ECG-Based Emotion Prediction in Real Life: Tackling Heterogeneity Through a Hybrid Personalization Method

Predicting people's momentary emotions in real life remains an underexplored challenge in affective computing. Wearable electrocardiography (ECG) offers a promising tool for this purpose, given its psychological relevance to emotion and increasing affordability in recent years. However, a major obstacle lies in the substantial individual differences in the associations between emotional experiences and physiological responses, which is further amplified in real-world settings. Emerging personalization methods offer a potential solution, but their utility for real-life ECG-based emotion prediction has yet to be evaluated. To address this, the present study leverages the largest real-life ECG dataset to date (N = 363) to evaluate a hybrid personalization approach that tailors model training using a subset of the most informative participants. We compared three modeling strategies-idiographic personalization, hybrid personalization, and a general model-across binary classification tasks for positive affect (PA) and negative affect (NA). The hybrid personalization method outperformed both the idiographic personalization (PA: 76.6% vs. 71.5%; NA: 77.7% vs. 74.3%) and the general model (PA: 55.5%; NA: 49.3%), with the largest advantages observed under limited data conditions. Follow-up analyses revealed that data from individuals with higher emotional awareness contributed more to model performance. These findings highlight the utility of hybrid personalization for real-world ECG-based emotion prediction and suggest that emotional awareness may serve as a useful trait for identifying informative training data and enabling more efficient model development.