CNN Based Subject-Independent Driver Emotion Recognition System Involving Physiological Signals for ADAS

Ali, Mouhannad; Machot, Fadi Al; Mosa, Ahmad Haj; Kyamakya, Kyandoghere

doi:10.1007/978-3-319-44766-7_11

Cited by 26 publications

(22 citation statements)

References 10 publications

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“…In human-robot interaction contexts, it has been found that some affective states of humans, such as excitement, fatigue, engagement, and distractions (and likely comfort as well) [82,83], are correlated to their physiological signals including those measured using electrodermal activity (EDA), photoplethysmography (PPG), skin temperature (ST), eye tracking (ET), and electroencephalography (EEG). EDA, also known as galvanic skin response, is the measurement of conductance or resistance across the surface of one's skin, which continuously varies as one responds to various stimuli [84]. PPG is a measurement of the changes in light absorption of the skin, which is measured using a pulse oximeter; it is a periodic signal that measures cardio performance and can be used to evaluate a participant's level of arousal [85].…”

Section: Physiological Approachmentioning

confidence: 99%

Learning and Comfort in Human–Robot Interaction: A Review

Wang

Chen

et al. 2019

Applied Sciences

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Collaborative robots provide prospective and great solutions to human–robot cooperative tasks. In this paper, we present a comprehensive review for two significant topics in human–robot interaction: robots learning from demonstrations and human comfort. The collaboration quality between the human and the robot has been improved largely by taking advantage of robots learning from demonstrations. Human teaching and robot learning approaches with their corresponding applications are investigated in this review. We also discuss several important issues that need to be paid attention to and addressed in the human–robot teaching–learning process. After that, the factors that may affect human comfort in human–robot interaction are described and discussed. Moreover, the measures utilized to improve human acceptance of robots and human comfort in human–robot interaction are also presented and discussed.

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Section: Physiological Approachmentioning

confidence: 99%

Learning and Comfort in Human–Robot Interaction: A Review

Wang

Chen

et al. 2019

Applied Sciences

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“…Based on the previous points, computers can be made to understand human emotions by capturing these modalities, extracting a set of useful features from them and fusing those features in order to infer an accurate emotional state [ 9 ]. There is a growing number of sensors that can capture various physical manifestations of emotion: video recordings of facial expressions [ 10 ], vocal inflection changes [ 7 ], recording of brain waves using Electroencephalogram EEG [ 3 ], skin-surface sensing of muscle tension [ 11 ], electrocardiogram (ECG) [ 12 ], electrodermal activity (EDA) [ 13 ], skin temperature (ST) [ 14 ], etc.…”

Section: Introductionmentioning

confidence: 99%

“…In our previous work [ 13 ], we have developed a subject-independent emotion recognition approach based on cellular neural networks (CNN) and we could thereby reach

of accuracy for four emotional states (High/Low arousal and High/Low valence). Overall, one should notice the following core building bricks or aspects of relevance of/for a robust and reliable emotion recognition system: the sensors used, the number of subjects involved in training and testing, the number emotional states to be detected, the stimuli used for inducing emotions, the features extraction and selection, and the classification method.…”

Section: Introductionmentioning

confidence: 99%

A Globally Generalized Emotion Recognition System Involving Different Physiological Signals

Ali¹,

Machot

Mosa³

et al. 2018

Sensors

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Machine learning approaches for human emotion recognition have recently demonstrated high performance. However, only/mostly for subject-dependent approaches, in a variety of applications like advanced driver assisted systems, smart homes and medical environments. Therefore, now the focus is shifted more towards subject-independent approaches, which are more universal and where the emotion recognition system is trained using a specific group of subjects and then tested on totally new persons and thereby possibly while using other sensors of same physiological signals in order to recognize their emotions. In this paper, we explore a novel robust subject-independent human emotion recognition system, which consists of two major models. The first one is an automatic feature calibration model and the second one is a classification model based on Cellular Neural Networks (CNN). The proposed system produces state-of-the-art results with an accuracy rate between 80% and 89% when using the same elicitation materials and physiological sensors brands for both training and testing and an accuracy rate of 71.05% when the elicitation materials and physiological sensors brands used in training are different from those used in training. Here, the following physiological signals are involved: ECG (Electrocardiogram), EDA (Electrodermal activity) and ST (Skin-Temperature).

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“…From the perspective of applicability, there are two types of emotion recognition models: subjectdependent and subject-independent models [6,7,8,9]. Subject-dependent models are suitable to a given individual subject or a specific group and are difficult to apply to new samples.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Individual Emotion Recognition and Subgroup Analysis From Psychophysiological Signals

Zhang¹,

Traue²,

Hazer-Rau³

2018

SIPIJ

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This study involves intra-and inter-individual emotion classifications from psychophysiological signals and subgroup analysis on the influence of gender and age and their interaction on the emotion recognition. Individual classifications are conducted using a selection of feature optimization, classification and evaluation approaches. The subgroup analysis is based on the inter-individual classification. Emotion elicitation is conducted using standardized pictures in the Valence-Arousal-Dominance dimensions and affective states are classified into five different category classes. Advantageous intra-individual rates are obtained via multi-channel classification and the respiration best contributes to the recognition. High interindividual variances are obtained showing large variability in physiological responses between the subjects. Classification rates are significantly higher for women than for men for the 3-category-class of Valence. Compared to old subjects, young subjects have significantly higher rates for the 3-category-class and 2-category-class of Dominance. Moreover, young men's classification performed the best among the other subgroups for the 5-category-class of Valence/Arousal.

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CNN Based Subject-Independent Driver Emotion Recognition System Involving Physiological Signals for ADAS

Cited by 26 publications

References 10 publications

Learning and Comfort in Human–Robot Interaction: A Review

Learning and Comfort in Human–Robot Interaction: A Review

A Globally Generalized Emotion Recognition System Involving Different Physiological Signals

Individual Emotion Recognition and Subgroup Analysis From Psychophysiological Signals

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