Stress Level Assessment by a Multi-Parametric Wearable Platform: Relevance of Different Physiological Signals

Beatrice De Marchi (), Endi Agovi and Andrea Aliverti
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Beatrice De Marchi: Politecnico di Milano
Endi Agovi: Politecnico di Milano
Andrea Aliverti: Politecnico di Milano

Information Systems Frontiers, 2025, vol. 27, issue 1, No 7, 113-137

Abstract: Abstract 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.

Keywords: Stress level; Wearables; Physiological signals; Multi-parametric; Statistical analysis (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10796-024-10550-6

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