Take it Personally: The Limits of General SSL Representations for Real-Life PPG Emotion Detection

[2608.14675] Take it Personally: The Limits of General SSL Representations for Real-Life PPG Emotion Detection Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2608.14675 (cs) [Submitted on 3 Aug 2026] Title: Take it Personally: The Limits of General SSL Representations for Real-Life PPG Emotion Detection Authors: Dominika Kunc , Przemysław Kazienko , Stanisław Saganowski View a PDF of the paper titled Take it Personally: The Limits of General SSL Representations for Real-Life PPG Emotion Detection, by Dominika Kunc and 2 other authors View PDF HTML (experimental) Abstract: While Self-Supervised Learning (SSL) effectively extracts general representations from noisy, unconstrained physiological signals such as photoplethysmography (PPG), its suitability for highly subjective tasks remains unproven. In this work, we evaluate the efficacy of PPG-based SSL for real-life intense emotion detection. First, we pretrain a Real-Life PPG encoder (RL-PPG) on unconstrained, real-life data. As a rigorous sanity check, we demonstrate that these representations transfer exceptionally well to an objective physical activity recognition task, yielding almost 5-fold increase in performance over baselines in a leave-one-subject-out evaluation (LOSO). However, when applied to a~subjective real-life emotion detection task, these same general representations fail to surpass naive baselines under the LOSO protocol. Using an Across-Time validation strategy, we establish that incorporating an individual's personal data during fine-tuning is the main driver of predictive performance, outweighing the benefits of population-level pretraining. Ultimately, our findings indicate that in the evaluated scenario, general SSL representations may be insufficient for subjective affective inference, suggesting that personalization is likely a key component for real-world emotion recognition. To support future research, we share the code and pretrained RL-PPG~encoder~weights. Comments: 9 pages, 4 Figures, 2 Tables, Accepted as the 14th International Conference on Affective Computing and Intelligent Interaction (ACII 2026) Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2608.14675 [cs.LG] (or arXiv:2608.14675v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.14675 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Dominika Kunc [ view email ] [v1] Mon, 3 Aug 2026 22:25:08 UTC (538 KB) Full-text links: Access Paper: View a PDF of the paper titled Take it Personally: The Limits of General SSL Representations for Real-Life PPG Emotion Detection, by Dominika Kunc and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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