In contemporary society, where chronic stress is increasingly prevalent, this study aims to propose a multi-parametric wearable platform suitable for real-life monitoring and to validate its ability to acquire four physiological signals relevant for the stress response (electrocardiogram, respiration, galvanic skin response, photoplethysmogram). Secondly, it seeks to conduct a statistical analysis on the derived features both to identify the physiological signals necessary for a comprehensive analysis of the stress response and to understand the distinct contribution of each one. The results obtained revealed at least two statistically significant features from each of the physiological signals considered, confirming the importance of a multi-parametric approach for an accurate stress response analysis. Additionally, the proposed statistical hypotheses allowed to determine how each physiological signal contributes differently to characterize various aspects of the stress response. For these reasons, this study could represent a benchmark for future investigations aiming to classify the stress response.

在慢性压力日益普遍的当代社会，本研究旨在提出一种适用于现实生活监测的多参数可穿戴平台，并验证其获取与压力反应相关的四种生理信号（心电图、呼吸、皮肤电反应、光电容积脉搏波）的能力。其次，它试图对衍生特征进行统计分析，以确定全面分析压力反应所需的生理信号，并了解每个信号的不同贡献。获得的结果揭示了所考虑的每个生理信号中至少有两个具有统计学意义的特征，证实了多参数方法对于准确应力反应分析的重要性。此外，提出的统计假设允许确定每个生理信号如何以不同的方式表征应激反应的各个方面。由于这些原因，这项研究可以代表未来旨在对压力反应进行分类的研究的基准。