Human Inspired Technology Research Centre, University of Padua, 35121 Padua, Italy.
Digital Health Research, Centre for Digital Health and Wellbeing, Fondazione Bruno Kessler, 38123 Trento, Italy.
Sensors (Basel). 2024 May 18;24(10):3221. doi: 10.3390/s24103221.
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.
压力是人类生活中自然存在但潜在有害的方面，需要进行有效的管理，特别是在面临巨大压力的情况下。本文对使用可穿戴技术的个性化压力检测模型进行了范围综述。我们采用 PRISMA-ScR 框架进行严格的方法学构建，系统地分析了来自 Scopus、IEEE Xplore 和 PubMed 等主要数据库的文献。我们的重点是生物信号、人工智能方法、数据集、可穿戴设备以及现实世界中的实施挑战。本综述介绍了压力及其生物学机制的概述，详细说明了文献搜索的方法，并综合了研究结果。结果表明，生物信号，特别是 EDA 和 PPG，常用于压力检测，在多模态环境中具有潜在的可靠性。虽然发现了向深度学习模型发展的趋势，但与传统方法的有限比较需要进一步研究。人们对数据集的代表性和可穿戴技术部署中的实际挑战表示关注，其中包括与数据质量和隐私相关的问题。未来的研究应致力于开发全面的数据集，并探索不仅准确而且计算效率高且以用户为中心的人工智能技术，从而缩小理论模型和实际应用之间的差距，提高实际场景中压力检测系统的有效性。
