The present application relates to the technical field of
infant care, and in particular to a method and
system for infant
facial expression recognition and emotional state analysis, which synchronously collects infant facial video and wearable physiological parameters to obtain multi-
modal monitoring data; uses physiological data to match expression timing feature fluctuation, quantifies full-face
muscle movement differences, and further estimates emotional
abnormality risk; combines timing feature and
muscle difference data to output emotional state classification, and generates targeted emotional intervention strategies according to the classification results and risk
data matching; constructs an automatic analysis model based on the above classification data and intervention strategies, and deploys it to a cloud platform to perform full-
process analysis. This method overcomes single-
modal interference through multi-source fusion of vision and
physiology, realizes closed-loop management from dynamic expression capture, risk warning to intelligent intervention, and significantly improves the accuracy and real-time monitoring capability of infant emotional recognition.