An Application of Artificial Intelligence for Detecting Emotions in Neuromarketing

Filipović, Filip; Despotović-Zrakić, Marijana; Radenković, Božidar; Jovanić, B.R.; Zivojinovic, Lazar

doi:10.1109/ic-aiai48757.2019.00016

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…The identification and classification of emotions for improving people’s lives have gained interest in recent years as several fields can take advantage of the results in this area [ 15 , 16 , 17 ]. such as mental health, human-machine interfaces, learning and teaching methods, video games or neuromarketing.…”

Section: State Of the Art: Emotions Physiological Response And Affect...mentioning

confidence: 99%

Fear Detection in Multimodal Affective Computing: Physiological Signals versus Catecholamine Concentration

Gutiérrez-Martín

Romero

Andújar

et al. 2022

Sensors

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Affective computing through physiological signals monitoring is currently a hot topic in the scientific literature, but also in the industry. Many wearable devices are being developed for health or wellness tracking during daily life or sports activity. Likewise, other applications are being proposed for the early detection of risk situations involving sexual or violent aggressions, with the identification of panic or fear emotions. The use of other sources of information, such as video or audio signals will make multimodal affective computing a more powerful tool for emotion classification, improving the detection capability. There are other biological elements that have not been explored yet and that could provide additional information to better disentangle negative emotions, such as fear or panic. Catecholamines are hormones produced by the adrenal glands, two small glands located above the kidneys. These hormones are released in the body in response to physical or emotional stress. The main catecholamines, namely adrenaline, noradrenaline and dopamine have been analysed, as well as four physiological variables: skin temperature, electrodermal activity, blood volume pulse (to calculate heart rate activity. i.e., beats per minute) and respiration rate. This work presents a comparison of the results provided by the analysis of physiological signals in reference to catecholamine, from an experimental task with 21 female volunteers receiving audiovisual stimuli through an immersive environment in virtual reality. Artificial intelligence algorithms for fear classification with physiological variables and plasma catecholamine concentration levels have been proposed and tested. The best results have been obtained with the features extracted from the physiological variables. Adding catecholamine’s maximum variation during the five minutes after the video clip visualization, as well as adding the five measurements (1-min interval) of these levels, are not providing better performance in the classifiers.

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Section: State Of the Art: Emotions Physiological Response And Affect...mentioning

confidence: 99%

Fear Detection in Multimodal Affective Computing: Physiological Signals versus Catecholamine Concentration

Gutiérrez-Martín

Romero

Andújar

et al. 2022

Sensors

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“…As a result, much thought goes into making models that are compact and can be executed quickly [17]. In fact, model loading performance exceeds inference task performance because [8,30,38,46,49,60,71,75,77] handtrack.js d This real-time hand detection library frames hand tracking as an object detection problem and predicts bounding boxes for the position of hands in an image using a trained convolutional neural network. Example works include [58] and [120] machinelearn.js e This is a library for machine learning algorithms, similar to scikit-learn from Python.…”

Section: Front-end Deep Learning Development Approachmentioning

confidence: 99%

“…Two other studies that introduced visualization for learning deep learning include TensorSpace, 37 a framework for visually representing pre-trained neural network models in three dimensions, and LEGION, 38 in [28], a graphical analytic app that enables users to compare and choose regression models that have been created either via hyperparameter tweaking or feature engineering. [97] is a web-based visual programming environment for data science.…”

Section: Deep-learning Playground Appsmentioning

confidence: 99%

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Front-end deep learning web apps development and deployment: a review

Goh

Abas

2022

Appl Intell

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Machine learning and deep learning models are commonly developed using programming languages such as Python, C++, or R and deployed as web apps delivered from a back-end server or as mobile apps installed from an app store. However, recently front-end technologies and JavaScript libraries, such as TensorFlow.js, have been introduced to make machine learning more accessible to researchers and end-users. Using JavaScript, TensorFlow.js can define, train, and run new or existing, pre-trained machine learning models entirely in the browser from the client-side, which improves the user experience through interaction while preserving privacy. Deep learning models deployed on front-end browsers must be small, have fast inference, and ideally be interactive in real-time. Therefore, the emphasis on development and deployment is different. This paper aims to review the development and deployment of these deep-learning web apps to raise awareness of the recent advancements and encourage more researchers to take advantage of this technology for their own work. First, the rationale behind the deployment stack (front-end, JavaScript, and TensorFlow.js) is discussed. Then, the development approach for obtaining deep learning models that are optimized and suitable for front-end deployment is then described. The article also provides current web applications divided into seven categories to show deep learning potential on the front end. These include web apps for deep learning playground, pose detection and gesture tracking, music and art creation, expression detection and facial recognition, video segmentation, image and signal analysis, healthcare diagnosis, recognition, and identification.

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The Effects of Font-Based Emotional Expression Messenger on User Interaction in Text-Based Communication

Kim,

Kim

et al. 2024

adr

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An Application of Artificial Intelligence for Detecting Emotions in Neuromarketing

Cited by 5 publications

References 8 publications

Fear Detection in Multimodal Affective Computing: Physiological Signals versus Catecholamine Concentration

Fear Detection in Multimodal Affective Computing: Physiological Signals versus Catecholamine Concentration

Front-end deep learning web apps development and deployment: a review

The Effects of Font-Based Emotional Expression Messenger on User Interaction in Text-Based Communication

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