, the benefit of emotion recognition systems towards healthcare systems is also reviewed here. Based on the literature reviewed, a thorough discussion on the subject matter and future works is suggested and concluded. The findings presented here are beneficial for prospective researchers to look into the summary of previous works conducted in the field of ECG-based emotion recognition systems, and for identifying gaps in the area, as well as in developing and designing future applications of emotion recognition systems, especially in improving healthcare.

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“…In the current study, we used the CLAS dataset [26] due to its particular design-it allows to evaluate the individual's ability to concentrate and successfully solve cognitive assignments under stress. The CLAS dataset contains a large number of recordings of the physiological response of university students in their twenties, acquired while they were performing three different cognitive tasks and two emotions-evoking tasks.…”

Section: Dataset and Protocolmentioning

confidence: 99%

Detection of acute stress caused by cognitive tasks based on physiological signals

et al. 2021

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We report on the development of an automated detector of acute stress based on physiological signals. Our detector discriminates between high and low levels of acute stress accumulated by students when performing cognitive tasks on a computer. The proposed detector builds on well-known physiological signal processing principles combined with the state-of-art support vector machine (SVM) classifier. The novelty aspects here come from the design and implementation of the signal pre-processing and the feature extraction stages, which were purposely designed and fine-tuned for the specific needs of acute stress detection and from applying existing algorithms to a new problem. The proposed acute stress detector was evaluated in person-specific and person-independent experimental setups using the publicly available CLAS dataset. Each setup involved three cognitive tasks with a dissimilar crux of the matter and different complexity. The experimental results indicated a very high detection accuracy when discriminating between acute stress conditions due to significant cognitive load and conditions elicited by two typical emotion elicitation tasks. Such a functionality would also contribute towards obtaining a multi-faceted analysis on the dependence of work efficiency from personal treats, cognitive load and acute stress level.

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CLAS: A Database for Cognitive Load, Affect and Stress Recognition

Cited by 73 publications

References 10 publications

Toward Emotion Recognition From Physiological Signals in the Wild: Approaching the Methodological Issues in Real-Life Data Collection

Toward Emotion Recognition From Physiological Signals in the Wild: Approaching the Methodological Issues in Real-Life Data Collection

Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare—A Review

Detection of acute stress caused by cognitive tasks based on physiological signals

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