Emotion classification in arousal-valence dimension using discrete affective keywords tagging

Henia, Wiem Mimoun Ben; Lachiri, Zied

doi:10.1109/icemis.2017.8272991

Cited by 16 publications

(11 citation statements)

References 23 publications

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“…In these investigations different sensors augmented with machine learning were presented, which subsequently yield in emotion detection system. Authors of study [10,11,13,14,17] used benchmark datasets for the emotion classification. The authors of the study [10] used the Augsburg dataset of physiological signals for emotion detection, which has only twenty-five recordings for each emotion (joy, anger, pleasure, and sadness), yielding a dataset of only a hundred instances.…”

Section: Discussion and Analysismentioning

confidence: 99%

“…In study [13,14] Mahnob-Hci data set is used for emotion classification of nine emotions on arousal and valance scale. Physiological signals were collected using cameras that, due to the effects of environmental variables such as light, temperature, and so on, could not be used in real time.…”

Section: Discussion and Analysismentioning

confidence: 99%

“…The ratio of data used for training and testing is missing in the manuscript. The trained model in study [13] is implemented on RPi in study [14]. In study [17] both Augsburg and DEAP datasets are used for emotion classification.…”

Section: Discussion and Analysismentioning

confidence: 99%

“…MAHNOB-HCI dataset of physiological signals was used for emotion classification into valence and arousal classes by [14]. This dataset has the physiological signals (ECG, GSR, ST, and respiration amplitude) of twenty-four volunteers induced by showing twenty movie clips.…”

Section: Literature Reviewmentioning

confidence: 99%

See 3 more Smart Citations

A Review on Physiological Signal Based Emotion Detection

Shahzad¹,

Saleem²,

Ahmed³

et al. 2021

AETiC

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Emotions are feelings that are the result of biochemical processes in the body that are influenced by a variety of factors such as one's state of mind, situations, experiences, and surrounding environment. Emotions have an impact on one's ability to think and act. People interact with each other to share their thoughts and feelings. Emotions play a vital role in the field of medicine and can also strengthen the human computer interaction. There are different techniques being used to detect emotions based on facial features, texts, speech, and physiological signals. One of the physiological signal breathing is a parameter which represents an emotion. The rational belief that different breathing habits are correlated with different emotions has expanded the evidence for a connection between breathing and emotion. In this manuscript different recent investigations about the emotion recognition using respiration patterns have been reviewed. The aim of the survey is to sum up the latest technologies and techniques to help researchers develop a global solution for emotional detection system. Various researchers use benchmark datasets and few of them created their own dataset for emotion recognition. It is observed that many investigators used invasive sensors to acquire respiration signals that makes subject uncomfortable and conscious that affects the results. The numbers of subjects involved in the studies reviewed are of the same age and race which is the reason why the results obtained in those studies cannot be applied to diverse population. There is no single global solution exist.

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Section: Discussion and Analysismentioning

confidence: 99%

Section: Discussion and Analysismentioning

confidence: 99%

Section: Discussion and Analysismentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

A Review on Physiological Signal Based Emotion Detection

Shahzad¹,

Saleem²,

Ahmed³

et al. 2021

AETiC

View full text Add to dashboard Cite

show abstract

“…Finally, psychology has made great strides in understanding the effects of emotions on user attention and engagement [44]. In accordance with the arousal-valence model, it is possible to classify the emotional engagement into active and passive engagement and positive and negative engagement [45]. The engagement level is then obtained as the sum of each Ekman emotion's probability of occurrence; hence, this can be used as an additional indicator of driving performance, as reported in [46].…”

Section: Valence and Engagementmentioning

confidence: 99%

On Driver Behavior Recognition for Increased Safety: A Roadmap

Davoli

Martalò

Cilfone

et al. 2020

Safety

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Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced.

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Affect Analysis: A Literature Survey on Student-Specific and General Users’ Affect Analysis

Asaju,

Vadapalli

2024

Lecture Notes in Networks and Systems

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Emotion classification in arousal-valence dimension using discrete affective keywords tagging

Cited by 16 publications

References 23 publications

A Review on Physiological Signal Based Emotion Detection

A Review on Physiological Signal Based Emotion Detection

On Driver Behavior Recognition for Increased Safety: A Roadmap

Affect Analysis: A Literature Survey on Student-Specific and General Users’ Affect Analysis

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