This invention relates to the fields of medical
data processing and multimodal emotion computing, and discloses an emotion
state recognition system for vocal micro-expressions in patients with
advanced cancer. The
system constructs a temporal
state vector reflecting the patient's
neural control ability by calculating the neural conduction
lag rate and the physiological vocal
energy index. A state-adaptive gating module generates a confidence
mask to weight the multimodal features. Subsequently, an emotion decoupling and reconstruction module uses manifold orthogonal projection to separate the weighted features into physiological harm
perception components and psychological emotional response components, and combines sparse dictionary reconstruction to remove
noise. Finally, a fusion decision and grading module combines a temporal convolutional network with an adaptive threshold calibration
mechanism based on the
energy index to output pain grading results and decision signals. This invention can effectively distinguish between a patient's subjective masking and physiological exhaustion state, decouple the physical and psychological attributes of pain, and avoid missed detections due to weak reactions.