These authors have contributed equally to this work.
Version 1
: Received: 26 April 2024 / Approved: 28 April 2024 / Online: 28 April 2024 (07:15:33 CEST)
How to cite:
Bolpagni, M.; Pardini, S.; Dianti, M.; Gabrielli, S. Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review. Preprints2024, 2024041829. https://doi.org/10.20944/preprints202404.1829.v1
Bolpagni, M.; Pardini, S.; Dianti, M.; Gabrielli, S. Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review. Preprints 2024, 2024041829. https://doi.org/10.20944/preprints202404.1829.v1
Bolpagni, M.; Pardini, S.; Dianti, M.; Gabrielli, S. Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review. Preprints2024, 2024041829. https://doi.org/10.20944/preprints202404.1829.v1
APA Style
Bolpagni, M., Pardini, S., Dianti, M., & Gabrielli, S. (2024). Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review. Preprints. https://doi.org/10.20944/preprints202404.1829.v1
Chicago/Turabian Style
Bolpagni, M., Marco Dianti and Silvia Gabrielli. 2024 "Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review" Preprints. https://doi.org/10.20944/preprints202404.1829.v1
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
Personalized stress detection; Stress; Wearables; Internet of Things (IoT); Artificial Intelligence (AI); Scoping review; PRISMA framework
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
