Beyond Context: Identifying Individuals from Physiological Signals Across Experiments

Authors

  • Pedro Rodrigues Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics

DOI:

https://doi.org/10.56394/aris2.v5i1.54

Keywords:

Biometric Systems, User-Dependent, Signal Processing, Electrocardiography (ECG), Electrodermal Activity (EDA)

Abstract

This study evaluates the feasibility of using ECG and EDA signals for biometric identification in diverse VR contexts. Participants were first assessed in a controlled puzzle-based VR game and later in a dynamic exergame, separated by a two-year temporal gap. The proposed CNN model achieved 98.9% accuracy in the controlled environment, confirming the reliability of physiological signals for biometric identification. However, a 24% performance decline was observed in the dynamic exergame setting, highlighting the critical challenge of contextual dependence in biometric systems. Unlike most existing studies, which examine time spans of no more than a week, this work provides new insights into the impact of long-term variability and task-induced changes on identification performance. The findings underscore the importance of addressing contextual and temporal variability to improve the robustness and adaptability of biometric models.

References

K. Davis and T. Ruotsalo, “Physiological data: Challenges for privacy and ethics,” arXiv [cs.CY], 2024.

E. Piciucco, E. Di Lascio, E. Maiorana, S. Santini, and P. Campisi, “Biometric recognition using wearable devices in real-life settings,” Pattern Recognit. Lett., vol. 146, pp. 260–266, 2021. DOI: https://doi.org/10.1016/j.patrec.2021.03.020

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A. D. Diaz Alonso, C. M. Travieso, J. B. Alonso, M. K. Dutta, and A. Singh, “Biometric personal identification system using biomedical sensors,” in 2016 2nd International Conference on Communication Control and Intelligent Systems (CCIS), 2016, pp. 104–109. DOI: https://doi.org/10.1109/CCIntelS.2016.7878210

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Published

2025-05-16

How to Cite

[1]
P. Rodrigues, “Beyond Context: Identifying Individuals from Physiological Signals Across Experiments”, ARIS2-Journal, vol. 5, no. 1, pp. 86–99, May 2025.