EmoSens – The Proposal of System for Recognition of Emotion with SDK Affectiva and Various Sensors

Magdin, Martin; Kohutek, M.; Koprda, Štefan; Balogh, Zoltán

doi:10.1007/978-3-030-26763-6_39

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…Skin conductance amplitude and response latency provide direct measures of arousal when watching TV commercials, unlike selfreported measures that are often based on later memory recall. Although GSR cannot independently relate to emotional valence, some of the Neuromarketing studies, i.e., Cherubino et al [42], Çakar et al [34], Ungureanu et al [53], Magdin et al [71], Goyal and Singh [54], and Singh et al [56] have used skin conductance along with heart rate to measure the consumer attention and focus on the TVC. [43], b fMRI imaging [50], and c eye tracking for online booking appointment [48]…”

Section: Neural Response Recording Techniquesmentioning

confidence: 99%

Technological advancements and opportunities in Neuromarketing: a systematic review

et al. 2020

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Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an effective tool for recognizing the unspoken response of consumers to the marketing stimuli. This article presents the very first systematic review of the technological advancements in Neuromarketing field over the last 5 years. For this purpose, authors have selected and reviewed a total of 57 relevant literatures from valid databases which directly contribute to the Neuromarketing field with basic or empirical research findings. This review finds consumer goods as the prevalent marketing stimuli used in both product and promotion forms in these selected literatures. A trend of analyzing frontal and prefrontal alpha band signals is observed among the consumer emotion recognition-based experiments, which corresponds to frontal alpha asymmetry theory. The use of electroencephalogram (EEG) is found favorable by many researchers over functional magnetic resonance imaging (fMRI) in video advertisement-based Neuromarketing experiments, apparently due to its low cost and high time resolution advantages. Physiological response measuring techniques such as eye tracking, skin conductance recording, heart rate monitoring, and facial mapping have also been found in these empirical studies exclusively or in parallel with brain recordings. Alongside traditional filtering methods, independent component analysis (ICA) was found most commonly in artifact removal from neural signal. In consumer response prediction and classification, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) have performed with the highest average accuracy among other machine learning algorithms used in these literatures. The authors hope, this review will assist the future researchers with vital information in the field of Neuromarketing for making novel contributions.

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Section: Neural Response Recording Techniquesmentioning

confidence: 99%

Technological advancements and opportunities in Neuromarketing: a systematic review

et al. 2020

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“…As each of the mentioned approaches to analysing drivers' emotions-the analysis of self-report, physiological signals or facial expressions-is associated with certain caveats, it is preferable to estimate drivers' valence and arousal levels by using a multimodal approach. There is an increasing movement towards the use of multimodal emotion-prediction systems [70][71] [72], with a meta-analysis reporting that unimodal systems are outperformed 85% of the time by multimodal systems for both natural versus acted emotions [73]. Such approaches allow for the inherent multidimensional nature of emotions and the context-dependent nature of their expression-that in some situations, a person may not reveal their emotional state outwardly, in which case their physiological responses might offer the best insight into how they are feeling.…”

Section: Multimodal Approachmentioning

confidence: 99%

A Comparison of Unimodal and Multimodal Measurements of Driver Stress in Real-World Driving Conditions

Spencer¹,

Ia²,

Suga³

et al. 2020

Preprint

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Objectively measuring drivers’ emotions in real-world conditions is a challenging endeavour. This study investigated whether drivers’ emotional responses were captured more meaningfully by unimodal measurements or by a multimodal machine-learning approach. Ten participants drove a 23-mile route around Sunnyvale, California, while their heart rate, breathing rate and facial expressions were recorded. At regular intervals, participants indicated how they were feeling. After the study, independent observers reviewed a sample of the videotaped sessions, classifying the participants as experiencing high or low levels of stress according to their behaviour. The degree to which drivers’ self-report scores, single physiological data streams and facial behaviour - as judged by a facial recognition machine classifier - reflected their actual stress levels was compared to the multimodal algorithmic estimates which combined physiological and facial data outputs. The results showed that, compared to the other single data modes, the multimodal approach captured how the participants were feeling in a way which most meaningfully corresponded with their observed behaviour. The findings re-affirm the need for multimodal emotion-recognition systems for capturing driver stress. The limitations of the study are also discussed.

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Cognitive and Emotional Monitoring with Inexpensive Wrist-Worn Consumer-Grade Wearables

Valdez

Forlines

2023

2023 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Work

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EmoSens – The Proposal of System for Recognition of Emotion with SDK Affectiva and Various Sensors

Cited by 3 publications

References 29 publications

Technological advancements and opportunities in Neuromarketing: a systematic review

Technological advancements and opportunities in Neuromarketing: a systematic review

A Comparison of Unimodal and Multimodal Measurements of Driver Stress in Real-World Driving Conditions

Cognitive and Emotional Monitoring with Inexpensive Wrist-Worn Consumer-Grade Wearables

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