Stressalyzer: Convolutional Neural Network Framework for Personalized Stress Classification

Sah, Ramesh Kumar; Cleveland, Michael J.; Habibi, Assal; Ghasemzadeh, Hassan

doi:10.1109/embc48229.2022.9871842

“…Two types of experiments are conducted. The first one is subject-oriented, in which the model's validation method has been used in previous works [4]. The second experiment follows a different validation approach, by randomly splitting samples among subjects.…”

Section: Resultsmentioning

confidence: 99%

“…Each sample is labelled with the task associated with the temporal position of the sliding window. We study our hypothesis by training a CNN-based model inspired by [4].…”

Section: B Experimental Setupmentioning

confidence: 99%

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Stress Lingers: Recognizing the Impact of Task Order on Design of Stress and Emotion Detection Systems

Maestro,

Banaee,

Loutfi

2023

2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology

0

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This paper examines the significance of the priming effect in designing and developing models for recognizing of affective states. Using a public dataset, often considered a benchmark in automatic stress recognition, the significance of the priming effect is explicated. Two experimental setups confirm the importance of task ordering in this problem. The results demonstrate the statistical significance of the model's confusion when the subject has previously experienced stress and illustrate the importance for the Affective Computing community to develop methods to mitigate the priming effect where the order of tasks impacts how data should be modelled.

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“…However, they can still contribute to outlining the emerging components of architectures and the strategies researchers employ to address specific problems. Discussing the particularly relevant components of the architectures introduced by researchers, we find the CNN layers [24,43,45,48,49] and self-attention mechanisms [37]. CNN layers (used in 7 out of 11 studies) are fundamental building blocks of a CNN.…”

Section: Deep Learning Approachmentioning

confidence: 99%

Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review

Bolpagni,

Pardini,

Dianti

et al. 2024

Preprint

0

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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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“…However, they can still contribute to outlining the emerging components of architectures and the strategies researchers employ to address specific problems. Discussing the particularly relevant components of the architectures introduced by researchers, we find the Convolutional Neural Network (CNN) layers [26,45,47,50,51] and self-attention mechanisms [39]. CNN layers (used in 7 out of 11 studies) are fundamental building blocks of a CNN.…”

Section: Deep Learning Approachmentioning

confidence: 99%

Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review

Bolpagni,

Pardini,

Dianti

et al. 2024

Sensors

0

View full text Add to dashboard Cite

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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Stressalyzer: Convolutional Neural Network Framework for Personalized Stress Classification

Cited by 6 publications

References 14 publications

Stress Lingers: Recognizing the Impact of Task Order on Design of Stress and Emotion Detection Systems

Stress Lingers: Recognizing the Impact of Task Order on Design of Stress and Emotion Detection Systems

Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review

Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review

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