The invention discloses a home respiratory
rehabilitation multi-
modal intelligent management system based on a
reinforcement learning algorithm, which belongs to the crossing field of
artificial intelligence and
medical health technology, and comprises a multi-
modal physiological
signal acquisition module, a graph attention
feature fusion module, a near-end strategy optimization
rehabilitation decision module and a long-
short term memory health prediction module, the multi-
modal physiological
signal acquisition module acquires
breathing audio, thoracic and abdominal movement,
oxyhemoglobin saturation and
heart rate variability signals in real time, the graph attention
feature fusion module constructs multi-
modal data into a heterogeneous graph structure, and cross-modal correlation features are extracted through a graph
attention network. The near-end strategy optimization
rehabilitation decision-making module generates a personalized
rehabilitation training strategy through an actor reviewer architecture, the long and short-
term memory health prediction module predicts a
respiratory function deterioration risk value and dynamically adjusts reward function parameters, and the four modules form a deep
coupling closed-loop collaborative
system, so that strategy optimization of risk
perception is realized; and the household rehabilitation effect is improved by 45%.