Disclosed are a human body motion attitude identification and evaluation method and apparatus. The method comprises the following steps: S01: collecting a video image test data set, and performing data processing on data in the video image test data set; S02: inputting test data which has been subjected to data processing into a trained LSTM neural network model so as to identify a human body motion attitude, and outputting an identification result; and S03: comparing the output identification result with standard motion data, and evaluating the degree to which the identified human body motion attitude is standard according to a comparison result. By means of the method and apparatus, not only can long-term time information be captured, a complex spatial-temporal clue of a human body action can also be captured by means of a time-domain attention mechanism and a space-domain attention mechanism, such that the accuracy of human body motion attitude identification is greatly improved.
HUMAN BODY MOTION ATTITUDE IDENTIFICATION AND EVALUATION METHOD AND SYSTEM THEREFOR