Automated Recognition of Affect and Stress evoked by Audio-Visual stimuli

Markova, Valentina; Ganchev, Todor

doi:10.1109/balkanlight.2018.8546887

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…They predicted emotional self-ratings and the Big 5 Personality traits from measured physiological signals. This database was also used in studies [55], [56]. The ASCERTAIN Dataset is available on request.…”

Section: Test Databasesmentioning

confidence: 99%

Multimodal Features for Detection of Driver Stress and Fatigue: Review

Němcová

Svozilová

Bucsuházy

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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Driver fatigue and stress significantly contribute to higher number of car accidents worldwide. Although, different detection approaches have been already commercialized and used by car producers (and third party companies), research activities in this field are still needed in order to increase the reliability of these alert systems. Also, in the context of automated driving, the driver mental state assessment will be an important part of cars in future. This paper presents state-of-the-art review of different approaches for driver fatigue and stress detection and evaluation. We describe in details various signals (biological, car and video) and derived features used for these tasks and we discuss their relevance and advantages. In order to make this review complete, we also describe different datasets, acquisition systems and experiment scenarios.

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Section: Test Databasesmentioning

confidence: 99%

Multimodal Features for Detection of Driver Stress and Fatigue: Review

Němcová

Svozilová

Bucsuházy

et al. 2021

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…Only a few studies have used the most recent datasets, WAUC and MAUS, in their analyses. Existing works show that the majority of the works are carried on timefrequency domain [44], [62], [83], [84], [85], [86], [87], [88], subject dependent [44], [62], [77], [83], [84], [89], and using machine learning models [44], [62], [83], [84], [89], [90]. Few researchers used traditional deep learning techniques [82], [85], [86], [87], [88].…”

Section: F Comparison Studymentioning

confidence: 99%

Multimodal Hierarchical CNN Feature Fusion for Stress Detection

2023

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Stress is one of the most severe concerns in modern life. High-level stress can create various diseases or loss of focus and productivity at work. Being under stress prevents people from recognizing their stress levels, so early stress detection is essential. Recently, multimodal fusion has enhanced the performance of stress detection models using Deep Learning (DL) techniques. The low, mid, and high-level features of a Convolutional Neural Network (CNN) are discriminative. A comprehensive feature representation can be obtained by fusing all three levels of CNN's features. This study mainly focuses on detecting stress by exploiting these advantages using a multimodal hierarchical CNN feature fusion. The two multimodal physiological signals used in this study are Electrodermal activity (EDA) and Electrocardiogram (ECG). We develop a hierarchical feature set by concatenating multi-level CNN features for each modality. Multimodal fusion on both hierarchical feature sets is performed using the Multimodal Transfer Module (MMTM). The experiments are carried out with raw frequency domain data and the features from the frequency bands to study the effectiveness of both. The model's performance is compared to the different combinations of hierarchical features from low, mid, and high levels. To verify the generalizability, the proposed approach has been evaluated on four benchmark datasets -ASCERTAIN, CLAS, MAUS, and WAUC. The proposed method showed its effectiveness by outperforming existing models by 1-2%, respectively, on frequency band features. It is observed that the hierarchical feature set from all three levels performed better than all other combinations by 2-4%. As a result, this strategy can be a useful addition to stress detection.

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“…Table 2 provides an overview of the performance metrics. Previous works demonstrate that the most of the work is done on time-frequency domain [37]- [39], [47], [49]- [51], [53]- [57], subject-dependent [38], [47], [49], [50], [53]- [56], [58] and using machine learning models [38], [47], [49], [50], [53], [54], [57], [58]. Few researchers used traditional deep learning techniques [36], [48], [51], [55], [56].…”

Section: F Comparison With Existing Workmentioning

confidence: 99%

Hierarchical Autoencoder Frequency Features for Stress Detection

Kuttala,

Subramanian,

Oruganti

2023

IEEE Access

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Stress has a significant negative impact on people, which has made it a primary social concern. Early stress detection is essential for effective stress management. This study proposes a Deep Learning (DL) method for effective stress detection using multimodal physiological signals -Electrocardiogram (ECG) and Electrodermal activity (EDA) . The extensive latent feature representation of DL models has yet to be fully explored. Hence, this paper proposes a hierarchical autoencoder feature fusion on the frequency domain. The latent representations from different layers of AutoEncoders(AE) are combined and given as input to the classifier -Convolutional Recurrent Neural Network with Squeeze and Excitation (CRNN-SE) model. A two-set performance comparison is performed (i) performance on frequency band features, and raw data are compared. (ii) autoencoders trained on three cost functions -Mean Squared Error (MSE), Kullback-Leibler (KL) divergence, and Cosine similarity performance are compared on frequency band features and raw data. To verify the generalizability of our approach, we tested it on four benchmark datasets-WAUC, CLAS, MAUS and ASCERTAIN. Results show that frequency band features showed better results than raw data by 4-8%, respectively. MSE loss produced better results than other losses for both frequency band features and raw data by 3-7%, respectively. The proposed approach considerably outperforms existing stress detection models that are subject-independent by 1-2%, respectively.

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Automated Recognition of Affect and Stress evoked by Audio-Visual stimuli

Cited by 7 publications

References 8 publications

Multimodal Features for Detection of Driver Stress and Fatigue: Review

Multimodal Features for Detection of Driver Stress and Fatigue: Review

Multimodal Hierarchical CNN Feature Fusion for Stress Detection

Hierarchical Autoencoder Frequency Features for Stress Detection

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