Utilizing representation learning for ECG-based authentication
A Woźniak, J Szyndlarewicz, D Kunc, S Saganowski
ACIIDS 2026 475–489
2026
Dominika Kunc
Doktorant
Dominika received the Eng. degree in Computer Science in 2020 and the M.Sc. degree in Computer Science, specialization in Data Science in 2021, both from Wrocław University of Science and Technology (WUST), Poland. She was one of the Top 10 Computer Science and Management faculty graduates in 2021. She is a Ph.D. student in computer science at WUST currently working with the Emognition research team. Her research interests include affective computing, physiological signals, and machine learning.
Grupy badawcze
Wykształcenie
- 2020
inżynier informatyki, Wydział Podstawowych Problemów Techniki
Politechnika Wrocławska
- 2021
magister informatyki, specjalność Data Science, Wydział Informatyki i Zarządzania
Politechnika Wrocławska
Publikacje20
Dominance and complementarity in cross-modal representation learning for wearable time series
D Kunc, J Komoszyńska, P Kazienko, S Saganowski, K Avramidis, T Feng, …
Information Fusion 135, 1–15
2026
Using artificial intelligence to generate affective images: methodology and initial library
M Behnke, D Kunc, S Saganowski, M Kłoskowski, M Klichowski, W Krzyżaniak, …
Advances in Methods and Practices in Psychological Science 9(1), 1–28
2026
HeartAuth: ECG-based biometric authentication system using smartwatch signals
J Szyndlarewicz, A Woźniak, K Zając, K Tomczyk, D Kunc, S Saganowski
IEEE 2026
2026
Scaling representation learning from ubiquitous ECG with State-Space models
D Kunc, B Perz, P Kazienko, S Saganowski, K Avramidis, K Adsul, …
IEEE Journal of Biomedical and Health Informatics 28(10), 5877–5889 · Cytowane przez 13
2024
Designing and executing a large-scale real-life affective study
J Komoszyńska, D Kunc, B Perz, A Hebko, P Kazienko, S Saganowski
Materiały konferencyjne · Cytowane przez 6
2024
Ethical considerations and checklist for affective research with wearables
M Behnke, S Saganowski, D Kunc, P Kazienko
IEEE Transactions on Affective Computing 15(1), 50–62 · Cytowane przez 21
2024
Embracing subjectivity in affective research: naturalistic and controlled settings
D Kunc, J Komoszyńska, S Saganowski, P Kazienko, KS Quigley, LF Barrett
Materiały konferencyjne · Cytowane przez 2
2024
Unsupervised learning for physiological signals in real-life emotion recognition using wearables
Materiały konferencyjne · Cytowane przez 2
2022
Emognition system - wearables, physiology, and machine learning for real-life emotion capturing
D Kunc, J Komoszyńska, B Perz, S Saganowski, P Kazienko
Materiały konferencyjne · Cytowane przez 7
2023
Lessons learned from developing emotion recognition system for everyday life
S Saganowski, J Miszczyk, D Kunc, D Lisouski, P Kazienko
SenSys '22 1047–1054 · Cytowane przez 6
2023
Real-life validation of emotion detection system with wearables
D Kunc, J Komoszyńska, B Perz, P Kazienko, S Saganowski
IWINAC 2022 45–54 · Cytowane przez 10
2022
The cold start problem and per-group personalization in real-life emotion recognition with wearables
S Saganowski, D Kunc, B Perz, J Komoszyńska, M Behnke, P Kazienko
Materiały konferencyjne · Cytowane przez 14
2022
Emognition dataset: emotion recognition with self-reports, facial expressions, and physiology using wearables
S Saganowski, J Komoszyńska, M Behnke, B Perz, D Kunc, BP Klich, P Kazienko, …
Scientific Data 9, 1–11 · Cytowane przez 89
2022
A system for collecting emotionally annotated physiological signals in daily life using wearables
S Saganowski, M Behnke, J Komoszyńska, D Kunc, B Perz, P Kazienko
Materiały konferencyjne · Cytowane przez 15
2021
How to catch them all? Enhanced data collection for emotion recognition in the field
M Dzieżyc, J Komoszyńska, S Saganowski, M Boruch, J Dziwiński, K Jabłońska, D Kunc, P Kazienko
Materiały konferencyjne · Cytowane przez 13
2021