Stress Detection Using Adaptive Threshold Methodology

Machiraju, Srivasthasva Srinivas; Konijeti, Nikhil; Batchu, Akhil; Tata, Naveen

doi:10.1109/icces48766.2020.9137908

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…At some point in life, every individual is exposed to a stressful scenario and will react accordingly. If an individual can cope with stressful scenarios, the next time a similar scenario arises, the individual won't have as much of a stressful impact [5]. Similarly, if an individual cannot cope with a stressful situation and is repeatedly exposed to a similar situation, the individual will develop chronic stress [6].…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%

Multimodal Hierarchical CNN Feature Fusion for Stress Detection

2023

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“…Acute stress is a common occurrence in daily life and keeps the stress response system active. However, if we are exposed to stressors over an extended time, we will suffer from the adverse effects of chronic stress [2]. It will affect the immunological system, endocrine system, and brain.…”

Section: Introductionmentioning

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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