The invention provides a
gait analysis and
anomaly detection method and
system based on spatio-temporal joint modeling, and a medium. The method comprises the following steps: S1, dividing an original GRF
signal into a time slice sequence; s2, inputting the time slice sequence into a multi-scale depth residual scaling network to extract multi-scale spatial-temporal features; s3, splicing the multi-scale spatial-temporal features into a
time sequence feature sequence according to a
time sequence, inputting the
time sequence feature sequence into a Transform
encoder, shielding invalid time steps at the same time, and outputting
global time sequence features; and S4, carrying out classification
processing on the
global time sequence features, and outputting
gait classification probability distribution. According to the method, time
sequence modeling and spatial
feature extraction are combined, complex space-time characteristics of
gait signals are comprehensively captured, an accurate detection means is provided for Parkinson's
disease, musculoskeletal injury and other gait-related diseases, meanwhile, model robustness, calculation efficiency and medical interpretation are improved, and the method is suitable for popularization and application. And a new
technical support is provided for
clinical diagnosis,
rehabilitation evaluation and scientific research of movement.