This invention relates to the field of
emotion recognition technology, specifically to a method and
system for emotion
event recognition based on multimodal physiological parameters. The method determines a baseline time window based on the
changing trend of the
respiratory rate time series; it determines the
heart rate analysis interval and
blood pressure analysis interval based on the
hysteresis response characteristics of the
heart rate and
blood pressure time series within the baseline time window; the baseline time window,
heart rate, and
blood pressure analysis intervals constitute the current representation feature; the
feature matching degree is determined based on the similarity between the current representation feature and a preset set of historical representation features; the current representation feature is weighted and fused for optimization based on the
feature matching degree to determine the optimized representation feature; the optimized representation feature is input into a pre-trained classification model, which outputs a category
label corresponding to the emotional event. This makes the representation feature more closely resemble the real emotion-physiological linkage, so that the
classification result can take into account the differences in individual physiological characteristics.