StressBal: Personalized Just-in-time Stress Intervention with Wearable and Phone Sensing

Han, Yunjo; Lee, Hyemin; Toshnazarov, Kobiljon; Noh, Youngmin; Lee, Uichin

doi:10.1145/3544793.3560324

“…In datasets that focus on real-life contexts, the primary method for assigning ground truth labels is based on surveys. As seen in many articles that covered real-life experiments [30,34,36,37,40,45,52], a dataset [86] provides labeled biosignals through Ecological Momentary Assessments (EMAs). Ecological Momentary Assessment (EMA) is a research method used in psychology and related fields to assess individuals' real-time experiences, behaviors, and physiological states in their natural environments [91].…”

Section: Datasets For Personalized Stress Detectionmentioning

confidence: 99%

“…HRV Variation in time intervals between heartbeats; serves as an indicator of the body's adaptability to stress. Higher HRV is generally associated with a more flexible autonomic nervous system and better resilience to stressors [18][19][20]22,26,27,40,42,47]. LF Frequency activity (0.04 -0.15Hz); often associated with sympathetic nervous system activity.…”

mentioning

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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“…In datasets that focus on real-life contexts, the primary method for assigning ground-truth labels is based on surveys. As seen in many articles that covered real-life experiments [32,36,38,39,42,47,54], a dataset [88] provides labeled biosignals through Ecological Momentary Assessments (EMAs). EMA is a research method used in psychology and related fields to assess individuals' real-time experiences, behaviors, and physiological states in their natural environments [93].…”

Section: Datasets For Personalized Stress Detectionmentioning

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.

show abstract