Continuous mental stress level assessment using electrocardiogram and electromyogram signals

Pourmohammadi, Sara; Maleki, Ali

doi:10.1016/j.bspc.2021.102694

Cited by 13 publications

(11 citation statements)

References 30 publications

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“…The model includes fuzzy inference system and fuzzy clustering algorithms. The result showed that there was a strong correlation between pressure and perceived pressure, with a specific correlation coefficient of 0.959 (Pourmohammadi and Maleki, 2021 ). Azam et al built an ensemble classifier with 12 selected PPG features to detect social stress, and achieved an accuracy of 92.8%, and also showed that the median frequency and average frequency are the most effective features for stress detection (Azam et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

A novel physiological feature selection method for emotional stress assessment based on emotional state transition

Xing

et al. 2023

Front. Neurosci.

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The connection between emotional states and physical health has attracted widespread attention. The emotional stress assessment can help healthcare professionals figure out the patient's engagement toward the diagnostic plan and optimize the rehabilitation program as feedback. It is of great significance to study the changes of physiological features in the process of emotional change and find out subset of one or several physiological features that can best represent the changes of psychological state in a statistical sense. Previous studies had used the differences in physiological features between discrete emotional states to select feature subsets. However, the emotional state of the human body is continuously changing. The conventional feature selection methods ignored the dynamic process of an individual's emotional stress in real life. Therefore, a dedicated experimental was conducted while three peripheral physiological signals, i.e., ElectroCardioGram (ECG), Galvanic Skin Resistance (GSR), and Blood Volume Pulse (BVP), were continuously acquired. This paper reported a novel feature selection method based on emotional state transition, the experimental results show that the number of physiological features selected by the proposed method in this paper is 13, including 5 features of ECG, 4 features of PPG and 4 features of GSR, respectively, which are superior to PCA method and conventional feature selection method based on discrete emotional states in terms of dimension reduction. The classification results show that the accuracy of the proposed method in emotion recognition based on ECG and PPG is higher than the other two methods. These results suggest that the proposed method can serve as a viable alternative to conventional feature selection methods, and emotional state transition deserves more attention to promote the development of stress assessment.

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Section: Introductionmentioning

confidence: 99%

A novel physiological feature selection method for emotional stress assessment based on emotional state transition

Xing

et al. 2023

Front. Neurosci.

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“…Previous work mainly focused on mental stress detection based on peripheral signals such as electrocardiography and electrodermal activity, and yielded positive results [10][11][12]. However, these studies may overlook several critical issues.…”

Section: Introductionmentioning

confidence: 99%

Exploring EEG characteristics of multi-level mental stress based on human–machine system

Yao,

Gu,

Wang

et al. 2023

J. Neural Eng.

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Objective:The understanding of cognitive states is important for the development of human-machine systems, and one of the fundamental but challenging issues is the understanding and assessment of the operator's mental stress state in real task scenarios. Approach: In this paper, a virtual unmanned vehicle (UAV) driving task with multi-challenge-level was created to explore the operator's mental stress, and the human brain activity during the task was tracked in real time via EEG. A mental stress analysis dataset for the virtual UAV task was then developed and used to explore the neural activation patterns associated with mental stress activity. Finally, a multiple attention-based convolutional neural network (MACN) was constructed for automatic stress assessment using the extracted stress-sensitive neural activation features.Main Results:The statistical results of EEG power spectral density (PSD) showed that frontal θ-PSD decreased with increasing task difficulty, and central β-PSD increased with increasing task difficulty, indicating that neural patterns showed different trends under different levels of mental stress. The performance of the proposed MACN was evaluated based on the dimensional model, and results showed that average three-class classification accuracies of 89.49% / 89.88% were respectively achieved for arousal / valence. Significance: The results of this paper suggest that objective assessment of mental stress in a human-machine system based on a virtual UAV scenario is feasible, and the proposed method provides a promising solution for cognitive computing and applications in human-machine tasks.

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“…pNN50Percentage of NN intervals differing by >50ms; indicator of parasympathetic activity and heart regulation. Monitoring changes in pNN50 provides insights into the dynamic regulation of the heart and its response to stressors [19,20,26,27,[34][35][36]47,52]. HRV Variation in time intervals between heartbeats; serves as an indicator of the body's adaptability to stress.…”

mentioning

confidence: 99%

“…LF Frequency activity (0.04 -0.15Hz); often associated with sympathetic nervous system activity. LF variations may indicate the sympathetic influence on heart rate during stress [17,19,20,26,27,34,35]. HF Frequency activity (0.15 -0.40Hz); primarily associated with parasympathetic nervous system activity and respiratory influences.…”

mentioning

confidence: 99%

“…HF Frequency activity (0.15 -0.40Hz); primarily associated with parasympathetic nervous system activity and respiratory influences. HF variations may indicate changes in relaxation and parasympathetic dominance [19,20,26,27,34,35]. LF/HF Ratio of Low Frequency to High Frequency; considered a measure of the balance between sympathetic and parasympathetic nervous system activity.…”

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confidence: 99%

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Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review

Bolpagni,

Pardini,

Dianti

et al. 2024

Preprint

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Stress is a natural yet potentially harmful aspect of human life, necessitating effective management, particularly during overwhelming experiences. This paper presents a scoping review of personalized stress detection models using wearable technology. Employing the PRISMA-ScR framework for rigorous methodological structuring, we systematically analyzed literature from key databases including Scopus, IEEE Xplore, and PubMed. Our focus was on biosignals, AI methodologies, datasets, wearable devices, and real-world implementation challenges. The review presents an overview of stress and its biological mechanisms, details the methodology for the literature search, and synthesizes the findings. It shows that biosignals, especially EDA and PPG, are frequently utilized for stress detection and demonstrate potential reliability in multimodal settings. Evidence for a trend towards deep learning models was found, although the limited comparison with traditional methods calls for further research. Concerns arise regarding the representativeness of datasets and practical challenges in deploying wearable technologies, which include issues related to data quality and privacy. Future research should aim to develop comprehensive datasets and explore AI techniques that are not only accurate but also computationally efficient and user-centric, thereby closing the gap between theoretical models and practical applications to improve the effectiveness of stress detection systems in real scenarios.

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Continuous mental stress level assessment using electrocardiogram and electromyogram signals

Cited by 13 publications

References 30 publications

A novel physiological feature selection method for emotional stress assessment based on emotional state transition

A novel physiological feature selection method for emotional stress assessment based on emotional state transition

Exploring EEG characteristics of multi-level mental stress based on human–machine system

Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review

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