This invention relates to the fields of intelligent agents and
computer vision technology, specifically to a method and
system for guiding Parkinson's
disease hand
rehabilitation training based on visual features and intelligent agents. It integrates hand movement videos captured by
high frame rate event cameras, physiological sensor data, and prior
medical knowledge to construct a closed-loop
rehabilitation guidance system centered on a medical
intelligent agent. This
system utilizes
deep neural networks to extract spatiotemporal features of key hand points, combines
Euclidean distance error and
joint angle error to construct a two-dimensional comprehensive evaluation index, and dynamically adjusts the evaluation weights according to the patient's UPDRS stage. Through a medical
intelligent agent based on the
Transformer architecture, combined with a multi-objective
reinforcement learning strategy, personalized, multimodal
rehabilitation feedback instructions are generated. This invention achieves precise
quantitative assessment and personalized guidance of
hand movements in Parkinson's
disease patients, effectively distinguishing between Parkinson's tremor and
essential tremor, and significantly improving the accuracy, compliance, and individual adaptability of
rehabilitation training.