Abstract
As digital transformation increasingly reshapes human-computer interaction, migrating from purely functional systems to human-centric, empathetic environments represents a significant paradigm shift. Affective computing bridges this gap by enabling machines to recognize and adapt to human emotional states through computational modeling. This study investigates the detection of sequential patterns in physiological transitions during emotional episodes using the ARMADA algorithm, addressing the need for robust temporal analysis in smart, responsive ecosystems. By analyzing datasets with synchronized cardiovascular, electrodermal, and skin temperature features, the study validated ARMADA’s effectiveness for temporal pattern discovery. The generated association rules were found to be statistically significant via permutation tests, with over 50 rules meeting rigorous cross-dataset validation criteria. Quantitative analysis revealed significant findings regarding the predominance of moderate affective states, HRV synchronicity, and psychological lag in transition logic, offering actionable insights for the next generation of emotionally intelligent digital platforms.
Paper Type
Full Paper
DOI
10.62036/ISD.2026.163
Using the ARMADA Algorithm to Analyze Physiological Marker Patterns in Presumed Emotional States
As digital transformation increasingly reshapes human-computer interaction, migrating from purely functional systems to human-centric, empathetic environments represents a significant paradigm shift. Affective computing bridges this gap by enabling machines to recognize and adapt to human emotional states through computational modeling. This study investigates the detection of sequential patterns in physiological transitions during emotional episodes using the ARMADA algorithm, addressing the need for robust temporal analysis in smart, responsive ecosystems. By analyzing datasets with synchronized cardiovascular, electrodermal, and skin temperature features, the study validated ARMADA’s effectiveness for temporal pattern discovery. The generated association rules were found to be statistically significant via permutation tests, with over 50 rules meeting rigorous cross-dataset validation criteria. Quantitative analysis revealed significant findings regarding the predominance of moderate affective states, HRV synchronicity, and psychological lag in transition logic, offering actionable insights for the next generation of emotionally intelligent digital platforms.
Recommended Citation
Mańczak, P. & Zawadzka, T.(2026). Using the ARMADA Algorithm to Analyze Physiological Marker Patterns in Presumed Emotional States. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.163