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. 2024 May 18;24(10):3221.
doi: 10.3390/s24103221.

Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review

Marco Bolpagni  1   2 , Susanna Pardini  2 , Marco Dianti  2 , Silvia Gabrielli  2
Affiliations

Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review

Marco Bolpagni et al. Sensors (Basel). .

Abstract

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.

Keywords: Internet of Things (IoT); PRISMA framework; artificial intelligence (AI); personalized stress detection; scoping review; stress; wearables.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Number of studies published in each category over the past decade.
Figure 2
Figure 2
PRISMA flowchart of this scoping review.
Figure 3
Figure 3
Number of devices capable of capturing raw biosignals released over the past decade.
Figure 4
Figure 4
Number of studies grouped by AI approach.
Figure 5
Figure 5
1D CNN in signal processing. Adapted from [78].
Figure 6
Figure 6
Transformer-based architecture for signal classification. Adapted from [79].
See this image and copyright information in PMC

References

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