The invention provides a video-based identification model for Parkinson's
disease and idiopathic tremor. The video-based identification model comprises an attitude
estimation module, a
data processing module and a classification module, the attitude
estimation module comprises a pre-trained RTMPose-L model, performs
whole body attitude
estimation on three
upper limb motion videos of a subject frame by frame, extracts key point coordinates of a
wrist and five fingers of a target hand, and forms an absolute coordinate sequence of multiple groups of hand key points; the
data processing module converts the absolute coordinate sequence into a relative coordinate sequence and calculates a statistical
characteristic sequence of the speed, the acceleration, the amplitude, the frequency and the entropy; the classification module comprises a PatchTST
time sequence classification model, the relative coordinate sequence and the statistical feature sequence are divided into a plurality of patches with preset lengths, then
feature extraction is carried out on each patch through a Transform
encoder, patch prediction is output, finally, an output result is flattened into a one-dimensional vector, and the one-dimensional vector is subjected to patch prediction. And the Parkinson's
disease and the idiopathic tremor are classified through the full connection layer.