A
rehabilitation evaluation system for a patient with
central nervous system dysfunction comprises the following steps of S1, collecting videos, motion data and
pressure data of a user through a data collection layer, S2, uploading the video data to an
edge computing server, performing preliminary posture
estimation, and S3, performing
rehabilitation evaluation. The method comprises the following steps: S1, carrying out edge calculation on the data, S3, transmitting the data subjected to edge calculation to a
cloud server for deep modeling analysis of
gait and balance parameters, S4, carrying out abnormal mode identification and comparison after
parameter analysis, S5, uploading the analyzed data after comparison to a decision
application layer, S6, transmitting each parameter and a corresponding
processing scheme to a doctor end for a doctor to check, and S5, carrying out
data processing. The method has the advantages that through full-link innovation of multi-mode lightweight acquisition, edge cloud hierarchical
processing, double-model intelligent diagnosis,
reinforcement learning dynamic optimization and visual remote decision, central nervous
rehabilitation evaluation extends to a daily life scene from a laboratory, and core breakthrough of precision improvement, efficiency multiplication and personalized enhancement is achieved.