发表机构
Wroclaw University of Science and Technology（弗罗茨瓦夫科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究评估PPG-based SSL在真实生活情感检测中的效能，发现通用SSL表征无法胜任主观情感推理，微调时纳入个人数据才是性能关键，公开了RL-PPG编码器相关资源。
AI 中文摘要
自监督学习（SSL）虽能从光体积描记（PPG）等含噪声、无约束的生理信号中有效提取通用表征，但其在高度主观任务中的适用性尚未得到验证。本研究评估了基于PPG的SSL在真实生活强烈情感检测中的效能：首先，我们在无约束的真实生活数据上预训练了真实生活PPG编码器（RL-PPG）；作为严格的健全性检查，我们证明该表征可极佳地迁移至客观的物理活动识别任务，在留一被试交叉验证（LOSO）中，其性能较基线提升近5倍。然而，当将这些相同的通用表征应用于主观的真实生活情感检测任务时，在LOSO协议下其性能未能超过朴素基线。我们采用跨时间验证策略证实，微调阶段纳入个体的个人数据是预测性能的主要驱动因素，其重要性超过了群体级预训练的益处。最终，我们的研究结果表明，在所评估的场景中，通用SSL表征可能不足以支持主观情感推理，这提示个性化很可能是真实世界情感识别的关键组成部分。为支持未来研究，我们公开了代码及预训练RL-PPG编码器权重。
英文摘要
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.
Comments9 pages, 4 Figures, 2 Tables, Accepted as the 14th International Conference on Affective Computing and Intelligent Interaction (ACII 2026)
